Design and Implementation of Digital Image Processing Using STM32F407ZG Microcontroller for Traffic Light Management System

Traffic jam is one of the big problems happened ina big city, such as Jakarta and Bandung. Sometimes the trafficjam is caused by the inappropriate traffic light time control. Thispaper describes the prototype implementation of traffic lightcontrol system by analyzing the image condition of the streetcaptured by camera. The digital signal processing algorithm andthe time of the traffic light are controlled by a STM32F407ZGMicrocontroller. The size of Random Access Memory (RAM) islimited, so an additional external RAM is used in this research.The system input is 120 x 160 pixels of road image which alreadystored in the SD Card. A digital signal processing is used todetermine which road has more traffic, and then the system willadjust the time of the traffic light based on the calculation

Design and Implementation of Digital Image Processing using STM32F407ZG Microcontroller for Traffic Light Management System

Traffic jam is one of the big problems happened ina big city, such as Jakarta and Bandung. Sometimes the trafficjam is caused by the inappropriate traffic light time control. Thispaper describes the prototype implementation of traffic lightcontrol system by analyzing the image condition of the streetcaptured by camera. The digital signal processing algorithm andthe time of the traffic light are controlled by a STM32F407ZGMicrocontroller. The size of Random Access Memory (RAM) islimited, so an additional external RAM is used in this research.The system input is 120 x 160 pixels of road image which alreadystored in the SD Card. A digital signal processing is used todetermine which road has more traffic, and then the system willadjust the time of the traffic light based on the calculation

Preliminary design issues for inertial rings in Ambient Assisted Living applications

A wearable 9dof inertial system able to measure hand posture and movement is presented. The design issues for the deployment of measurement instrumentation based on no-invasive ring-shaped inertial units and of a wireless sensor network by them composed are described. Compromises between the physical and functional proprieties of a wearable device and the requirements for the hardware development are discussed with attention to an handsome design concept aesthetically effective. Techniques of power saving based on an optimized firmware programming are mentioned to realize a performing battery powered system featured by an exhaustive operation time. The printed circuit board (PCB) design rules, the choice of the components and materials, the fusion of inertial data with optical sensors outcomes are also discussed. Previous experience in the field of wearable systems are mentioned in the presentation of the results that emphasize the functional and application potential of a 9dof inertial system integrated in a ring-shaped device. � 2015 IEEE

Аналіз особливостей використання ресурсів мікроконтролера для розпізнавання мовлення

В роботі виконано аналіз використання обчислювальних ресурсів мікроконтролера для машинного навчання та розпізнавання голосу. Поставлено експеримент для визначення залежності часу розпізнавання ключового слова, об’єму використаної оперативної пам’яті та пам’яті програм в залежності від кількості мел-частотних кепстральних коефіцієнтів та типу згорткової нейронної мережі. Для проведення експерименту використано плату розробки Arduino Nano 33 BLE Sense. Модель нейронної мережі створено та треновано на програмній платформі Edge Impulse. В результаті аналізу встановлено, що пам’яті 32-х бітного мікроконтролера достатньо для обчислень та використання нейронної мережі. Однак час класифікації ключового слова складає приблизно 0,3 с, відповідно розпізнавання довгих фраз може зайняти декілька секунд, що не завжди є прийнятним.The use of neural networks for information recognition, in particular, voice, expands the functional capabili-ties of embedded systems on microcontrollers. But it is necessary to take into account the limitations of the microcontroller resources. The purpose of the work is to analyze the impact of voice processing parameters and neural network architecture on the degree of microcontroller resources usage. To do this, a database of samples of the keyword, samples of other words and voices, and samples of noise are created, the probability of recognizing the keyword among other words and noises is evaluated, the dependence of the amount of memory used on the microcontroller and the decision-making time on the num-ber MFC coefficients is established, the dependence of the amount of used memory of the microcontroller and the decision-making time on the type of convolutional neural network is established also. During the experiment, the Arduino Nano 33 BLE Sense development board was used. The neural network model was built and trained on the Edge Impulse software platform. To conduct the experiment, three groups of data with the names "hello", "unknown", "noise" were created. The group "hello" contains 94 examples of the word "hello" in English, spoken by a female voice. The "unknown" group contains 167 examples of other words pronounced by both female and male voices. The "noise" group contains 166 samples of noise and random sounds. According to Edge Impulse's recommendation, 80% of the samples from each of the data groups were used to train the neural network model, and 20% of the samples were used for testing. Analysis of the results shows that with an increase in the number of MFC coefficients and, accordingly, the accuracy of keyword recognition, the amount of program memory occupied by the code increases by 480 bytes (less than 1%). For the nRF52840 microcontroller, this is not a significant increase. The amount of RAM used during the experiment did not change. Although the calculation time of the accuracy of the code word definition increased by only 14 ms (less than 5%) with the increase in the number of MFC coefficients, the calculation procedure is quite long (approximately 0.3 s) compared to the sound sample length of 1 s. This can be a certain limitation when processing a sound signal with 32-bit microcontrollers. To analyze phrases or sentences, it is necessary to use more powerful microcontrollers or microprocessors. Based on the results of experimental research, it can be stated that the computing resources of 32-bit microcontrollers are quite sufficient for recognizing voice commands with the possibility of pre-digital processing of the sound signal, in particular, the use of low-frequency cepstral coefficients. The selection of the number of coefficients does not significantly affect the amount of used FLASH and RAM memory of the nRF52840 microcontroller. The comparison results show the superiority of the 2D network in the accuracy of the keyword definition for both 12 and 13 MFC coefficients. The use of a one-dimensional convolutional neural network for voice sample recognition in the conducted experiment provides memory savings of approximately 5%. The quality of keyword recognition with the number of MFC coefficients of 12 is approxi-mately 0.7. For 17 MFC coefficients, the recognition quality is already 0.97. The amount of RAM used in the case of the 2D network has decreased slightly. Voice sample processing time for both types of networks is practically the same. Thus, 1D convolutional neural networks have certain advantages in microcontroller applications for voice processing and recognition. The limitation of voice recognition on the microcontroller is the sufficiently long processing time of the sound sample (ap-proximately 0.3 s) with the duration of the sample itself being 1 s, this can be explained by a sufficiently low clock frequency of 64 MHz. Increasing the clock frequency will reduce the calculation time

A Tiny Convolutional Neural Network driven by System Identification for Vibration Anomaly Detection at the Extreme Edge

Vibration data analysis is the driving tool for the Structural Health Monitoring (SHM) of structures in the dynamic regime, i.e., structures showing important oscillatory behaviours, which largely dominate the transportation back-bone: from terrestrial/aerial vehicles (e.g., trains, aircraft, etc.) to the supporting infrastructures (e.g., bridges, viaducts, etc.). Outstanding opportunities have recently been disclosed in the field of Intelligent Transportation Systems (ITS) by the advent of sensor-near processing functionalities, eventually empowered by Artificial Intelligence (AI). The latter allow for the extraction of damage-sensitive features at the extreme edge, without the need of transmitting long time series over the monitoring network. In this work, we explore for the first time a novel anomaly detection workflow for on-sensor vibration diagnostics, which combines the unique advantages of embedded System Identification (eSysId) as a data compression strategy with the computational/energy advantages of Tiny Machine Learning (TinyML). Experimental results conducted on a representative SHM dataset demonstrate that the proposed pipeline can achieve high classification scores (above 90%) for the health assessment of the well-known Z24 bridge. In particular, the minimal inference time (less than 44 ms) and power consumption performed while running on three different general-purpose microprocessors make it a promising solution for the development of the next generation of SHM-oriented ITS

Reconfigurable Antenna Systems: Platform implementation and low-power matters

Antennas are a necessary and often critical component of all wireless systems, of which they share the ever-increasing complexity and the challenges of present and emerging trends. 5G, massive low-orbit satellite architectures (e.g. OneWeb), industry 4.0, Internet of Things (IoT), satcom on-the-move, Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles, all call for highly flexible systems, and antenna reconfigurability is an enabling part of these advances. The terminal segment is particularly crucial in this sense, encompassing both very compact antennas or low-profile antennas, all with various adaptability/reconfigurability requirements. This thesis work has dealt with hardware implementation issues of Radio Frequency (RF) antenna reconfigurability, and in particular with low-power General Purpose Platforms (GPP); the work has encompassed Software Defined Radio (SDR) implementation, as well as embedded low-power platforms (in particular on STM32 Nucleo family of micro-controller). The hardware-software platform work has been complemented with design and fabrication of reconfigurable antennas in standard technology, and the resulting systems tested. The selected antenna technology was antenna array with continuously steerable beam, controlled by voltage-driven phase shifting circuits. Applications included notably Wireless Sensor Network (WSN) deployed in the Italian scientific mission in Antarctica, in a traffic-monitoring case study (EU H2020 project), and into an innovative Global Navigation Satellite Systems (GNSS) antenna concept (patent application submitted). The SDR implementation focused on a low-cost and low-power Software-defined radio open-source platform with IEEE 802.11 a/g/p wireless communication capability. In a second embodiment, the flexibility of the SDR paradigm has been traded off to avoid the power consumption associated to the relevant operating system. Application field of reconfigurable antenna is, however, not limited to a better management of the energy consumption. The analysis has also been extended to satellites positioning application. A novel beamforming method has presented demonstrating improvements in the quality of signals received from satellites. Regarding those who deal with positioning algorithms, this advancement help improving precision on the estimated position

Використання мікроконтролера Arduino для розпізнавання ключових слів

Використання нейронних мереж для розпізнавання інформації, зокрема голосу, розширює функціональні можливості вбудованих систем на мікроконтролерах. Але необхідно враховувати обмеження ресурсів мікроконтролера. Мета роботи – проаналізувати вплив параметрів обробки голосу та архітектури нейронної мережі на ступінь використання ресурсів мікроконтролера. Для цього створюється база даних зразків ключового слова, зразків інших слів і голосів, зразків шумів, оцінюється ймовірність розпізнавання ключового слова серед інших слів і шумів, залежність обсягу використовуваної пам'яті від мікроконтролера та встановлено час прийняття рішення від кількості коефіцієнтів MFC, а також встановлено залежність обсягу використаної пам’яті мікроконтролера та часу прийняття рішення від типу згорткової нейронної мережі. Під час експерименту використовувалася плата Arduino Nano 33 BLE Sense. Модель нейронної мережі була побудована та навчалась на програмній платформі Edge Impulse. Для проведення експерименту було створено три групи даних з назвами «hello», «невідомо», «шум». Група «hello» містить 94 приклади слова «hello» англійською мовою, вимовленого жіночим голосом. Група «невідомі» містить 167 прикладів інших слів, які вимовляються як жіночими, так і чоловічими голосами. Група «шум» містить 166 зразків шуму і випадкових звуків. Згідно з рекомендацією Edge Impulse, 80% зразків з кожної з груп даних використовувалися для навчання моделі нейронної мережі, а 20% зразків використовувалися для тестування. Аналіз результатів показує, що зі збільшенням кількості коефіцієнтів MFC і, відповідно, точності розпізнавання ключових слів, обсяг програмної пам’яті, зайнятої кодом, збільшується на 480 байт (менше 1%). Для мікроконтролера nRF52840 це не є значним збільшенням. Обсяг використовуваної оперативної пам'яті під час експерименту не змінився. Хоча час розрахунку точності визначення кодового слова збільшився лише на 14 мс (менше 5%) із збільшенням кількості коефіцієнтів MFC, процедура розрахунку досить тривала (приблизно 0,3 с) у порівнянні з довжиною звукової вибірки. 1 с. Це може бути певним обмеженням при обробці звукового сигналу 32-розрядними мікроконтролерами. Для аналізу фраз або речень необхідно використовувати більш потужні мікроконтролери або мікропроцесори. За результатами експериментальних досліджень можна стверджувати, що обчислювальних ресурсів 32-розрядних мікроконтролерів цілком достатньо для розпізнавання голосових команд з можливістю попередньої цифрової обробки звукового сигналу, зокрема використання низькочастотних кепстральних коефіцієнтів. Вибір числа коефіцієнтів суттєво не впливає на обсяг використовуваної FLASH і RAM пам'яті мікроконтролера nRF52840. Результати порівняння показують перевагу 2D мережі в точності визначення ключового слова як для 12, так і для 13 коефіцієнтів MFC. Використання одновимірної згорткової нейронної мережі для розпізнавання зразків голосу в проведеному експерименті забезпечує економію пам’яті приблизно на 5%. Якість розпізнавання ключового слова з числом коефіцієнтів MFC 12 становить приблизно 0,7. Для 17 коефіцієнтів MFC якість розпізнавання становить уже 0,97. Обсяг використовуваної оперативної пам'яті у випадку 2D мережі трохи зменшився. Час обробки вибірки голосу для обох типів мереж практично однаковий. Таким чином, одновимірні згорткові нейронні мережі мають певні переваги в додатках мікроконтролерів для обробки та розпізнавання голосу. Обмеженням розпізнавання голосу на мікроконтролері є досить великий час обробки звукового відліку (приблизно 0,3 с) при тривалості самого відліку 1 с, це можна пояснити досить низькою тактовою частотою 64 МГц. Збільшення тактової частоти зменшить час обчислення.The functional capabilities of embedded systems using microcontrollers are increased by the use of neural networks for information recognition, particularly speech recognition. However, it is important to consider the microcontroller's resource constraints. The goal of the work is to examine how the architecture of neural networks and voice processing parameters affect how much microcontroller resource is used. To achieve this, a database of samples of the keyword, samples of other words and voices, and samples of noise is created. The likelihood of recognizing the keyword among other words and noises is then assessed, and relationships between the amount of memory used by the microcontroller and the decision-making time on the number of MFC coefficients are established. The Arduino Nano 33 BLE Sense development board was employed throughout the experiment. The Edge Impulse software platform was used to create and train the neural network model. Three groups of data with the designations "hello," "unknown," and "noise" were constructed in order to carry out the experiment. There are 94 instances of the English word "hello" pronounced by a female voice in the "hello" group. There are 167 instances of additional words in the "unknown" group that are pronounced by both male and female voices. There are 166 samples of noise and random sounds in the "noise" group. 80% of the samples from each of the data groups were used to train the neural network model, and 20% of the samples from each data group were utilized for testing, as suggested by Edge Impulse. Analysis of the results shows that with an increase in the number of MFC coefficients and, accordingly, the accuracy of keyword recognition, the amount of program memory occupied by the code increases by 480 bytes (less than 1%). For the nRF52840 microcontroller, this is not a significant increase. The amount of RAM used during the experiment did not change. Although the calculation time of the accuracy of the code word definition increased by only 14 ms (less than 5%) with the increase in the number of MFC coefficients, the calculation procedure is quite long (approximately 0.3 s) compared to the sound sample length of 1 s. This can be a certain limitation when processing a sound signal with 32-bit microcontrollers. To analyze phrases or sentences, it is necessary to use more powerful microcontrollers or microprocessors. Based on the findings of experimental research, it can be concluded that 32-bit microcontrollers' computational capabilities are more than adequate for voice command recognition with the option of pre-digital sound signal processing, particularly the usage of low-frequency cepstral coefficients. The quantity of FLASH and RAM memory used by the nRF52840 microcontroller is unaffected by the choice of the coefficients' number. The comparison findings demonstrate the 2D network's superiority in terms of keyword definition precision for both 12 and 13 MFC coefficients. A one-dimensional convolutional neural network is used in the experiment to recognize voice samples, which results in a memory savings of about 5%. The effectiveness of keyword recognition with 12 MFC coefficients. When using 12 MFC coefficients, the quality of keyword recognition is roughly 0.7. The recognition quality for 17 MFC coefficients is already 0.97. In the case of the 2D network, less RAM is now being utilized. Both types of networks take essentially the same amount of time to process voice samples. As a result, 1D convolutional neural networks have some advantages in voice processing and recognition applications for microcontrollers. Voice recognition on the microcontroller is limited by the sufficiently low clock frequency of 64 MHz, which accounts for the sufficiently long processing time of the sound sample (about 0.3 s) with the sample duration itself being 1 s. The calculation time will be shortened by raising the clock frequency

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs