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Microchip’s First Libero SoC Design Suite Release Boosts FPGA Designer Productivity and Delivers One Unified Design Suite for Latest Families As each new generation of devices scale, Field Programmable Gate Array (FPGA) designs are increasing in complexity and resource utilization, making designer productivity essential to accelerating time to market. Microchip Technology Inc., via its Microsemi Corporation subsidiary, announced the release of Libero SoC version 12.0, delivering new gains in runtime and quality of results, as well as one unified design suite for all the company’s latest-generation FPGA families, including new production releases of PolarFire FPGAs.

Libero SoC v12.0 reduces design flow runtimes and, with the improved quality of results, it provides results in fewer design iterations and improves customer productivity. By upgrading to Libero SoC v12.0, designers will see runtime reduction of 60 percent for timing, 25 percent for place and route and 18 percent for power results. They will also see an average increase of four percent in quality of results for larger designs and a 10 percent improvement for the PolarFire MPF300/TS-1 device. Libero SoC v12.0 is being released simultaneously with the production release of the PolarFire MPF100T, PolarFire MPF200T and PolarFire MPF300T devices. The release includes production timing and power for PolarFire MPF300T-1 devices, as well as support for two new industry-leading devices for the aerospace and defense market segments—the low-power, radiation-tolerant RT4G150L, which offers 25 percent savings for standard speed grade; and military-grade support for the SmartFusion2 M2S150T/S FCV484 device. One unified design suite for PolarFire, IGLOO2, SmartFusion2 and RTG4 FPGAs eliminates the need for designers to qualify multiple pieces of software when working across product families. Libero SoC v12.0 now supports FPGA Hardware Breakpoint (FHB) for RTG4 and PolarFire devices, PCIe debug support for PolarFire and continuous transceiver eye monitoring using SmartDebug. The new release also improves Double Date Rate (DDR) memory performance by an average of 29 percent in high-effort mode and 39 percent in regular-effort mode. Enhanced Tool Command Language (TCL) support enables a much-requested feature where customers can run the entire design flow on the command line if they so choose. Microchip Technology |


Win a Microchip MPLAB PICkit 4 In-Circuit Debugger

Win a Microchip MPLAB PICkit™ 4 In-Circuit Debugger (PG164140) from Electronica Azi International. The Microchip MPLAB PICkit 4 In-Circuit Debugger allows fast and easy debugging and programming of PIC® and dsPIC® flash microcontrollers, using the powerful graphical user interface of MPLAB X Integrated Development Environment (IDE). The MPLAB PICkit 4 programs faster than its predecessor with a powerful 32-bit 300MHz SAME70 MCU and comes ready to support PIC and dsPIC MCU devices. Along with a wider target voltage, the PICkit 4 supports advanced interfaces such as 4-wire JTAG and Serial Wire Debug with streaming Data Gateway, while being backward compatible for demo boards, headers and target systems using 2-wire JTAG and ICSP. Key features of the PICkit 4 include matching silicon clocking speed, supplying up to 50Ma of power to the target, a minimal current consumption at <100μA from target, and an option to be self-powered from the target. The MPLAB PICkit 4 is connected to the design engineer's computer using a high-speed 2.0 USB interface and can be connected to the target via an 8-pin Single InLine (SIL) connector. The connector uses two device I/O pins and the reset line to implement in-circuit debugging and In-Circuit Serial Programming™ (ICSP™). Currently, the MPLAB PICkit 4 In-Circuit Debugger/ Programmer supports many but not all PIC MCUs and dsPIC DSCs, but is being continually upgraded to add support for new devices.

For your chance to win a Microchip MPLAB PICkit 4 In-Circuit Debugger, visit: and enter your details in the online entry form. 3

Electronica Azi International » TABLE OF CONTENTS 3 | CONTEST: Win a Microchip MPLAB PICkit 4

18 | Renesas empowers predictive maintenance and

In-Circuit Debugger

anomaly detection at the endpoint through its embedded artificial intelligence technology

6 | Defining the Battlefields of the Future 8 | The power of autonomous peripherals: Achieving low-power in real time

12 | Cover story


21 | Machine learning and sensor fusion on embedded MCU 24 | The Raspberry Pi Pmod HAT Adapter from DesignSpark 28 | Leuze: Distance sensors ODS 110 / HT 110 29 | Sensor Instruments: Accurate High-Speed Counting of Stacked Plastic Caps

Edge intelligence at the edge of the world The enablement of compute at the edge isn’t just one more step towards a world of a trillion devices; if we want to realize the global ambitions of the IoT, then we need the entire spectrum of compute capable, fired up and ready, from the smallest device to the highest performance data center.

30 | Contrinex: New generation of IO-Link-enabled smart sensors 31 | Sensor Instruments: Inline Color Measurement of Paint Through a 15mm Thick Inspection Glass 32 | Rent Your SMT Line - Staying Ahead of the Game

14 | Improving Embedded Security with the Armv8-M Architecture and TrustZone 14


34 | HARTING showcases new standards and solutions for intelligent infrastructure in the field

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Electronica Azi International is published 6 times per year in 2019 by Euro Standard Press 2000 s.r.l. It is a free to qualified electronics engineers and managers involved in engineering decisions. Copyright 2019 by Euro Standard Press 2000 s.r.l. All rights reserved.

Electronica Azi International | 1/2019

Defining the Battlefields of the Future By Mark Patrick Mouser Electronics In recent years military, intelligence, and government agencies have become aware of a new and fast evolving threat, driven by technological change, and taking forms they are often ill-equipped to identify and counter. These new technologies are set to change the face of modern warfare. Perhaps most disturbing for conventional military thinkers has been the emergence of coordinated attacks unlike anything previously encountered. For example, in early January 2018, a swarm of unmanned drones launched an assault against a Russian military base in Syria. The Russian authorities stated that the drones were shot down and that no damage or casualties resulted, though these claims are difficult to verify. In any case, it was the first reported example of a totally different type of low-cost attack on high-value facilities – a threat that may soon become commonplace. Away from the traditional theatre of conflict, critical national infrastructure has also been attacked using novel methods made possible by advanced technology. In 2017, hackers (allegedly from North Korea) gained access to the power grid controls of one of the US’s utility companies. Back in 2015 and 2016, the Ukraine government claimed that Russia-linked hackers had repeatedly shut down parts of its power distribution network, thereby causing chaos. Some years 6

earlier, targeted malware - dubbed Stuxnet - was alleged to have severely damaged vital equipment in Iran’s atomic research facilities. More recently, there have been claims of foreign interference in US and European elections via tailored information leaks and targeted online ads, as well as various malware tools. In almost every case, these leaks could be blamed, at least in part, on the poor security practices of government agencies, employees and consultants. Underlying these alarming stories is a key trend – the blurring of the dividing line between consumer products and specialised military, aerospace and intelligence hardware/software. This change is being driven by the increasing efficiency of mass production. It is getting tougher to justify a multi-million Euro development budget for a new military device or component, when a few thousand Euros worth of consumer hardware and software could adequately perform many of the required tasks. LETTING DOWN THE GUARD Before 2000, a government minister would typically have used a dedicated secure communications network based on proprietary protocols and hardware, effectively creating a safe air-gap between themselves and public systems. Recently though many officials have insisted on ditching clunky

old secure handsets and utilising modern user-friendly mobile phones or tablets. As this consumer-level technology increasingly moves into military and intelligence applications, there are scenarios that illustrate the profound shift and potential impact it may bring about. A modern cutting-edge military aircraft, such as the F-35 or MiG-35, can cost almost Euro 80 million. But could that aircraft always survive being swarmed by dozens of Euro 100 drones armed with simple shrapnel grenades? The answer to that question is uncertain, but amongst civil aviation authorities there is already growing concern about the risk that even a single accidental drone impact could pose to a commercial aircraft. Demonstrating the damage that a small, cheap weapon can do if it can get close enough – in 2000 the USS Cole, a state-ofthe-art $1.8 billion destroyer, was caught off guard, holed and disabled by a single small explosive-laden fibreglass boat piloted by two suicide bombers. Seventeen crew lost their lives as a consequence. Armed forces, intelligence services and governments are increasingly heavy users of off-the-shelf consumer electronics, for networking, storage and so on, because a custom device would cost tens of millions to develop, and take so long that it would be out-dated by the time it was delivered. In Electronica Azi International | 1/2019


fact, this has conversely led to the curious trend of ‘military spec’ standards, originally developed for the armed forces, migrating into the consumer and commercial markets, because they are seen as a strong guarantee of reliability and ruggedness. Unfortunately, the proliferation of consumer electronics in military applications is opening up potential vulnerabilities. The use of consumer-grade hardware and services to handle secure data – sometimes even in breach of official rules or guidelines – has been blamed for several damaging information leaks, among them US officials’ emails and US intelligence services’ hacking tools. ASYMMETRIC THREATS In general, it can be argued that the spread of cheap, powerful, mass-produced technology has, in some areas, tended to tilt the balance of power away from large organisations, such as armies and government agencies. The very nature of this imbalance lends itself to one of a modern military’s greatest fears: asymmetric warfare, or low cost attacks that generate enormously costly damage. These concerns are driving increasing

investment on cyber-defence, as well as spending on research into new military technologies. NATO saw a 60% in cyber security incidents in 2016, and officials called for a corresponding increase in defensive spending. Recent US budget proposals ask for a $1.5 billion cyber-security budget for the Department of Homeland Security alone, and total US federal government’s annual cyber-security spend is estimated to have soared from $7.5 billion a decade ago to over $28 billion today. TECHNOLOGY TO THE RESCUE? As recent news reports have shown, governments are waking up to the grave intelligence threat posed by officials and staff who use insecure devices to handle secure information. Snatching the iPhones and easy-to-use laptops back out of the hands of staff is a tough ask, but personal security practices can still be tightened up, and platform security can be enhanced with encryption, biometric identification (such as fingerprint ID), as well as failsafe mechanisms that wipe or lock devices if they are stolen. Somewhat ironically, the consumer product sector is

forging ahead with all of these security measures, so consumer-grade hardware and software may turn out to be the answer to the very problems that they have fostered. In addition, while the shifting technological landscape certainly gives military planners headaches, it is also constantly presenting them with attractive new opportunities. For example drones and other unmanned aerial vehicles (UAVs), unmanned ground vehicles (UAGs) and also general-purpose robots are already increasingly used in military operations for surveillance, bomb detection/disposal, supply and air strikes. By keeping military personnel out of harm’s way, these tools create the potential for new strategies and may make a high-risk attack feasible. They also provide clearer information, again without risking personnel, and they allow more precisely targeted attacks to be carried out – thereby greatly reducing the risk of innocent lives being lost and minimising other forms of collateral damage. Mouser Electronics Authorised Distributor


The power of autonomous peripherals:

ACHIEVING LOW-POWER IN REAL TIME By: Gregor Sunderdiek, Business Development Manager MCU8 EMEA at Microchip Technology Inc.

Core Independent Peripherals (CIPs) are autonomous, interconnected and intelligent peripherals. With CIPs the microcontroller doesn’t need to interact with the Central Processing Unit (CPU) to executing tasks. This equals several advantages for the application. First, the CPU is not needed for communication between peripheral and peripheral. The core can sleep and the software flow doesn’t need to be interrupted. Obviously, if the core is in sleep mode and software is not needed then the current consumption of the application will be lower. The CPU is the part of the microcontroller which has the highest current consumption. Therefore, using CIPs reduces power consumption. Second, CIPs do not cause interrupts which allows for much faster communication overall. If the CPU core is running software and it has to be interrupted from the peripheral to accomplish a specific action, it requires a lot of time. The interrupt needs three clock cycles + two clock cycles for the relative jump and might use several cycles for the context switch, in order to save the data in the registers on the stack depending on the application. CIPs allow communication to be much faster than if the core has to be interrupted. Third, using CIPs means faster time-to-market. Less software has to be written as the hardware can do the task on its own. This reduces the risk of software errors and less software validation is needed. Therefore, the development time of the product is 8

shorter than without using CIPs. In AVR® microcontrollers (MCUs), all Core Independent Peripherals are connected via the Event System. In the Event System, a multiplexer connects the event generator and the event user. There are synchronous and asynchronous events. Asynchronous events need less than one clock cycle and synchronous events need two clock cycles. Many peripherals can be connected to the event system to be CIPs. These are timers, the Real Time Counter (RTC), Periodic Interrupt Timer (PIT), the Custom Configurable Logic

(CCL), Analog Comparator (AC), Analog-toDigital Converter (ADC), Universal Synchronous/ Asynchronous Receiver/ Transmitter (USART) and the General Purpose Input/Outputs (GPIOS). USING THE CORE INDEPENDENT PERIPHERALS Core Independent Peripherals need to be configured once before they are used. The CPU executes the instruction to do the right initialization of the Event System and the needed peripherals.

Figure 1: Filter of the CCL Electronica Azi International | 1/2019


DELAY/DEBOUNCING Several applications nowadays still use a button as an input. For every button a debounce logic or piece of software is needed to get a non-toggling signal. For the AVR MCU, it is an easy task to do the debouncing as software. This can be done by delays and/or logic in the software program. The software is not complicated but it uses CPU resources. Whether the button has been pressed or not can be checked either by polling or via an interrupt from the GPIO controller. Both need time and CPU usage to do the complete debouncing task. Debouncing / Delay with the filter of the CCL With Core Independent Peripherals, completing the debouncing task can be done without any additional overhead of the CPU. All that is needed is the Custom Configurable Logic (CCL). The GPIO, where the button is connected is configured as an asynchronous event generator. The CCL will be the event user. The signal from the GPIO pin to the CCL input will be transferred with no delay. The truth table in the CCL is configured so that the output is equal to the input. The output of the truth table is routed to the filter. For the filter of the CCL, see Figure 1. It is possible to remove glitches from the input signal and we can set the delay of the filter to two to five clock cycles (peripheral clock or an alternative clock) for the output signal. If we use a slow clock of 32 KHz than we have a delay of 1.5 ms. It is also possible to extend the delay time with a different clock or with a timer. Delay with a Timer For example, Timer/Counter B (TCB) is set up in “Single-Shot” mode. If the timer gets an event signal it starts counting until it reaches its programmed max value and stops it. The output of the TCB is connected to the CCL. In the CCL the desired combination of the delay signal can be done.

This allows for a very flexible time delay. Every new event to the TCB timer starts the counting again. DEAD TIME GENERATION Dead time is used in applications where switches (transistor, FET or IGBT) are in series between power and ground (GND). If both are activated at the same time there is a short. An example is an H-bridge configuration commonly used for motor control drive.

The timer TCA generates the base PWM signal for the motor control. The AC is externally connected to the hall sensor of the motor and internally connected via the event system to timer TCB. Timer TCB generates the dead time signal if it gets a signal from the AC. The CCL combines the TCA (PWM), TCB (dead-time) and the AC signal. The input signals can be selected directly in the CCL configuration and do not need to use the Event System.

Figure 2: CCL LUT 0 Dead Time Generation

Figure 3: CCL LUT 1 Dead Time Generation Depending on the application the dead-time will either be between commutations or between the Pulse-Width Modulation (PWM) pulses. Dead time between PWM pulses is needed, for example, in sinusoidal drive and between commutations in 1-pole Brushless DC (BLDC) motors. Dead-time between PWM pulses can be generated with the timer Time Code Display (TCD). To generate dead-time between commutations we need two timers, the Custom Configurable Logic (CCL) and the AC. Figure 2 and Figure 3 represent the logic of the truth table of the CCL.

There are hardwired connections between these modules. The CCL then generates two PWMs, see Figure 4 which drives the switches for the motor. The motor runs without any CPU involvement. For more technical information see the AVR Application Note AVR42778. AUTOMATIC SHUT-OFF PWM SIGNAL Many applications need to monitor the current consumption so that it does not exceed a maximum level. This can easily be done with the analog comparator AC. The AC measures the voltage (current in the resistor) via a shunt register.

Figure 4: Timing Diagram of Dead Time Generation



If it exceeds a previous configured threshold then the PWM signal should stop immediately. Both examples below are using Core Independent Peripherals. The PWM output signal can be stopped when an over current is detected without interaction of the CPU.

via the Event System to the Timer/Counter D0 (TCD). The TCD features include fault handling. If the threshold of the AC is exceeded (over current detected) then an event is signaled to the TCD and the PWMs are stopped automatically.

Example of LED Lightning with TCA and CCL The Timer/Counter A0 generates the PWM for the Light Emitting Diode (LED). The AC is used to detect the overcurrent and the CCL is used to combine these signals, so that if there is an overcurrent detected then the PWM is automatically stopped. The AC and the TCA0 are connected via the Event System to the CCL. The AC output signal and the PWM are configured in the TRUTH table of the CCL, see Figure 5. The PWM signal is fed through if the event signal form so the AC is zero. If the over current is detected, the AC event signal is one and the output is zero as long as there is an overcurrent.

MEASURING TIME-OF-FLIGHT Time of flight measurement is used to measure the distance a signal travels. The measurements starts when the signal leaves the transceiver and stops when it’s detected by the receiver. With the time and the known m/s of the signal the distance can be calculated. In the below example we are measuring the distance with ultrasonic signal. For that we need the Core Independent Peripherals TCA0, TCB0, TCS0, AC and 2x CCL and can compute the time of flight without CPU usage. Figure 6 shows the look up Table 1 (LUT1) generates the Transmitted Signal. TCA Out generates the PWM signal and TCD Out B is the transmit mask. The inverted transmit mask and the PWM are logic AND combined and then generate the transmit signal, which is shown by the LUT1 truth table.

Figure 5: CCL Truth-Table for Fault Handling Example of Motor Control with the TCD A BLDC motor is controlled by the TCD timer that generates the two channels + two complementary channels PWM signals to drive the four Metal-Oxide Semiconductor Field-Effect Transistor (MOSFETs) in an Hbridge. The AC is used to detect the overcurrent in the motor with a shunt between the motor and GND. The AC is connected

LUT 0 generates the reflected signal. AC Out gives the activity on the receive line and TCD Out A is the receive mask. The inverted receive mask and the “receive line” are logic AND combined generate the Reflected Signal, which is shown by the LUT0 truth table. The SR latch is reset with the first transmitted signal and starts the counter in TCD. With the signal from reflected signal and when the SR latch is set, the TCD counter is

stopped. The time of flight is now stored in the counter value of TCD without any use of the CPU. The CPU is only needed for the distance calculation where the time of flight is multiplied with the speed of the signal. For more technical information for ultrasonic distance measurement see the AVR Application Note AVR42779. CONCLUSION The new ATtiny1617/1616/1614/817/816/ 814/417 microcontroller series from Microchip adds innovative Core Independent Peripherals (CIPs) to the tinyAVR® family of microcontrollers. With these CIPs, an application can react in real-time, with less software overhead and lower current consumption than without CIPs. These examples show that CIPs are easy to set it up and that the real-time performance is faster and needs less power consumption than softwarebased solutions. Even with much higher performance microcontrollers this level of realtime performance cannot always be reached and, if possible, the power consumption would be several times higher. REFERENCES • Further information of the new tinyAVR family: • AVR ATtiny817 webpage • AVR42778 Application Note: Core Independent Brushless DC Fan Control Using Configurable Custom Logic on ATtiny817 • AVR42779 Application Note: Core Independent Ultrasonic Distance Measurement with ATtiny817 Microchip Technology

Figure 6: Ultrasonic Time of Flight Measuring 10

Electronica Azi International | 1/2019


Kontron Introduces New TRACe-RM404 Railway 19-Inch Platform for Train Control Kontron announced the Kontron TRACe-RM404-TR, a fanless 19-Inch 1,5U railway computer. EN50155-certified, it is specifically designed for train control and communication applications. Designed as a robust and compact 19Inch 1.5U box computer, the Kontron TRACe-RM404-TR provides a perfect balance between processing performance, I/Os, power consumption, and reliability in demanding railway environments. Thanks to its 19-inch mechanical design (compliant to EN60297-3- 100), the

Kontron TRACe-RM404-TR can easily fit any existing railway equipment. The box computer has already been chosen for a train retrofit by a large rail system solutions provider in Asia, supporting the train control in an automated metro. Kontron TRACe(TM) platforms are designed to make customization faster, system integration easier, and to reduce time to market while shrinking maintenance and support costs over the entire life cycle of the program. Kontron's TRACe-RM404-TR 10 year's product lifetime combined with the long term support services ensure long service life, up to 25 years and more. The first TRACe-RM404-TR variant features the Intel Atom® x5-E3940 quadcore @ 1.6 GHz high performance per watt processor, with 2GB DDR3L memory up to 1866 MHz (optional up to 8GB DDR3L) and 64 GB Industrial MLC SSD memory. The Kontron TRACe-RM404-TR offers multiple communication ports, making it ideal for train control applications. The TRACeRM404-TR features three independent Gigabit Ethernet network ports (on M12 connectors), 2x RS232/422/485 isolated ports with galvanic isolation, 8 isolated digital inputs and 8 isolated digital outputs for operation plus 2x USB, 1x GbE RJ45, 1x RS232, and display port interfaces for maintenance purposes. It comes with an EN50155 class S2-C1 ultra wide range power supply (from 24 VDC to 110 VDC nominal input voltage range) adapted to all types of railway vehicles from light rail vehicles to high-speed trains. Its rugged EN50155 fanless and low-power design ensures high performance and high reliability, operating within a temperature range of -25 to +70°C. Thanks to its modularity, the TRACe-RM404-TR product can accommodate several optional wireless (4G LTE, Wi-Fi) interfaces, field busses (CAN2.0, MVB) and/or additional optional I/Os (Audio, USB) to match any other railway applications such as onboard CCTV, entertainment/infotainment PIS or train-to-ground communications. Kontron |

Maxim’s Highly Integrated, Single-Chip Security Solutions Offer Simple Implementation While Safeguarding Sensitive IoT Data Designers of internet of things (IoT) devices now have a smarter and more secure way to protect stored, sensitive information with the highly integrated MAX36010 and MAX36011 single-chip security supervisors from Maxim Integrated Products, Inc. These security solutions make it easier for designers to implement robust tamper detection, cryptography and secure storage while safeguarding sensitive information via logical and physical protections, without having to be security experts themselves.

The MAX36010 and MAX36011 both offer strong security that can be easily integrated into a design at any stage of its development. Additionally, if these parts are integrated later in the design cycle, there is no need to change the platform to accommodate them, thereby simplifying the implementation process. Compared to competitive solutions, the devices, due to their high level of integration, facilitate a 60 percent faster design cycle, while also lowering bill of materials (BOM) costs by 20 percent. To ensure a higher level of security, these supervisors generate keys via a true random number generator (TRNG). The keys are then stored in battery-backed RAM along with certificates and other sensitive data. This data is erased when tampering is detected, a capability that meets the requirements of Federal Information Processing Standard (FIPS) Publication 140-2 at its highest security levels (Levels 3 and 4). The MAX36010 and MAX36011 both support symmetric and asymmetric cryptographic functions such as Data Encryption Standard (3DES), Advanced Encryption Standartd (AES), Rivest–Shamir–Adleman (RSA) and Elliptic Curve Digital Signature Algorithm (ECDSA). These secure cryptographic engines are designed and compliant to the requirements of Payment Card Industry (PCI) and FIPS140-2 certifications. The MAX36010 supports symmetric key generation for AES and 3DES, whereas the MAX36011 supports both symmetric and asymmetric key generations for AES, 3DES, RSA and ECDSA Maxim Integrated | 11

Edge intelligence at the edge of the world By: Chris Shore, Director of Embedded Solutions at Arm


Electronica Azi International | 1/2019


Early proponents of the Internet of Things (IoT) took the centralized network approach that had worked so well within the comfortable confines of corporate IT and imagined it applied, unhindered, to hundreds, perhaps thousands of square miles of real-world geography.

If distance increases network latency, the obvious answer is to decrease the realworld distance between where sensor data is collected and where it is processed. This means enabling compute in the most physically appropriate location – whether that is on the device, in mid-network equipment

Thinking ‘local’ (green arrows) brings a range of benefits compared to cloud processing Yet in reality, the latency and vulnerability that topography and distance introduce stands to limit so many of IoT’s most interesting use cases. Applications first to fall are those in industries operating within challenging conditions such as oil and gas, remote healthcare and disaster relief. Next is any device for which an instantaneous response is vital – such as medical equipment or an autonomous vehicle. Even smart cities, which may seem far more fertile grounds for IoT proliferation, will struggle as the burden of big data defeats available bandwidth and the cloud’s ability to process at such scale. OVERCOMING THE BARRIERS OF DATA AND DISTANCE FOR IoT TO SCALE In 2016 the IoT generated 1.6 trillion gigabytes of data, and Cisco estimates this will rise to 500 trillion gigabytes by the end of 2019, growing exponentially in years following. As the data grows, the friction it encounters along its journey will increase.

such as a gateway or in the traditional cloud. Data will still travel from the edge to the cloud, but in the form of compact, computed findings rather than raw information. Giving devices the power and resource to perform their primary function independ-

ently benefits a far broader spread of industries in tech-hostile environments, such as

mines, oil rigs, underwater applications and other adverse environments that may lack the safety net of always-on internet connectivity – or even always-on electricity. ENSURING SECURITY IS BUILT IN LOWPOWER DEVICES FROM THE GROUND-UP Of course, there are other factors to consider: the level of compute, privacy and security required and how much latency can be tolerated. Increase the compute capability and complexity and cost and power consumption may skyrocket. Then there are the physical extremes: a wave-powered gas sensor placed on the bed of the North Sea needs to be able to perform, and to keep performing, its primary function – even if network or power connectivity is lost for long periods of time. In terms of security, this requirement must run throughout the entire design process. We need a standardized, holistic, scalable security framework for low-power devices, such as the Arm Platform Security Architecture (PSA). It provides a common foundation, based on industry best-practice, that allows security to be consistently designed in at both a hardware and firmware level.

Arm TrustZone security technology takes it one step further to simplify IoT security and offer the ideal platform on which to build a device that adheres to PSA principles. All of this is possible with the Arm ecosystem of silicon partners, software, tools, OSes and resources, helping developers speed time to market. The enablement of compute at the edge isn’t just one more step towards a world of a trillion devices; if we want to realize the global ambitions of the IoT, then we need the entire spectrum of compute capable, fired up and ready, from the smallest device to the highest performance data center. Learn more about IoT solutions from Arm



Improving Embedded Security with the Armv8-M Architecture and TrustZone By: Jacob Beningo, Contributed By Digi-Key's North American Editors Securing a microcontroller-based application for the IoT can be tricky. Security starts at the hardware level and then scales into the embedded software. To successfully secure the software, developers require that the underlying hardware support critical features such as: • • • • • •

Secure boot Memory protection Cryptographic engine accelerators True random number generator (TRG) Secure pin multiplexing Software isolation

While some of these features are supported in the Arm® Cortex®-M processors such as the M0+, M3/4/7 series, it can be difficult and time consuming to create a successful solution. A new solution that developers can leverage at the hardware level is to use the new Cortex-M23/33 series of microcontrollers which are based on the Armv8-M architecture. These processors are designed with security in mind and contain many security features like those listed earlier, including Arm TrustZone® for microcontrollers. In this article we will become more familiar

with the Armv8-M architecture and explore how we can improve embedded security using TrustZone. INTRODUCTION TO THE ARMV8-M ARCHITECTURE The first thing to realize about the Armv8M architecture is that it is the latest microcontroller architecture from Arm that targets low cost, deeply embedded real-time embedded systems. There are three new processor types that are joining the family. The M23, which is a low-power variant, the M33, which is a high-performance variant, and the recently announced M35P which is a high-performance, physical security (think tamper-resistance) processor (figure 1). While the Armv8-M architecture does improve performance from previous architecture generations, several critical improvements to note include: • Instruction set enhancements • Flexible breakpoint configuration • Dynamic reprioritization of interrupts • Enhanced trace support • Simpler Memory Protection Unit (MPU) setup

Figure 1: From a performance standpoint, the new Cortex-M23/33 processors fit into the family as improved Cortex-M0+ and Cortex-M4 processors. (Image source: Arm) 14

The biggest and most interesting improvement to the architecture is the ability to use Arm TrustZone. TrustZone is a security extension to the architecture that allows a developer to physically isolate executing code and memory regions such as RAM, code space, and peripherals in hardware. Electronica Azi International | 1/2019


TrustZone allows the software to be broken up into secure and unsecure regions which then execute in either a secure or nonsecure processor state. The secure state allows full access to the processor’s memory and peripherals, while the non-secure state can only access non-secure regions and secure functions that are purposely exposed to the non-secure code (figure 2).


Industry’s first End-to-End LoRa® security solution provides secure key provisioning with Microchip and The Things Industries As the LoRa® (Long Range) technology ecosystem accelerates, security remains an area for improvement in the market due to vulnerabilities that leave the network and application server keys accessible in the memory of modules and microcontrollers (MCUs) paired with a LoRaWAN™ stack. If keys are accessed in a LoRaWAN device, a hacker can impersonate it and authorise fraudulent transactions, which can result in a scalable attack with substantial losses in service revenue, recovery costs and brand equity. Microchip Technology Inc. in partnership with The Things Industries, announced the industry’s first end-toend security solution that adds secure, trusted and managed authentication to LoRaWAN devices at a global scale.

Figure 2: TrustZone uses hardware isolation to separate the processor and application into non-secure and secure states. Code executing in the non-secure state cannot access or manipulate secure memory or code. Secure memory and code can only be accessed while running in a secure state. (Image source: Arm) Developers can choose which flash and RAM locations belong to the secure state and which belong to the non-secure state. When non-secure code calls a secure function, the switch between nonsecure and secure states is handled completely in hardware in a deterministic manner that has a worst-case switch time overhead of three clock cycles. There are several registers within the CPU that are shared between the secure and non-secure states, but each state also has their own stack pointer, fault, and control registers. The M33 even has a stack limit register that can be used to detect a stack overflow. It’s important to note that TrustZone is a processor extension, which means that it is up to the processor manufacturer as to whether they will include TrustZone support or not on the part. Since TrustZone is optional, let’s examine a few Armv8-M processors that are currently available and how they handle TrustZone. SELECTING AN ARMV8-M PROCESSOR WITH TRUSTZONE SUPPORT There are currently several processors that are available that support the Armv8-M processor. What’s interesting is that these parts are so new, that as of late summer 2018, the only manufacturer that has parts in production is Microchip Technology. There have been announcements from other processor manufacturers such as Nuvoton that parts are coming. We can expect over the next 12 months to see a dramatic increase in the number of Armv8-M parts, including those that support TrustZone.

The solution brings hardware-based security to the LoRa ecosystem, combining the MCU- and radio-agnostic ATECC608A-MAHTN-T CryptoAuthentication device with The Things Industries’ managed join servers and Microchip’s secure provisioning service. The joint solution significantly simplifies provisioning LoRaWAN devices and addresses the inherent logistical challenges that come with managing LoRaWAN authentication keys from inception and throughout the life of a device. The Common Criteria Joint Interpretation Library (JIL) “high”-rated ATECC608A comes pre-configured with secure key storage, keeping a device’s LoRaWAN secret keys isolated from the system so that sensitive keys are never exposed throughout the supply chain nor when the device is deployed. Microchip’s secure manufacturing facilities safely provision keys, eliminating the risk of exposure during manufacturing. Combined with The Things Industries’ agnostic secure join server service to the LoRaWAN network and application server providers, the solution decreases the risk of device identity corruption by establishing a trusted authentication when a device connects to a network. For more information, visit: ATECC608AMAHTN-T CryptoAuthentication device Microchip Technology | 15


Microchip has produced two main versions of the Armv8-M architecture in their SAML10 and SAML11 family of parts. The SAML10 version does NOT include TrustZone, while the SAML11 parts do. Figure 3 shows all the variants for the SAML10 and SAML11 parts that are currently in production and available. The main differences between the variants is the availability of RAM, flash, pins and peripherals, which is what we expect when selecting a microcontroller.

flash, 2 Kbytes of data flash memory, 4 Kbytes SRAM, and comes in a 32-pin package. The Microchip SAML11 Xplained Evaluation Board includes the SAM L11E16A microcontroller which includes 64 Kbytes of flash, 2 Kbytes of data flash memory, 16 Kbytes SRAM and also comes in a 32-pin package. The development boards are identical minus the fact that the processors are different. The Xplained board can be seen in figure 4.

First, developers need to separate out their applications spaces to determine what code and libraries belong in the secure state and which belong in the nonsecure state. Once this is determined, a developer creates two different software applications; one for the secure code and one for the non-secure code.

Figure 3: The Microchip SAML10 and SAML11 microcontroller variants. Only the SAML11 parts include Arm TrustZone. (Image source: Microchip Technology) For developers that are looking to get started with Armv8-M, there are two development kits to choose from. The Microchip SAML10 Xplained evaluation board includes the SAM L10E14A microcontroller which includes 16 Kbytes of

HOW TRUSTZONE APPLICATIONS WORK Developers working with TrustZone will discover that the way in which an embedded application is developed is going to dramatically change.

Figure 5: When using TrustZone, developers end up with a multi-project workspace where one project is specifically for the secure code and the other is for the user code. (Image source: Keil)

Tips and tricks for securing an embedded application with TrustZone

Figure 4: The Microchip SAML10/L11 development board is based on the Armv8 architecture. The SAML11 version supports TrustZone. (Image source: Keil) 16

There are many techniques that can help improve embedded security. Below are several tips and tricks that will help developers interested in using the Armv8-M architecture with TrustZone: • Use the secure zone during reset to establish a root of trust and a trusted execution environment. • Put security critical tasks, libraries, and keys into the secure zone. • Let the user code be placed in the non-secure zone. • To keep things simple, put the RTOS kernel in one place, either the secure or the non-secure zone. • Use the MPU in the secure and non-secure zones to improve process isolation. • Minimizing the code in the secure zone can help to minimize the secure codes attack surface. • Make sure that secure code clears any secret information from unbanked registers before initiating a transition from the secure to non-secure state. Electronica Azi International | 1/2019


This can be done very easily using a compiler/ IDE like Keil MDK. What a developer essentially ends up with is a multi-project workspace where one project is the secure code and the other is the non-secure code (figure 5). When a TrustZone application starts, the code begins executing in the secure state.

This allows a developer to immediately establish a root of trust from which the rest of the application can execute. Once the system boots, the application will switch from the secure state to the non-secure and execute what is known as the user code. At this point, the application executes just like any other embedded application. The main

Figure 6: A TrustZone application starts execution from the secure state and enters the non-secure state once the root of trust has been established. The non-secure state can only make function calls to exposed functions within the secure code, otherwise an exception is thrown. (Image source: Keil)

difference is that the non-secure code can only access secure functions and callbacks through a secure gateway (figure 6). If the user application attempts to access secure code, memory or peripherals without going through the secure gateway, an exception will be generated. Undoubtedly this means that either there is a bug in the software, or in a production environment, a hacker is attempting to access the system. At this point, the code can then decide how it should thwart the attack, such as restarting the system to remove any injected code that may be running in the non-secure SRAM. CONCLUSION Securing a microcontroller-based application for the IoT is important but tricky. Security starts at the hardware level, but many traditional microcontroller families running Cortex-M0+, Cortex-M3/4/7 cores may lack the features necessary to successfully secure the device. Developers can now leverage the new Armv8-M architecture on the Cortex-M23 and Cortex-M33 cores to secure their embedded applications using a rising number of processors using the architecture.


Arduino unveils the Arduino IoT Cloud Arduino, the world’s leading open-source hardware and software platform, today announced the introduction of an IoT Cloud as part of its professional IoT strategy. Targeted at developers, system integrators and maker hobbyists, the Arduino IoT Cloud is an easy-to-use Internet of Things application platform that enables users to develop and manage IoT applications that solve reallife problems in a business environment or in everyday life. The introduction of this new platform builds on Arduino’s mission of making complex technology simple enough for anyone to use.

Convenience and flexibility were key considerations for the Arduino IoT Cloud. A major benefit is its ability to program Arduino boards, whereas previously users were required to program them via Arduino Sketch. The Arduino IoT Cloud will quickly and automatically generate a sketch when setting up a new thing, thus enabling a developer to achieve a working device within five minutes of unboxing a board. The Arduino IoT Cloud also allows other methods of interaction, including HTTP REST API, MQTT, command-line tools, JavaScript, and WebSockets.

Designed for seamless IoT development the MKR form factor delivers embedded connectivity and very low power consumption in a compact size.

These features make the boards the most suitable solution for emerging battery-powered IoT edge applications, such as environmental monitoring, tracking, agriculture, energy monitoring, and industrial automation. Arduino |



Renesas empowers predictive maintenance and anomaly detection at the endpoint through its embedded artificial intelligence technology By: Knut Dettmer, Renesas Electronics EMBEDDED ARTIFICIAL INTELLIGENCE (E-AI) TECHNOLOGY The evolution of artificial intelligence (AI) technologies such as machine learning and deep learning has been remarkable in recent years. The range of applications is rapidly expanding from cloud centric applications mainly focused on the IT field to the embedded systems market. AI enables embedded devices to dynamically react and adapt to changes in the operating environment, and to adapt to the constantly changing state and condition of a device or machine. WHY MOVE TO THE ENDPOINT? The trend to move AI processing from centralized cloud processing platforms to the endpoint is motivated by a variety of reasons. First, bandwidth constraints limit the capability to deliver the required data from the observation point to a cloud processing unit to process the related analytics.

For many devices and machines, there may even be no cloud connection available. This may be motivated by infrastructure restriction or by data privacy concerns. Still, one might well like to enjoy the many benefits from using AI technology to improve product performance and the overall equipment efficiency. Secondly, even if cloud connectivity with sufficient bandwidth is available, many AI applications are required to infer data in realtime, within milli- or microseconds. With todayâ&#x20AC;&#x2122;s connectivity technology, this is not achievable. A cloud connection is unreliable and non-deterministic in terms of latency and introduces more than several dozens of milliseconds delay. Thirdly, data privacy is another motivation to process the analytics at the embedded endpoint and not in the cloud. Many industry segments regard the processed data itself as proprietary and are sensitive to share this data outside their own network.

Figure 1: Motivation for embedded AI in the endpoint 18

For example, healthcare devices collect personal data about an individualâ&#x20AC;&#x2122;s health that must be highly restricted in terms of data distribution. In industrial automation, the analyzed data reveals how processes are controlled in a factory and is considered core know-how of the production company. Finally, data protection laws place a lot of restrictions on how end user data can be stored and processed. Other advantages of working at the endpoint include the possibility to create hierarchical networked AI systems that are robust and scalable while being optimized on an individual use case in terms of performance and power consumption. When looking at these issues, the need for efficient AI inference at the embedded endpoint becomes obvious. It demands efficient endpoints that can infer, pre-process and filter data in real-time. All to optimize device performance and analyze the respective application specific data points directly at the endpoint, while avoiding all the aforementioned constraints. This is the core of the Renesas e-AI activity. WHAT IS SPECIAL ABOUT EMBEDDED AI? USE CASES A common goal for AI is to improve the overall equipment efficiency, which targets maximizing the device availability, performance and output. Using e-AI methods, one can implement predictive maintenance measures that continuously analyze the state and condition of a device to indicate necessary maintenance Electronica Azi International | 1/2019


before the performance of the device degrades, avoiding unplanned downtime at the same time. Through these measurement analytics, the time for maintenance can be optimized individually for a specific device or machine. That results in a dynamic individual maintenance plan which is much more cost efficient than to operate on a static maintenance plan. FOCUS ON INFERENCE It is important to understand that embedded

AI processing typically means inference processing. In AI terminology, inference is the process in which captured data is analyzed by a pretrained AI model. In embedded applica-

with our customers and therefore enable our customers to enjoy all of its benefits. More concretely, Renesas has implemented anomaly detection and predictive maintenance algorithms based on neural network architectures to optimize performance of plasma edging machines in its Naka factory. The results were so convincing that further proof of concepts were implemented with a variety of customers and partners. For example, GE Healthcare Japan's Hino Factory is utilizing an AI unit which is based on the Renesas e-AI technology for improving their productivity. Our partner Advantech provides this AI unit as easy retrofit option to implement e-AI technologies for existing machines or devices.


e-AI Inference Figure 2: Learning vs Inference tions, applying a pre-trained model to a specific task is the typical generic use case. In contrast, creating a model is called training (or learning) and requires a completely different scale of processing performance. Therefore, training is typically done by high performance processing entities, often provided by cloud services. Depending on model complexity, training a model can take minutes, hours, weeks, or even months. e-AI processing does not normally attempt to tackle these kinds of model creation tasks. Instead, e-AI will help to improve the performance of a device using pre-trained models. Taking advantage of the data generated by rapidly increasing data received from sensors, e-AI can ensure that the devices output operate at the ideal state, whether in an industrial drive, a washing machine or a footstep detector. This is where Renesas focusses - endpoint intelligence. RENESAS E-AI: BORN FROM EXTENSIVE R&D ACTIVITIES As a leading semiconductor manufacturing company, Renesas has implemented such mechanisms in their factories. The technologies developed from these activities put Renesas in a position to share them

WHAT IS THE SPECIFIC ADVANTAGE OF THE RENESAS SOLUTION? FOCUS ON MCU/MPU LOW POWER FOR HIGH RESPONSE ENDPOINT E-AI When moving AI processing to the endpoint, power-performance becomes of prime importance. An off-the-shelf graphics card or smartphone accelerator will

Existing design assets

exceed the power and size limitations of industrial applications by many times. Plus, todayâ&#x20AC;&#x2122;s solutions need to have a platform approach and must scale depending on the application requirements. For instance, some basic algorithms might be processed simply by the software of a microcontroller. Some others may need some basic hardware accelerators. Still others might need significant hardware acceleration to meet the algorithms performance targets. UNIQUE ACCELERATORS AND A COMMON AI FRAMEWORK To address this variety of performance targets, Renesas has developed a dynamically reconfigurable processor (DRP) module, that can flexibly assign resources to accelerate e-AI tasks. DRP uses highly parallel processing to meet modern AI algorithm demands. As the DRP design is optimized for low power consumption, it can support multiple use cases of customized algorithms with rapid inference which will fit most embedded endpoint requirements. Renesas DRP enables high performance at low power, so that it fits ideally into embedded applications. DRP is dynamically reconfigurable, thus allowing adaption of use cases and/or algorithms within the same hardware. The good news is that whatever level or acceleration is utilized; the software tools and interfaces will stay the same. Renesas does not provide its own learning frameworks but provides tools that translate neural network models into a format that can be executed on Renesas MCUs and MPUs.

Deep learning (as Machine learning) Open source

Development program

e-AI Translator/ Checker

MCU/MPU-IDE Integrated Development Environment

Combined in e2 studio

Figure 3: Embedding neural network processing onto Renesas devices. 19


Figure 4: Renesas e-AI capability Index

The translator takes neural network models from common training frameworks such as Google’s TensorFlow as an input. These frameworks are typically used to train a neural network model. The inference will be executed by the Renesas MCU/MPU devices, simply by embedding the output of the translator tools into the respective program. It is as easy as that.

The initial e-AI/DRP roadmap shows 4 performance classes, each class adding 10 times of neural network performance to the previous class. The unique positioning for endpoint inference in combination with Renesas leading MCU/MPU technology gives our customers unmatched power/performance ratio for AI processing.

SPECIALIZED FOR EMENDED ARTIFICIAL INTELLIGENCE INFERENCE AT THE ENDPOINT. With its flexible and scalable e-AI concept, Renesas offers a future proof real-time, low power AI processing solution that is unique in the industry and addresses the specific needs for artificial intelligence in embedded devices at the endpoint.

For further information please visit the Renesas e-AI website at Renesas |


Renesas Electronics Simplifies Home Appliance Maintenance with Failure Detection e-AI Solution for Motor-Equipped Home Appliances Renesas announced the launch of its Failure Detection e-AI Solution for motor-equipped home appliances, featuring the Renesas RX66T 32-bit microcontroller (MCU). This solution with embedded AI (e-AI) enables failure detection of home appliances -- such as refrigerators, air conditioners, and washing machines -- due to motor abnormality. Property data showing the motor’s current or rotation rate status can be used directly for abnormality detection, making it possible to implement both motor control and e-AI– based abnormality detection with a single MCU. Using the RX66T eliminates the need for additional sensors, thereby reducing a customer’s bill of materials (BOM) cost. When a home appliance malfunctions, the motor operation typically appears abnormal when running and being monitored for fault detection in real-time. By implementing e-AI-based motor control-based detection, the failure detection results can be applied not only to trigger alarms when a 20

fault occurs, but also for preventive maintenance. For example, e-AI can estimate when repairs and maintenance should be performed, and it can identify the fault locations. This capability provides home appliance manufacturers the means to boost maintenance operations efficiency and improve product safety by adding functionality that predicts faults before they occur in their products. The Renesas Failure Detection e-AI Solution for motor-equipped home appliances can control up to four motors because it utilizes the high-performance RX66T MCU. Today’s washing machines typically incorporate three motors: One to rotate the washing tub, one to drive the

water circulation pump, and one to drive the drying fan. The Renesas Failure Detection e-AI Solution can therefore be used to control these three motors with a single RX66T chip while at the same time monitoring all three motors for faults.

Renesas Electronics Corporation Electronica Azi International | 1/2019

INSIGHT » Embedded Analytics

Machine learning and sensor fusion on embedded MCU Many promises of great value are currently linked to machine learning or self-learning algorithms. Big data and great computing power are required to see them come to fruition. For sensor applications in predictive maintenance, an ARM®-based microcontroller may be sufficient to implement AI algorithms. This has been demonstrated by Andreas Mangler, Director Strategic Marketing & Communications at Rutronik, together with his engineering team. Rutroniker: Mr. Mangler, you and your team have developed algorithms that require neither a cloud nor a high-performance PC but run on a microcontroller. Why? Andreas Mangler: We see a clear move in many industrial applications away from having data analyzed and evaluated externally by a service provider (cloud analytics) or a decentralized industrial PC (edge analytics) and toward having everything implemented in an separate protected environment as an embedded MCU-based system (embedded analytics). And all of this ideally with appropriate hardware encryption. IP protection and real-time capability of the system are at the center of decisions in

favor of embedded analytics, in particular the protection of the raw data, the algorithms used, and their sequential sequencing in order to finally obtain the desired information from big data. And yet, embedded analytics systems are usually based on mathematical models, patterns, and functions with which a target/actual comparison is carried out using the data already learned and the measured data.

processed time synchronously; the keyword here is sensor fusion. An embedded analytics solution is indispensable, especially in safety-relevant systems or where functional safety is required in real time and quasi ad-hoc decisions have to be made in the microsecond or nanosecond range.

Many tasks therefore not only involve pattern matching, which is not necessarily only available in the image processing of graphic data, but the processing of a large amount of sensor data with varying physical measured variables, which are typically

One measure, for example, is the use of stochastic filters, which can be well supported by the MCU’s memory organization. Another option is to use IIR filters instead of block-oriented FIR filtering as the digital filters, taking into account the different group delay and transient response of the filter topologies.

Figure 1: With each step from right to left, less computing capacity and storage space are available, i.e. big data must become smart data and instead of extensive algorithms, lean, self-learning algorithms are required. (Image source: Knowtion)

This actually sounds quite simple, but most probably it isn't. In a real-life scenario, how can such large amounts of data and algorithms be processed in a small microcontroller? The problem of sensor fusion is well described by the buzzword VUCA, i.e. Volatility, Uncertainty, Complexity, and Ambiguity. Volatility arises because the system is constantly changing in terms of its data, dynamics, speed, and limits. Uncertainties exist, e.g., due to noise and unforeseeable events. In addition, the systems are typically complex and there are ambiguities, as some states may have different causes. The aim is to assess this “hidden” information in order to better describe the system. Data can already be reduced through the previous rough estimation of the actual state.


INSIGHT » Embedded Analytics

For example, a heating control system for a gas heater that has to carry out a CO2

problem of synchronous data processing has to be considered for sensor fusion.

restrict the dynamic behavior over the time or frequency range. Moreover, a temperature difference of one degree does not have an effect on the system. The data therefore only needs to be analyzed down to an accuracy level of one degree.

Figure 2: Principle of sensor fusion and data extraction. (Image source: Knowtion) analysis could determine the exact outside temperature in parallel. This way, when used in Norway, the heater can certainly take a winter temperature of -30°C into account when calculating the measured value. In southern Spain, this temperature is extremely unlikely, or downright unrealistic, in the winter. Consequently, the GPS location sensor or logistics data of the heater supplier determines the amount of data to be evaluated. The formula is: Data reduction plus lean algorithms that are combined correctly. This requires four steps: Firstly, sensor deployment planning, i.e. how many sensors of which sensor type are required where? The second step concerns data selection, i.e. the question: Which data is actually required to detect anomalies? This is how “big data” becomes “smart data”. Above all, the trick here is to select as much data as necessary and as little as possible,

How can this be implemented in the MCU? The physical system and the possible states in reality are considered and described for this purpose. This then leads to an assessment of the state. These types of model can be defined in advance in the laboratory and stored in the microcontroller’s look-up table. The sensor data can then be compared with the model and outliers can quickly be identified as an anomaly. This means fewer measuring points are required, which in turn helps to save storage space. Can you explain this using a practical example? Of course; a typical application would be an intelligent hot water tank connected to a photovoltaic system. I start with sensor deployment planning and specify that several temperature sensors as well as pressure and acceleration sensors are needed. Now the state has to be assessed: Since I

Figure 4: Principle of parameter identification and anomaly detection. (Image source: Knowtion) while still managing to pick the right data. In the third step, the algorithms for pre-filtering need to be selected. The parameters required for analysis are then extracted in the fourth step. All the steps have to be precisely adapted to the system and the actual problem. Furthermore, the basic 22

Figure 3: Schematic design of a hot water tank with a PV system and several temperature sensors. (Image source: Knowtion) The next step is to identify the relevant parameters for the task, e.g. protecting against overheating. In this respect, the temperature of the solar collectors, the cold and hot water inflow, the heat exchanger, and the burner plays a key role.

Figure 5: A characteristic curve can be extracted from the raw data of several sensors using various filters, thereby making anomalies visible. (Image source: Knowtion)

know, for example, that the temperature at the solar collector in these parts will only be between -20°C and +50°C, any data outside this range can be omitted. From a purely physical point of view, the water in the tank cannot rise by 30°C from one minute to the next and, as a result, it is also possible to

Their data must be filtered out from the sensor fusion. Nothing else needs to be included in the analysis. This is followed by other filters to further reduce the amount of data. This means it is primarily about the parameter identification that influences my overall system. Electronica Azi International | 1/2019

INSIGHT » Embedded Analytics

Selected, filtered data are now available. What is the next step? Certain patterns and anomalies can now be detected via statistical filters, for instance and these are the interesting points in order to detect overheating at an early stage in our example. By filtering out the anomalies, I am able to limit their data analysis. In order to describe the anomalies, extreme system values, i.e. the minimum and maximum values and the turning points, have to be explained mathematically. With cloud and edge analytics this is achieved through differential equations. However, these are too extensive for embedded analytics. We have, therefore, replaced the mathematical curve discussion with a self-learning iteration method. In principle, this is not high-level science, but mathematics taught to the first year students on every basic science course. For the extraction and subsequent visualization of data, a two-dimensional representation is helpful, as it allows you to choose a threedimensional representation in order to place certain identified parameters in the z-axis of the representation. This is basically comparable to a “topological map” of the sensor data.

We then implemented a self-learning iteration method on a STM32 F4 from ST Microelectronics using the so-called dictionary method. This entailed programming iterative queries that determine which mathematical function from the dictionary is to be used to replace which subsection of the sensor function. In just three or four query loops, we arrived at a result that describes the sensor characteristic curve with basic mathematical functions - in other words, an exact modeling of the system that immediately identifies anomalies. The “Sensor Function Dictionary” contains only 5 basic mathematical functions, such as radial basic functions (RBF) or linear functions. The self-learning approach further reduced the segments and the amount of data, meaning just 30 segments were needed, i.e. 300 instead of 400 data points. This was all achieved in real time. Can this be achieved with any MCU? In theory yes, in practice no. When processing sensor signals from several sensors (sensor fusion), the real-time capability of the MCU is the main focus of attention. Extremely efficient programming is required

Figure 6: The three-dimensional image of the filtered sensor data enables excellent identification of extreme points. (Image source: Knowtion) How did you proceed with this? We chose a three-dimensional analysis method in order to make the extreme values recognizable and to subsequently compare the sensor model data. It is already possible to see here that some parameters have very little or no influence on the anomalies. This data can then be neglected or filtered out, as deemed necessary. In order to explain the previously described “topological landscape (figure 5) mathematically, we divided it into less than 100 sub-segments and specified each segment with just 10 measuring points for the iteration, thereby limiting the required storage space from the outset. We defined the difference between the model data and the deviating data as ±1.5%. These are the detection limits of the anomalies.

for time-synchronous processes, e.g. MEMS sensors with six degrees of freedom. When it comes to time-critical measurements, we discovered that programming on the HAL (high

Figure 7: The STM32 has sufficient memory and performance for processing sensor fusion data and enables, for example, predictive maintenance as an embedded real-time system. (Image source: Rutronik)

abstraction layer) can lead to measurement errors in the time domain, or that the dynamic changes of the anomalies to be detected were insufficient. The consequence of this was the decision in favor of low-level programming. The memory requirement depends on the previous sensor deployment planning. We chose the STM32, as the analog and digital peripherals combined with the direct response via the low-level allow assembler-based programming in order to implement sensor fusion with the existing RAM and ROM. Is this process now tailored to a specific application? No, the self-learning algorithms allow us to map any non-linear system of all sensor types and sensor fusions. In addition, it also meets all other requirements placed on embedded analytics: It works offline, i.e. without a cloud, in embedded real-time systems, runs on a standard ARM MCU, and is both robust and scalable. What was the biggest obstacle during the development phase? That it demands comprehensive know-how in various disciplines. And this, in turn, requires a whole team of experts. When selecting parameters and pattern matching or determining anomalies, the physical overall system has to be understood fully. It also requires extensive knowledge of all types of sensors and how they work in order to select the appropriate higher mathematics and self-learning algorithms. We have the big advantage of having in-house sensor specialists, analog specialists, and MCU embedded specialists within the project group. In addition, we benefited greatly from the previous research carried out by our partner universities and the specialists in our third party hardware and software specialist network, for example at Knowtion, which specializes in sensor fusion and automatic data analysis. We can summarize this RUTRONIK proof of concept development as follows: Artificial intelligence and machine learning at the embedded MCU level is not simply a software task. A comprehensive physical and electrochemical understanding of the sensors and how they function with regard to process anomalies is absolutely necessary in order to implement predictive maintenance. The RUTRONIK engineering resources required for this are available to our customers and provide the necessary support for selecting perfectly coordinated products. Rutronik | 23


The Raspberry Pi Pmod HAT Adapter from DesignSpark Here at DesignSpark we are always keen to bring you exciting and relevant products, the latest offering from the DesignSpark Brand has been designed to expand your Raspberry Pi’s application portfolio even further. Anyone familiar with the world’s favourite computer will be aware of its potential as a development tool, becoming increasingly popular with developers and being used in multiple disciplines. With his fantastic Pmod HAT you can utilise any of the broad range of Pmods manufactured by Digilent ensuring your Raspberry Pi development projects gets as many advantages as possible from the get-go. The DesignSpark Pmod HAT (144-8419) an be used with any of the following Raspberry Pi models: ■ Raspberry Pi Model A+ (833-2699) ■ Raspberry Pi Model B+ (811-1284) ■ Raspberry Pi 2 B (832-6274) ■ Raspberry Pi 3 B (896-8660) ■ Raspberry Pi Zero W ■ Raspberry Pi Zero

TIME FOR A RASPBERRY PI PMOD HAT ADAPTOR CLOSE UP SPECIFICATION: • 5mm Follows Raspberry Pi HAT Specification • Provides access to full-line of Digilent Pmod Peripheral modules • Three Pmod ports: two SPI (JA/JB), one I2C (JB), one UART (JC), all three GPIO capable • SPU, UART, I2C, GPIO Connections are supported • 5V barrel jack for external power • 40-pin Raspberry Pi GPIO header − Connector arranged as a 2x20-pin header, female, 0.1-inch pitch − 26 GPIOs, 3.3V compatible inputs and outputs − Two I2C ID EEPROM pins − 8 GND pins − Two 3.3V pins − Two 5V pins • One power supply connector, or powered by the Pi via GPIO 5v pins • 16mA current limit for all PMOD GPIOs 24

Electronica Azi International | 1/2019

PRODUCT NEWS Âť Embedded Systems

INTERFACING WITH DIGILENT The DesignSpark Pmod HAT allows you to interface your Raspberry Pi with any one of the multitudes of diverse Digilent Pmods that are available from RS Components, such as:

PmodAD1 (134-6443), which is a two channel 12-bit ADC module with a sampling rate of 1 million samples per second.

PmodISNS20 (136-8069), which is a high accuracy Hall Effect current sensor.

PmodOLEDrgb (134-6481), an organic RGB LED module with 96x64 pixel display. And 16-bit colour resolution.

When you combine your Raspberry Pi, the DesignSpark Pmod HAT Adapter and any one of the great Pmods manufactured by Digilent, you have a powerful and versatile prototyping platform with a wealth of hardware configurations at your fingertips. For more information, visit

KUNBUS-GW MODULAR GATEWAYS FAST & LOW-COST NETWORK CONNECTION Nowadays, the demands on historically developed communication networks are getting more and more complex. Different networks with various data speed and data traffic are expected to communicate among each other. Where previously a classical fieldbus like CANopen, PROFIBUS or DeviceNet

was the preferred network, Ethernet based protocols supplement or replace them today. A complete exchange of the communication modules is expensive and often not realizable. Therefore, the modular gateways from KUNBUS offer an excellent way to connect fast, easily and cost-efficiently

different networks in compliance with the protection of investments.



Revolution Pi is an open, modular and inexpensive industrial PC based on the well-known Raspberry Pi. Housed in a slim DIN-rail housing, the three available base modules can be seamlessly expanded by a variety of suitable I/O modules and fieldbus gateways. The 24V powered modules are connected via an overhead connector in seconds and can be easily configured via a graphical configuration tool. To achieve a real industrial suitability according to EN 61131-2 or IEC 61131-2, the rather unknown Raspberry Pi Compute Module was used as a basis. The module, which looks like a notebook RAM bar, is limited to the essentials and does not have any external interfaces. With the Raspberry Pi Compute Module the foundation has been laid for equipping the Raspberry Pi with a robust and industry-compatible periphery developed by us, which meets all important industrial standards. On the software side, the Revolution Pi has a specially adapted Raspbian operating system, which is equipped with a real-time patch. The use of Raspbian ensures that most of the applications developed for the Raspberry Pi can also be used on the Revolution Pi. The KUNBUS-GW gateways consist of two modules each, which contain the respective protocol and are fitted with a common interface for the data exchange. Each module is integrated into the respective network as a Slave. The data is exchanged via a jumper that connects both modules. The gateway converts all data that the target network can transfer. It omits nontransferable data and adds necessary data in the new network. Thus, the operators can transfer all necessary data from one closed network to another. This intelligent approach makes the KUNBUS-GW gateway highly flexible. KUNBUS offers a protocol converter that can be attached to DIN rails and detached again easily by means of plug and play. The protocols themselves are located in compact modules, whose housings are just 22.5 × 101.4 × 115 in size. The modules are supplied with an operating voltage of 24 Volt whereby the power consumption is less than 3 Watt. Whereas conventional gateways have to be replaced completely in the event of a technical malfunction, with KUNBUSGW gateways it is sufficient to replace the defective module only. Apart from the resultant cost benefit from the module design, this approach offers another key benefit: The decision for or against a certain network only has to be made just before delivery. Consequently, it is possible to react better to any special and recent changes in requirements which have occurred. Customized solutions are also possible. The gateways are equipped with an integrated web server; program parts are updated via FTP server. The gateway is matching with the most important protocols of industrial communication networks.

Author: Bogdan Grămescu 26

Aurocon COMPEC authorised distributor for RS Components Electronica Azi International | 1/2019

Leuze: Distance sensors ODS 110 / HT 110 Smart is when more than two switching points can be combined in one sensor With our expanded line of distance sensors, you can choose from a large selection of different designs with especially large operating ranges and multiple switching points. Compartment occupation check

Push-through protection

Collision protection

Aisle end


Shelf positioning

Special features of the product range: ■ Absolutely independent of color and surface thanks to very small black/white error ■ High repeatability ■ Large operating ranges in various sizes ■ IO-Link ■ Analog outputs and up to three independent switching points ■ Different equipment options, e.g., with measurement value display ODS 110 / HT 110

ODS 10 / HT 10


Cost-optimized series for automated material flow with an operating range of 5 meters

High-performance series for automated material flow with an operating range of 8 meters

Especially small model as switching version with an operating range of 2.5 meters and 2 switching points

■ Operating range up to 5m on bright objects and 3m on dark objects ■ Repeatability < 5 mm ■ Models with 2 switching outputs or analog output ■ Simple configuration via teach buttons or IO-Link ■ Easy alignment thanks to focused, highly visible red light laser

■ Operating range up to 8 m on objects and 25 m on reflective tape ■ Repeatability < 5 mm ■ Models with up to 3 switching outputs or analog output ■ Configurable analog output: current of 4-20mA or voltage of 0-10V ■ Easy configuration via display with control buttons or IO-Link ■ Easy alignment thanks to focused, highly visible red light laser

■ Operating range up to 2.5 m on objects ■ Repeatability of 10 mm ■ Models with up to 3 switching outputs ■ Simple configuration via teach button or IO-Link ■ Large infrared light spot

28 Electronica Azi International | 1/2019

Sensor Instruments: Accurate High-Speed Counting of Stacked Plastic Caps Especially the counting of transparent, stacked plastic caps, as they for example frequently are used in the packing industry, usually is a highly problematic application because on the one hand the edges of individual caps are not always exactly aligned, and on the other hand copy counters that work with the reflected-light principle do not provide a reliable counting result especially with transparent objects. Now, however, the A-LAS-N-F16-9.5x0.8-150/80-C-2m laser transmitted-light sensor in combination with the SPECTRO-1-CONLAS control unit performs this application task without any problems. The approx. 9.5mm wide and 0.8mm high laser light strip is partially covered by the edges. If the laser fork sensor is moved along the stack, for example by a linear unit or a robot, the laser light curtain is partially covered. The degree of covering of the laser light strip increases when an edge is reached, whereas between the edges the level of covering of the laser spot is lower. Due to the transmitted-light method transparent objects show the same behaviour as non-transparent materials. By means of an alternating signal sequence and differential signal evaluation in the SPECTRO-1-CONLAS control unit the edges are accurately detected even if the stack shows a wave shape. The high scan frequency of the sensor system of typ. 100kHz no doubt is of great advantage here. Furthermore, special algorithms such as the dynamic dead time prevent multiple counting of edges. When an edge is detected a digital signal (0V/+24V signal level) is provided at the output of the control unit. With the Windows® software that is included in the delivery the sensor system can be easily parameterised and monitored at the PC through the serial interface, inclusive of a digital scope function that shows the signal characteristics quasi in real time. Depending on the object size and on the available space, various fork sizes and laser light curtains are available. The forks feature a robust aluminium housing, and the optics covers are made of scratch-resistant glass.

Contrinex: RFID read/write modules with IO-Link ■ ■ ■ ■ ■

Small Ø4 x 35 mm or M5 x 35 mm stainless steel housing Diffuse and through-beam operating principles Visible red light on all types Sensing range, diffuse: up to 100 mm Sensing range, through-beam: up to 400 mm

Competitive Advantages ■ First-class sensing ranges ■ Smallest self-contained solution on market with IO-Link ■ IO-Link, ready for Industry 4.0 and Industrial Internet of Things ■ Sensitivity adjustment (through IO-Link only) ■ Embeddable thanks to a focused beam

Applications ■ Pharma ■ Semiconductor industry ■ Logistics and warehouse IO-Link functionalities Process data: Detection state & Stability alarm Parameters for through-beam: Output configuration, output timing, sensor mode, detection counter, event flags, maximum and actual sensor temperature sequence choice, sensitivity, sequence choice. Parameters for diffuse: Output configuration, output timing, sensor mode, detection counter, event flags, maximum and actual sensor temperature, sensitivity, teach command. 29

Contrinex: New generation of IO-Link-enabled smart sensors IO-Link inductive sensors with Smart Sensor Profile are easier to integrate into existing systems and can enhance maintenance strategies. Contrinex announces a new generation of cloud-ready sensors that takes standardization to the next level. Thanks to their Smart Sensor Profile (SSP), data from these new inductive IO-Link sensors has a highly uniform structure for greater ease of integration into existing systems. In addition, they provide a range of pre-defined functions to enhance customers’ preventive or predictive maintenance strategies. Designed according to IO-Link Smart Sensor Profile (SSP)

3.3 , Contrinex’s new inductive sensors meet the industry standard for uniformity of data. As digital measuring sensors with a disable function, their performance is equivalent to a conventional analog output sensor, but with a digital data output. Devices have a standard 24-bit process data input (PDI) and 8-bit output (PDO). For example, distance, switching counter and temperature measurements can be assigned 16-bit values while the output switching signal, switching signal and configurable alarms can have 8-bit values. Users can define many aspects of the sensor’s IO-Link configuration for themselves, including distance, counter and temperature. They can also select from a range of switchpoint modes (Deactivated, Single Point, Windows Mode, Two-Point Mode) and pre-defined preventive maintenance functions (operating hours over sensor lifetime/since last power-up, switching counter over sensor lifetime/since last power-up, sensor autodiagnostic alarm). The new generation will roll out first across standard inductive types in both cylindrical (M8 to M30) and cubic housings (C8 and C44).

POSITAL: Upgraded Analog Programmable Rotary Encoders Improved Accuracy with Faster Dynamic Responses POSITAL’s updated analog programmable rotary encoder are now available with even more features. ■ Broader supply voltage range for mobile machine applications ■ Increased accuracy, resolution and number of turns ■ Further programmability function with our UBIFAST configuration tool ■ Singleturn encoders with measuring range less than 360 degrees available POSITAL has launched the new generation of analog rotary encoders for position control. Compared to the earlier analog encoders, these new models feature improved accuracy, faster dynamic response and new programming options. They also accept a wider range of supply voltages, an advantage for mobile machinery applications. POSITAL’s analog encoders are designed for positioning tasks that use analog control systems. Outputs are either voltage (0-5V, 0.5-4.5V, 0-10V or 0.5-9.5V) or current (420mA). The magnetic measurement mechanism is wearfree and extremely durable so that these devices offer much better accuracy, reliability and service life than conventional potentiometers. 30

Customizable Programmable Measurement Characteristics An important feature of the POSITAL analog encoders is that they are programmable with measurement characteristics that can be customized to meet specific application requirements, (it is also available with POSITAL’s digital encoders with SSI and incremental interfaces). Programming can be carried out at the factory, in a distributor’s warehouse or at the customer’s job site, thanks to POSITAL’s easy-to-use UBIFAST programming tool. Programmable characteristics include direction of rotation (CW or CCW), zero set and the encoder output range. Measurement range programming allows the full range of electrical outputs (voltage or current) to be set to match a user-defined range of mechanical motion, resulting in significant improvements to control system accuracy. For single-turn models, the measurement range can be set to 90°, 180°, 270° or 360°. For multiturn models, the range can be set anywhere between 1 and 65,536 complete rotations. Analog encoders are available with push-buttons on the housing that enable the user to easily specify the upper and lower limits of the mechanical motion, with the electrical output fully spanning this range. Electronica Azi International | 1/2019

Sensor Instruments: Inline Color Measurement of Paint Through a 15mm Thick Inspection Glass Usually the color checking of color paint during production primarily is done in a laboratory. A paint sample is taken, and the color of this sample is then checked by way of a thin paint layer in dry condition. This process of course takes some time, time in which paint production might run out of the permissible tolerances and thus might require time-consuming and expensive follow-up treatment. It would therefore be highly desirable to have information about the color quality of the product directly after the dispersing of color pigments into the carrier material (binder system and diluting agent). An inspection glass therefore was integrated in the production line to allow an optical check of the already mixed color paint. A Sensor Instruments SPECTRO-3-28-45째/0째-MSM-ANA-DL color sensor then was positioned before this inspection glass at a distance of approx. 20mm. The ringshaped, similar-to-daylight light that is installed at 45째, and the vertical (0째) position of the true-color detector ensure that there are no unwanted reflections at the inspection glass surface which might affect the measuring accuracy. The color sensor provides color data with an accuracy of dE=0.3. In addition to the SPECTRO-3-Scope MSMANA parameterisation software, a monitoring software also is used here. On the PC monitor this software shows information about the color values and their trend and also displays if one of the color values should exceed the tolerance limit. Data are also saved in a file together with date, time, and production-specific information. The three analog signals (4mA ... 20mA or 0V ... +10V) that are available at the output furthermore can be used to control the color production process.

Sensor Instruments: Clear Strategy or Plain Guessing? In the last years the spray nozzles at windscreens have seen quite some development. The jet now comes as a fan jet or spot jet. A homogeneous spray mist application onto the intended angle range is now accurately guaranteed with fan jets, as is the point-shaped jet application on the front cameras by means of exactly directed and hardly diverging spot jets. The individual mechanical adjustment of the inclination of the fan jet and spot jet makes it possible to use the spray nozzles for various vehicle types. Of course, such adjustment today in most cases is performed automatically, which apart from a sophisticated handing system above all is made possible by corresponding sensors So-called laser transmitted-light line sensors are used for checking the respective angle positions. Especially due to the relatively large angle of the fan jet in one plane it is appropriate here to use a large scan range (L-LAS-TB-100-T/R-AL-SC), which furthermore allows the simultaneous detection of the spot jet. It must be ensured, however, that the two planes are checked simultaneously, which means that a second laser sensor with a smaller detection range that is arranged vertically to the first one is required (L-LAS-TB-50-T/R-AL-SC). With a special L-LAS-SprayControl-Scope V1.0 software that was specifically developed for the spray process the positions of the respective spot jets and of the fan jet are determined in both planes. From the distance data the downstream PLC then determines the opening angle of the fan jet, the angle position of the fan jet, and the emission angles of the spot jets in both planes. Based on these data the spray jets can then be optimally adjusted.


Rent Your SMT Line Not having to spend a lot of money upfront can help your business manage its cash flow more effectively. Whether you’re starting out or expanding, renting is the smart option for your business. Staying Ahead of the Game We live in a time of constant changes where every day we have to adapt to our customers’ needs. Either because of the new technological challenges, a focus on ROI “return on investment” or better productivity against new competition. The reasons can be many, and we believe we can help in providing the right solution. Keeping up with the pace and always being a step ahead of your competitors is what we are all striving to. Today you can rent almost everything starting from airplanes and properties to cars and machines. So, why not rent your next SMT equipment?

Advantages of Renting 1. 2. 3. 4. 5. 6. 7. 8.

It’s the right to use the equipment, and not the ownership, that creates revenue and profit for the company. Rentals can be customized from 18 months, and to customer's needs: monthly, quarterly or annually. Renting allows your company to “protect” your normal bank relationship. Renting allows you to minimize your risk on big asset depreciation. Renting does not affect a take away from the balance sheet as debt-financed assets; it has a positive effect on a number of key figures. Cash Flow; payments are allocated over the period during which the equipment is used and generates profit. (When you have bought the equipment your cash is locked away) Renting strengthens the company’s competitiveness; use your cash where your returns are the greatest. (Production companies often choose to use their cash on new development, salaries or purchase of raw materials which equals the highest return on investment) Flexibility – you are not “stuck” with your SMD-Line. Renting provides you the possibility to change your equipment depending on your customer's demands and market requirements! What equipment fits your business best? To give you a choice in our rental concept, we have created two different product production lines. The first one is called the “PREMIUM SMT CONCEPT” and the second one is called “ECONOMIC SMT CONCEPT”.

PREMIUM SMT CONCEPT It contains all the premium brands you know such as Assembléon, DEK, Vitronics-Soltec, and others. Everything to make you feel secure with the machines that will deliver your client's products. Scalable from 9,000 to 165,000+ CPH.

ECONOMIC SMT CONCEPT Here we have selected the equipment that is of high quality and proven reliability and that gives you a competent and powerful alternative, that maintains a lower price image without compromising on quality and reliability, and with access to good service and support. Scalable from 8,000 to 80,000+ CPH. LTHD Corporation S.R.L. Head Office: Timișoara - ROMÂNIA, 300153, 70 Ardealul Str.,, Tel.: +40 256 201273, +40 356 401266, Fax: +40 256 490813

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Electronica Azi International | 1/2019

FINANCIAL SOLUTIONS FOR ELECTRONIC MANUFACTURING Standard leasing solutions are restricted to any improvement / changes. The financial solution of SMTHOUSE is tailored to the needs of electronic production environments and includes the following additional advantages. n n n n n n n

Choose your manufacturing equipment based on today’s and future need from market leading suppliers. Total SMT line solutions or single machines Best competitive monthly rates based on contracts between 18-72 months Fixed rates including service, maintenance and spares SMTH Technology Guarantee allows you to swap your installed equipment during the contract period Additional options can be added into the running contract at any time Flexible options after the end of the contract based on your needs

Configure your SMT Lines upon your demands from world known manufacturers like KNS/Assembleon, Hanwa/Samsung, Mirae, DEK, Reprint, Vitronics Soltec, MEK, TRI and others. Adapt it to your changing demands during the rental agreement and get your full flexibility regarding changing production demands. PREMIUM LINE / 70 - 175.000 cph (IPC) Renting instead of buying from 13.995,- EUR per month Scalable output without the need to exchange machines COMPETITIVE LINE / 36.000 Bt/Std. (IPC) Highly flexible SMT Production line

Renting instead of buying from


EUR per month

ENTRY LINE / 15.000 Bt/Std. (IPC) Renting instead of buying from 2.495.- EUR per month Complete SMT Production Line for low volume and NPI

LTHD Corporation S.R.L. Head Office: Timișoara - ROMÂNIA, 300153, 70 Ardealul Str.,, Tel.: +40 256 201273, +40 356 401266, Fax: +40 256 490813





HARTING showcases new standards and solutions for intelligent infrastructure in the field The HARTING Technology Group will once again present a wide range of new products and smart solutions (Hall 11 / Stand C13) at this year's HANNOVER MESSE (1 April to 5 April 2019). HARTING will be emphasising this year's motto for the HANNOVER MESSE: "Integrated Industry – Industrial Intelligence". This means combining human and artificial intelligence to create industrial intelligence that adds value for customers.

has adapted its connector concept to future requirements and can handle 1GBit/s for shorter routes as well as 10Mbit/s for longer distances with one standardised mating face. Further development of the successful RJ Industrial® standards has resulted in toolfree assembly, simple operation and a robust metal housing. The ability to use these for AWG 26 to 22 wire strands and

"microSNAP": together with Kuka, HARTING will be presenting a corresponding robotoperated charging station for quickly recharging batteries separately.

[l.] M12 PushPull for factory automation, [r.] Dr. Metrix, HARTING's new heroine for metric circular connectors with PushPull interlock mechanism. HARTING is driving development forward in Ethernet connectivity for the networks of the future. Single Pair Ethernet (SPE) is a hot topic in the market for industrial cabling. HARTING provides the right standardised interfaces for this and will be showcasing the first serial products at HANNOVER MESSE 2019, making the leap as a pioneer of Industrie 4.0 from a technology trend to real-life application and a customer-specific solution. The HARTING stand will include the first IP20 interface consisting of a connector and a PCB socket. The core of this new HARTING T1 Industrial series is a standardised SPE mating face that can be used for all housing variants from IP20 to IP65/67. HARTING 34

their robust cable attachment are properties that make installation easier and quicker for the installer. While the classic RJ45 was a telecommunications development that was not enough for every industrial demand, the HARTING RJ Industrial® MultiFeature series can cope with all the requirements and challenges of a hard operating environment. Safe Cat. 6A performance, IP20 and IP65/67 housing combined with PoE power supplies IEEE802.3af (PoE 15.4W) / IEEE802.3at (PoE 25.5W) / IEEE802.3bt (PoE 100W) supply data and power for any device. Dr. Metrix is the new face of time-saving PushPull interlock mechanisms. HARTING's new heroine represents clever PushPull tech-

nology on metric circular connectors and connection in seconds. Dr. Metrix first provided a comprehensive M12 PushPull solution for factory automation on SPS IPC drives. In machinery, Han® 1A facilitates the efficient combination of tools and modules such as heating or cooling units, fans, control terminals, lighting systems, drives, vibration conveyors and similar equipment. In traffic technology, you can use Han® 1A to connect door drives and access systems, as well as lighting, headlights, loudspeakers, screens, display panels, warning and alarm lights, push buttons, acoustic signals and windscreen wipers. HARTING now offers the metal housing for the Han® B, EMC and M series in versions that allow the rear assembly of contact inserts. The new option makes it easier to assemble interfaces for control cabinets, aimed particularly at applications in machinery and automation, robotics and traffic and energy technology. The new Han® solutions also allow for prefabricated inserts to be locked in place directly in the bulkhead housing – from the inside of the control cabinet. With Han® F+B, HARTING has created a range of connectors specifically for the requirements of the food industry. Smooth surfaces make it harder for bacteria to accumulate. The housings and seals are robust, safe and long-lasting: they prevent the internal contacts coming into contact with the water from high-pressure cleaners or aggressive cleaning agents. Han® F+B housings are made from highperformance plastic and are water-repellent and suitable for cleaning agents. They are certified by Ecolab and have FDA 21 licences. The water flow from high-pressure cleaners cannot get into the housing as long as it remains closed. The HARTING stand in Hanover will be also be presenting the "microSNAP", the latest vehicle concept from Swiss automotive visionary Frank M. Rinderknecht. The bold idea behind "microSNAP": the quick removal and replacement of the chassis, drive ("Skateboard") and bodies ("Pods"). Together with Kuka, HARTING will be presenting a corresponding robot-operated charging station for quickly recharging batteries. A Kuka robot connects the HARTING charging plug and ends the charging process when the drive battery has reached the required charge level. HARTING Technology Group Electronica Azi International | 1/2019

Profile for Electronica Azi

Electronica Azi International no. 1 - 2019  

The English version of the Electronica Azi magazine

Electronica Azi International no. 1 - 2019  

The English version of the Electronica Azi magazine

Profile for esp2000