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Sensor calibration for dissolved gas is a critical and necessary step to ensure consistent sensor response over time and under different conditions. In this work, we describe a 3D printed device that facilitates the calibration of gas sensors. The device effectively maintained gas concentration in the solution for at least 2 h. Analytical curves were constructed in the pH range of 2 to 12, yielding determination coefficients surpassing 0.98. The chosen method proved suitable for the calibration of the sensors. Furthermore, using accurate and reliable reference sensors contributed to obtaining precise and trustworthy measurements.

This article discusses the challenges of scaling up photocatalytic advanced oxidation processes (AOPs) for the treatment of wastewater. While AOPs have shown promise in laboratory settings, their large-scale implementation has been limited due to the lack of control and automation strategies. The article explores the use of intelligent control systems, such as neural networks and fuzzy logic control, to optimize AOPs. The authors propose a fuzzy control system that uses pH as a control variable to regulate the doses of reagents in the AOP. The study demonstrates the effectiveness of the control algorithm in achieving maximum nutrient content in the treated water. The article emphasizes the importance of acquiring deep knowledge about the plant and using Programmable Logic Controllers for scaling AOPs to industrial environments. [Extracted from the article]

Our project focused on developing a waste sorting machine for metal, plastic, and glass for educational purposes. We divided it into several parts, including waste types, sorting methods, and recycling processes. All processes were done highlighting the use of the Arduino Uno controller. Additionally, we outlined the tools required for an automated sorting system and go beyond simulations by creating a prototype. Our goal is to establish a waste sorting machine that promotes recycling and environmental protection, providing both financial returns and sustainability by diverting waste from burial or incineration, thus mitigating pollution.

This paper presents a design paradigm to implement convolutional neural networks (CNNs) on low-power commercial microcontrollers for the detection of landing pads in small-size drone applications. A neural architecture search (NAS) strategy generates and selects CNN architectures automatically; candidate networks are compared in terms of their computing costs and representation capabilities. The proposed NAS procedure adopts a teacher-student learning paradigm, in which the ‘student’ network should mimic the ‘teacher’s’ intermediate representation. The associate selection strategy aims to attain an efficient feature representation that can take into account the peculiarities of the problem at hand. This approach allowed the generation of tiny networks capable of real-time execution on commercial micro-controllers (STM32 family). Experimental results confirmed that the resulting architectures could trade off generalization capabilities and computing costs effectively, and outperformed state-of-the-art solutions for landing-pad detection in small-size drones.

Motivated by the critical need for securing bio-signal transmissions in resource-constrained Internet of Medical Things (IoMT) devices, this paper proposes a novel lightweight cryptosystem based on a newly developed chaotic map called the Logistic-Coupled Memristor (LCM) map. This map is designed by coupling a discrete memristor with the well-known chaotic logistic map. The LCM exhibits a high degree of sensitivity to even slight changes in its ten control parameters, a crucial property for secure communication. We validate the chaotic behavior of the LCM using various established methods (Lyapunov exponents, bifurcation diagrams, parameter sensitivity analysis, and NIST randomness tests). Furthermore, we demonstrate its implementation on a low-cost microcontroller with limited resources. Building upon the LCM’s properties, we propose a novel lightweight cryptosystem for securing the wireless transmission of bio-signals. This cryptosystem is also implemented on a low-cost embedded system, showcasing its potential for real-world applications. Extensive simulations and comparisons confirm that the LCM map retains its chaotic behavior even when implemented on resource-constrained microcontrollers. Additionally, the implemented cryptosystem achieves a high level of security with lower cost in comparison to existing solutions.

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.

Copyright of Elektronik Industrie is the property of Hüthig GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Elektronik Industrie is the property of Hüthig GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Elektronik Industrie is the property of Hüthig GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The widespread availability and diversity of open-source microcontrollers paired with off-the-shelf electronics and 3D printed technology has led to the creation of a wide range of low-cost scientific instruments, including microscopes, spectrometers, sensors, data loggers, and other tools that can be used for research, education, and experimentation. These devices can be used to explore a wide range of scientific topics, from biology and chemistry to physics and engineering. In this study, we designed and built a multifunction fluorescent open source in vivo/in vitro imaging system (openIVIS) system that integrates a Raspberry Pi with commercial cameras and LEDs with 3D printed structures combined with an acrylic housing. Our openIVIS provides three excitation wavelengths of 460 nm, 520 nm, and 630 nm integrated with Python control software to enable fluorescent measurements across the full visible light spectrum. To demonstrate the potential applications of our system, we tested its performance against a diverse set of experiments including laboratory assays (measuring fluorescent dyes, using optical nanosensors, and DNA gel electrophoresis) to potentially fieldable applications (plant and mineral imaging). We also tested the potential use for a high school biology environment by imaging small animals and tracking their development over the course of ten days. Our system demonstrated its ability to measure a wide dynamic range fluorescent response from millimolar to picomolar concentrations in the same sample while measuring responses across visible wavelengths. These results demonstrate the power and flexibility of open-source hardware and software and how it can be integrated with customizable manufacturing to create low-cost scientific instruments with a wide range of applications. Our study provides a promising model for the development of low-cost instruments that can be used in both research and education. [ABSTRACT FROM AUTHOR]

The paper presents an adaptation of the microinverter platform from Texas Instruments to incorporate a battery energy storage system (BESS) alongside the development of the BESS system itself. Initially designed for unidirectional power flow between PV panels and an electric grid, the platform required modifications to accommodate bidirectional energy transfer for BESS integration. These modifications encompass software adjustments and hardware enhancements, which are all detailed within the paper. The electrical configuration includes selecting and deploying components such as DCDC power converters, microcontrollers, measured signals, and actuating signals to facilitate battery connection to the platform's DC bus. Furthermore, a supervisory control and data acquisition (SCADA) system is devised for supervisory control and monitoring, with its implementation outlined. Control software tailored for the chosen microcontroller of the DCDC converters is described in terms of structure and functionality. A hardware-in-the-loop (HIL) methodology is employed to validate the proposed modifications and microgrid configuration. Utilizing the real-time simulator OPAL-RT, the paper presents experimental results and their analysis within the considered microgrid environment. [ABSTRACT FROM AUTHOR]

In recent years, technological advancement characterized by heightened connectivity and digitization has revolutionized our interaction with the world. This evolution has been exemplified by the emergence of the Internet of Things, which has introduced a new era of digitization and automation, primarily propelled by devices based on the ESP32 microcontroller. This study seeks to comprehensively evaluate and characterize a fully functional sensor network built upon the ESP32 microcontroller. The evaluation covers three key aspects: network performance, the ESP32's built-in ADC peripheral, and the data acquisition system. The ESP32 device has demonstrated its capability to consistently sample sensors at a high rate with remarkable stability. The ESP32's built-in ADC exhibited undesirable behavior for analog devices, attributed to noise and non-linearity. As a result, in applications requiring precision with analog sensors, the utilization of a cost-effective external ADC, as employed in this study, is recommended. Finally, during the network analysis of the sensor network, no issues were identified. The data acquisition system exhibited no bottlenecks, and packet losses remained minimal, regardless of the sampling period. [ABSTRACT FROM AUTHOR]

Eye stimulation research plays a critical role in advancing our understanding of visual processing and developing new therapies for visual impairments. Despite its importance, researchers and clinicians still face challenges with the availability of cost-effective, precise, and versatile tools for conducting these studies. Therefore, this study introduces a high-resolution, compact, and budget-friendly multi-wavelength LED light source tailored for precise and versatile eye stimulation, addressing the aforementioned needs in medical research and visual science. Accommodating standard 3 mm or 5 mm package LEDs, the system boasts broad compatibility, while its integration with any microcontroller capable of PWM generation and supporting SPI and UART communication ensures adaptability across diverse applications. Operating at high resolution (18 bits or more) with great linearity, the LED light source offers nuanced control for sophisticated eye stimulation protocols. The simple 3D printable optical design allows the coupling of up to seven different wavelengths while ensuring the cost-effectiveness of the device. The system's output has been designed to be fiber-coupled with standard SMA connectors to be compatible with most solutions. The proposed implementation significantly undercuts the cost of commercially available solutions, providing a viable, budget-friendly option for advancing eye stimulation research. [ABSTRACT FROM AUTHOR]

This thesis introduces a nondestructive inspection and weight grading device for chicken wings to replace the traditional manual grading operation. A two-sided quality nondestructive inspection model of chicken wings based on the YOLO v7-tiny target detection algorithm is designed and deployed in a Jetson Xavier NX embedded platform. An STM32 microcontroller is used as the main control platform, and a wing turning device adapting to the conveyor belt speed, dynamic weighing, and a high-efficiency intelligent grading unit are developed, and the prototype is optimized and verified in experiments. Experiments show that the device can grade four chicken wings per second, with a comprehensive accuracy rate of 98.4%, which is better than the traditional grading methods in terms of efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Conventional data acquisition systems face challenges in achieving high acquisition speeds and rapid storage of large data volumes using microcontrollers. In contrast, field-programmable gate arrays (FPGAs) offer numerous advantages, including high clock frequencies, minimal internal delays, fast operational speeds, abundant internal RAM resources, and simplified control of complex peripheral circuits. This study presents the design of an FPGA-based multimedia remote monitoring system for information technology server rooms. The proposed system utilizes an FPGA as the primary controller and incorporates environmental sensors, electrical energy sensors, carbon monoxide sensors, smoke sensors, and A/D converter modules to monitor multiple locations within the server room. Simultaneously, the FPGA transmits the collected data from each monitoring point via a serial port to an LCD serial screen for display. An alarm is triggered if any environmental anomalies are detected, indicating abnormal statuses. Additionally, the system employs the fast Fourier transform algorithm and butterfly operations to derive voltage and current AC quantities, while utilizing relative temperature differences to identify equipment faults within the server room. The system is evaluated in terms of functionality, user-friendliness, and reliability. Experimental results demonstrate that all performance measures align with expectations, fulfilling the initial design objectives and highlighting the potential applicability and relevance of FPGA technology in the monitoring field. In addition, a comparison between FPGA and traditional microcontroller systems is performed, showcasing the superior processing speed and performance of the FPGA-based system. This comparative analysis further validates the advantages of using FPGA technology in high-speed data acquisition and monitoring applications. [ABSTRACT FROM AUTHOR]

High-precision waveform identification and measurement are effective for waveform detection and evaluation in signal processing. The accuracy of waveform identification, precision of measurement, and speed of response are important indicators of waveform measurement instruments. To detect the waveform accurately, a hold and attenuation circuit divided into two is designed, and the STM32F4 microcontroller is used to accurately capture and perform spectrum analysis using a high-precision analog-to-digital converter based on fast Fourier transform technology to identify key parameters, such as waveform type, frequency, peak-to-peak value, and duty cycle. To improve the recognition accuracy and response speed, technical solutions, such as high-frequency sampling and over-zero detection, are used to improve the system efficiency. Algorithm simulation, circuit simulation, and physical testing show that the high-precision waveform synchronization recognition circuit and algorithm can accurately recognize various essential waveforms in the voltage and frequency ranges of 50 mV ≤ VPP ≤ 10 V and 1 Hz ≤ f ≤ 50 kHz, respectively, and simultaneously measure important parameters, such as frequency, peak-to-peak value, and duty cycle with an accuracy within ±1%. Intelligent linkage, no intermediate parameter setting, and a response speed of approximately 0.3 s make it suitable for such applications as fast and high-precision waveform intelligent detection and display. The method is highly integrated, simple to operate, cost-effective, and practical. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a collaborative learning paradigm where multiple clients are used to build the model without sharing data and preserving privacy. An FL-based linear regression model is designed to predict the length of stay for patients at hospitals using the low-power Arduino Nano 33 BLE Sense microcontroller unit (MCU). FL uses a distributed learning technique that allows model building from decentralized data sources. The Arduino Nano 33 BLE Sense is a compact and energy-efficient MCU providing an ideal platform for implementing FL in resource-constrained environments. FL algorithms aggregate model parameters from multiple Arduino clients and collectively train and build a predictive model to estimate the length of stay at the hospital by patients. Experiments were conducted to understand the performance of FL on clients with data of equal and varying sizes and heterogeneous data from multiple sources. The performance of the algorithm is evaluated based on Mean Absolute Error (MAE), Percentage Decrease in Training error (PDTE), and Percentage Difference with Optimal Testing (PDOT) value. Experimental results show that the number of local epochs and FL rounds affects the convergence of clients to the optimal value. The experimental results demonstrate the applicability of FL on low-power MCUs, preserving privacy which is a core requirement for healthcare solutions. [ABSTRACT FROM AUTHOR]

Violent crime cases which include, robbery, rape, and homicide are terribly on the rise, and the role of security in combating this menace cannot be overemphasized. This research presents a security device that aims at fighting violent crimes using voice recognition technology. The work also tends to solve the issue of network downtime when the user is out of reach for help in time of attack. In this work, a voice processing unit which comprises the condenser microphone, an amplifier, a shift register, and a timer was designed. The processing unit circuit was incorporated into microcontrollers which create Human-Device interaction and the GSM communication unit which is made up of two GSM modules. The two microcontrollers used in the design are PIC18F4520 and PIC16F873A. The microcontrollers were programmed with C++ using the MPLAB IDE software and the circuit simulation was done using Proteus Design Suite version 8. The result shows that the appropriate authority receives SMS whenever the pre-recorded code is mentioned. The result also shows that during network downtime, the second GSM module sends an SMS to the appropriate authority. Evaluating the performance of this work, it was observed that the device works best in a calm area compared to a noisy area. This work is designed to work in domestic areas like homes, offices, malls, and mainly areas free from so much noise. Therefore, this work has successfully reduced the crime rate in emergencies. [ABSTRACT FROM AUTHOR]

Small animal studies in biomedical research often require anesthesia to reduce pain or stress experienced by research animals and to minimize motion artifact during imaging or other measurements. Anesthetized animals must be closely monitored for the safety of the animals and to prevent unintended effects of altered physiology on experimental outcomes. Many currently available monitoring devices are expensive, invasive, or interfere with experimental design. Here, we present MousePZT, a low-cost device based on a simple piezoelectric sensor, with a custom circuit and computer software that allows for measurements of both respiratory rate and heart rate in a non-invasive, minimal contact manner. We find the accuracy of the MousePZT device in measuring respiratory and heart rate matches those of commercial systems. Using the widely-used gas isoflurane and injectable ketamine/xylazine combination, we also demonstrate that changes in respiratory rate are more easily detected and can precede changes in heart rate associated with variations in anesthetic depth. Additional circuitry on the device outputs a respiration-locked trigger signal for respiratory-gating of imaging or other data acquisition and has high sensitivity and specificity for detecting respiratory cycles. We provide detailed instruction documents and all necessary microcontroller and computer software, enabling straightforward construction and utilization of this device. [ABSTRACT FROM AUTHOR]

Photosynthetically Active Radiation (PAR) is an important parameter in the plant photosynthesis process, which can relate to plant growth, crop water use, and leaf gas exchange. Previously, many researchers utilized commercially available sensors to monitor PAR. The high cost of the commercially available PAR sensors has limited researchers, agricultural professionals, and farmers to use and expand PAR monitoring in agricultural lands. Thus, this paper focuses on designing an affordable Internet of Things (IoT)-based PAR sensor monitoring system including 3D-printed enclosures (waterproof) for the sensors, performance evaluation of multiple light sensors, solar powering configuration, cloud setup, and cost analysis. Three sensors, including VTB8440BH photodiode, SI 1145, and LI-190R sensors, were evaluated. The 3D-printed waterproof enclosures were designed for the photodiode and SI 1145. Particle Boron was used for recording and sending the sensor data to the IoT webserver. Both the photodiode and SI 1145 were compared to LI-190R, which is the industry standard. In the calibration process, the R2 values of the photodiode and SI 1145 with LI-190R were 0.609 and 0.961, respectively. Field validation data shows that SI 1145 had a strong correlation with LI-190R. In addition, the performance evaluation data shows the photodiode had a weaker correlation with LI-190R than SI 1145. In conclusion, the study successfully developed and designed affordable and reliable IoT-based PAR sensor monitoring systems, including a 3D-printed housing, hardware, programming, and IoT website. SI 1145 with a glass filter is an alternative sensor to monitor PAR at a low cost and has the advantage of being connected to IoT microcontrollers. [ABSTRACT FROM AUTHOR]

This paper describes the design and optimization of a smart algorithm based on artificial intelligence to increase the accuracy of an ocean water current meter. The main purpose of water current meters is to obtain the fundamental frequency of the ocean waves and currents. The limiting factor in those underwater applications is power consumption and that is the reason to use only ultra-low power microcontrollers. On the other hand, nowadays extraction algorithms assume that the processed signal is defined in a fixed bandwidth. In our approach, belonging to the edge computing research area, we use a deep neural network to determine the narrow bandwidth for filtering the fundamental frequency of the ocean waves and currents on board instruments. The proposed solution is implemented on an 8 MHz ARM Cortex-M0+ microcontroller without a floating point unit requiring only 9.54 ms in the worst case based on a deep neural network solution. Compared to a greedy algorithm in terms of computational effort, our worst-case approach is 1.81 times faster than a fast Fourier transform with a length of 32 samples. The proposed solution is 2.33 times better when an artificial neural network approach is adopted. [ABSTRACT FROM AUTHOR]

The derivation of pseudorandom number generation (PRNG) from the Josephson junction (JJ) stimulated by the Wien bridge oscillator (JJSWBO) and its microcontroller validation is given in this paper. Via the numerical investigations of the JJSWBO, the encyclopedic dynamical maps in different coordinate spaces constituting the system's parameters explicitly elaborate the global behaviour of the system presenting the greatest Lyapunov exponents (GLE). Chaotic behaviours were captured for GLE greater than zero and periodic behaviours for GLE less than zero. Further, bifurcation characteristics expose monostable and bistable periodic oscillations, bistable periodic doubling route to bistable chaos, interceptions of bistable regular behaviours and bistable chaotic presentations, coexisting attractors, monostable chaotic dynamics and intermittency phenomenon. The microcontroller validation (MCV) of the JJSWBO is presented to validate the numerical simulation results. From the chaotic equations describing JJSWBO, a PRNG with a linear feedback shift register (LFSR) as a post-processing unit is designed. The randomness of the generated binary data from the proposed JJSWBO-based PRNG is successfully tested by using the NIST 800-22 test suite. This result helps to confirm the suitability of the JJSWBO for encryption schemes and other chaos-based applications. [ABSTRACT FROM AUTHOR]

A composite ferroelectric characterization test system constructed using a modified constant current method (CCM) and a modified virtual ground method (VGM) has been successfully designed and implemented. By sending instructions to the microcontroller through software, the system's test mode can be easily changed by arranging the switching status of six switching elements. When validating the system, a dual-channel precision source/measure unit B2912B was used to verify this design. There is also parasitic capacitance that cannot be ignored in this commercial machine. This parasitic capacitance affects the appearance of the entire hysteresis curve. However, the parasitic capacitance values also differ in various test current ranges. In addition, to confirm the data credibility of this composite ferroelectric test system, Keysight B1530A and Radiant Premier II were used to conduct cross-verification between different systems. The results obtained between different systems show good consistency. Furthermore, reproducible and recoverable imprint phenomena were found in this composite system during interactive validation using VGM and CCM methods. After designing different voltage profiles for verification, it was found that the root cause of this imprint phenomenon was the difference between the final polarization state of the previous test and the pre-initialized polarization state. This imprint phenomenon exists in traditional Pb(Zr, Ti)O3 (PZT) ferroelectric capacitors and Hf0.5Zr0.5O2-based ferroelectric capacitors. Fortunately, this imprint phenomenon is reversible. Moreover, this imprint phenomenon disappears through the design of the time-varying voltage profile on the ferroelectric capacitor of the CCM method. [ABSTRACT FROM AUTHOR]

Ultra-low noise is a critical component in the design of high-precision sensor front-ends. We introduced differential phase-sensitive detection (d-PSD) to mitigate both multiplicative and additive noise in optical sensors, aiming for an enhanced performance and cost-effectiveness. The d-PSD combines a capacitive transimpedance amplifier (C-TIA), a delta–sigma analog-to-digital converter (ΔΣ-ADC), and a software-based lock-in amplifier (s-LIA). The first two components utilize the DDC112 (a dual current input 20-bit ADC) for a minimal analog channel length, thus reducing noise efficiently, while the latter employs a cost-effective 32-bit microcontroller unit (MCU), the HC32F460. This approach was successfully implemented as the front-end for a smart optical sensor. Testing indicated that the sensor achieved an equivalent current noise level of 0.6 nA/√Hz, primarily attributed to the light source driver rather than the sensor's front-end circuit. The sensor exhibited an exceptional performance, with a 3σ measurement precision of 5.4 × 10−4 over a 1-second integration time and a dynamic range of 100 dB, leveraging the proposed method and design. Furthermore, the front-end of the sensor boasts a compact size, low power consumption, and affordability, making it an ideal, versatile solution for ultra-high precision, smart optical sensors. [ABSTRACT FROM AUTHOR]

Having as a main objective the exploration of power efficiency of microcontrollers running machine learning models, this manuscript contrasts the performance of two types of state-of-the-art microcontrollers, namely ESP32 with an LX6 core and ESP32-S3 with an LX7 core, focusing on the impact of process acceleration technologies like cache memory and vectoring. The research employs experimental methods, where identical machine learning models are run on both microcontrollers under varying conditions, with particular attention to cache optimization and vector instruction utilization. Results indicate a notable difference in power efficiency between the two microcontrollers, directly linked to their respective process acceleration capabilities. The study concludes that while both microcontrollers show efficacy in running machine learning models, ESP32-S3 with an LX7 core demonstrates superior power efficiency, attributable to its advanced vector instruction set and optimized cache memory usage. These findings provide valuable insights for the design of power-efficient embedded systems supporting machine learning for a variety of applications, including IoT and wearable devices, ambient intelligence, and edge computing and pave the way for future research in optimizing machine learning models for low-power, embedded environments. [ABSTRACT FROM AUTHOR]

This manuscript provides low-cost smart irrigation for agricultural land using IoT. For irrigation purposes, using a farmland manual system is time-consuming and risky. Agriculture is the most important occupation in India because villagers are primarily dependent on agriculture. We offer a solution to overcome traditional shortcomings. Farmland houses are Embedded by sensors through the node's microcontroller MCU. The statistics are handled with the microcontroller and sent to the cloud server. It acts as a bridge to send statistics through cell phones. The valve is activated using information acquired from signals sent to a cell phone. This increases performance and saves a lot of water. The hardware configuration becomes advanced and is examined within the farmland. The advantage is that the yields get enhanced for farmers. [ABSTRACT FROM AUTHOR]

In recent years, several deep learning (DL) approaches, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been utilized to carry out side-channel attacks. These attacks exploit vulnerabilities in cryptographic systems, especially in resource-constrained devices like microcontrollers. By analyzing side-channel measures using DL models, attackers can extract information leaked through physical means, including power consumption, time, heat, sound, and electromagnetic radiation, to uncover the secret keys of cryptographic algorithms. Most of these attacks rely on DL models to extract secrets from cryptographic algorithm implementations. In this article, we present a DL-based power analysis attack (PAA) to extract the advanced encryption standard (AES) keys from the ATmega328P microcontroller. Our approach employs a CNN trained on power consumption traces collected during AES-128 execution on the microcontroller. We explain how DL-based PAA exploit the leakage of information from cryptographic devices to recover the secret keys, and we provide a detailed performance comparison of our method to other methods that target cryptographic devices. Our approach stands out not only in its methodology, but also in its robustness and efficiency in revealing secret keys. In comparison with other state-of-the-art methods, our DL-based PAA, implemented on an ASIC with a 130 nm technology, requires only about 1200 traces to successfully extract all 16 bytes of the AES-128 key. This performance outperforms the next-best method evaluated in this paper by a margin of 100 power consumption traces. [ABSTRACT FROM AUTHOR]

The flood detection tool uses the HC-SR04 sensor made specifically to detect the water level in the river. This tool is a series of simple tools that utilize the esp32 microcontroller and media monitoring in real time via a smartphone. The flood detection tool using the HC-SR04 sensor consists of a tool frame made of iron, the HC-SR04 sensor and a panel box. The main power source for this tool is a 12 V 5 Ah battery that comes from a 50wp solar panel. This tool is equipped with various electronic components including an LCD, HC-SR04 sensor, relay and stepdown module. The designed tool will be placed on the riverbank, the HC-SR04 sensor acts as a real time measure of water level. When the sensor has detected a predetermined water level, the sensor will send data to the microcontroller. The microcontroller will process the data and send it to the virtuino application which will display it virtually on the smartphone. Along with this, the data will also be displayed on the LCD. [ABSTRACT FROM AUTHOR]

The accuracy level is important in shooting activities and depends on many factors, such as hand tremors as body vibration and shooting position. Achieving high accuracy in different shooters is challenging, especially in the case of different shooting positions. However, there is a lack of information about the influence of shooting positions and experiences on a shooter's body vibration and accuracy levels. Thus, this study aims to develop a portable and low-cost hand tremor measurement device (as a function of body vibration) to identify the influence of hand movement on shooting accuracy. For this purpose, low-cost accelerometer sensors and a microcontroller were used as the measurement kit. Three different shooting positions (squatting, standing, and prone) were analyzed. The shooters were classified into novice and expert groups. Each group had five participants with standard fire guns and accelerometer kits. These participants were asked to shoot the target to get their best accuracy. Besides, the hand tremor level data from the self-developed kit were recorded to investigate the hand tremors. The results show that the novice participants have more hand tremors in all shooting positions. There are significant differences between the squatting, standing, and prone positions in hand tremors for novice and expert participants. In the expert group, the prone and squatting positions have the least vibration level, indicated by the least acceleration (0.01 - 0.04 m/s2 for the expert group and 0.02 - 0.11 m/s2 for the novice group). The best accuracy for all positions is also obtained from expert shooters. It can be concluded that different shooting positions are related to the body vibrations. The expert shooters have a lower body vibration than the novice participants. The hand tremor levels may influence the accuracy level since different shooting positions and experiences have different vibration and accuracy levels. [ABSTRACT FROM AUTHOR]

Nano-composite piezo-responsive foam (NCPF) is an inexpensive foam that can be used to measure a static load while still providing a comfortable interface. The purpose of this study was to create a modularized foam-based pressure measurement system. A measurement system was developed that uses an interdigitated electrode applied to the NCPF. Applied pressure changes the impedance of the NCPF, which, in turn, is converted into a voltage using a voltage divider. A modular measurement system is described that uses an ATtiny 1627 microcontroller to measure the pressure at nine electrodes. The nine electrode modules are controlled by an ESP32 microcontroller that aggregates the data and wirelessly transmits the data to a tablet. The modular system was demonstrated with 1008 individual electrodes. The characterization of the electrode combined with the NCPF is presented, along with optimization of the electrode geometry. [ABSTRACT FROM AUTHOR]

At present greenhouse farming has become more popular in contrast to traditional farming because of its adjustment capability of the environmental parameters such as temperature, humidity, light intensity, and soil moisture according to the requirements of the crops. Continuous monitoring and controlling facilities of the greenhouse system allow the farmers a good maintenance system with good quality and high yield of the crops. In this paper, an Arduino microcontroller was used in a greenhouse system for an automatic monitoring system for cultivation incorporating various sensors such as a temperature-humidity sensor, and soil moisture sensor to collect parameters for monitoring the environment of the greenhouse. The collected data were used to control the temperature using cooling fans which facilitated the greenhouse controlling the environment. For storage and processing the data the controller code was generated in the Arduino programming language, and finally inserted into the Arduino UNO R3 microcontroller. A solar power system with a rechargeable battery was installed as a source of energy to ensure continuous power supply to the greenhouse system. Implementation of a greenhouse with a microclimatic parameter monitoring and controlling system will result in mitigating land and labor requirement problems for small-scale farmers, and gardeners as well as supplying suitable data for agricultural researchers. [ABSTRACT FROM AUTHOR]

In this work, we present a systematic study of Side-Channel Attacks (SCA) and Fault Injection Attacks (FIA) on structured lattice-based schemes, with main focus on Kyber Key Encapsulation Mechanism (KEM) and Dilithium signature scheme, which are leading candidates in the NIST standardization process for Post-Quantum Cryptography (PQC). Through our study, we attempt to understand the underlying similarities and differences between the existing attacks while classifying them into different categories. Given the wide variety of reported attacks, simultaneous protection against all the attacks requires to implement customized protections/countermeasures for both Kyber and Dilithium. We therefore present a range of customized countermeasures, capable of providing defenses/mitigations against existing SCA/FIA, and incorporate several SCA and FIA countermeasures within a single design of Kyber and Dilithium. Among the several countermeasures discussed in this work, we present novel countermeasures that offer simultaneous protection against several SCA- and FIA-based chosen-ciphertext attacks for Kyber KEM. We implement the presented countermeasures within two well-known public software libraries for PQC: (1) pqm4 library for the ARM Cortex-M4-based microcontroller and (2) liboqs library for the Raspberry Pi 3 Model B Plus based on the ARM Cortex-A53 processor. Our performance evaluation reveals that the presented custom countermeasures incur reasonable performance overheads on both the evaluated embedded platforms. We therefore believe our work argues for usage of custom countermeasures within real-world implementations of lattice-based schemes, either in a standalone manner or as reinforcements to generic countermeasures such as masking. [ABSTRACT FROM AUTHOR]

We present the design, implementation, and results of an algorithm for respiratory rate (RR) estimation using respiratory-induced frequency, intensity, and amplitude variation calculated from the infrared (IR) channel of the SEN-15219 board for photoplethysmography (PPG) acquisition. First, the algorithm was developed in Python (on a PC) using synthetic signals and publicly available respiration and PPG data. We also include a graphical user interface to process data from sensors and display vital signs. Later, we ported the algorithm to an MSP432P401R microcontroller to complete our wearable prototype. Results are promissory and show that RR estimation can be performed on the selected platform with our proposed Fourier Product (FP) method, which results in a Mean Absolute Error of 4.1 using 16-s windows of IR-PPG signals. [ABSTRACT FROM AUTHOR]

In this progressive technological era where automation is the norm it is a drawback that most of the customers in supermarkets wait in long queues because of the barcode-billing process. We want to introduce, our IOT-based project which consists of RFID sensors, microcontroller, communication module, and a Web application for inventory management which has reduced power needs. The main objective for designing this prototype is to reduce human efforts by introducing a low cost, easily scalable system for assisting the customers in shopping. The proposed system can easily be implemented and tested at a commercial scale under the real scenario in the future. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) systems can efficiently forecast chaotic time series using the nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more-efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper, we experimentally validated a physical RC system that substitutes the effect of randomness that underpins the operation of the traditional RC algorithm for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the 'next-generation' improvement of the traditional RC algorithm. [ABSTRACT FROM AUTHOR]

An optimal energy harvesting system is required at the remote seismic node due to the constraints of photovoltaic cells and high cost of seismic installations. The conventional approach concentrates on upgrading the hardware and charging to sustain the power at the node. However, the DC-DC converter as electrical conditioning unit and the lead-acid battery as an energy storage medium lose electrical energy, usually characterized by energy leakage and a shorter lifecycle. In the first contribution, a developed algorithm is modelled around the curve-fitting equations and embedded in a common 8-bit microcontroller-based MPPT system. The MPPT algorithm controls the curve equations using polynomial regression and approximates the order of the equation to track optimal electrical power. This enables the prediction of instantaneous duty cycle levels across synchronous buck DC-DC converters for optimal energy conversion. The second contribution is the implementation of a system that involves a dedicated supercapacitor energy storage as a buffer and charge controller for the stability of the current flow through the remote seismic node. Most conventional MPPT predicting procedures initiated will result to expensive computational costs, especially for a relatively high-consuming power system. The contributions of this work allow relatively less computational yet Maximum Power Point Tracking (MPPT) for low-power remote seismic nodes. Therefore, a longer continuous runtime and sustainable electrical energy are achieved. In addition, the proposed approach was validated using Computer Controlled Photovoltaic System (CCPS). The curve fitting equation models linearized the non-linear characteristic relationship between the irradiance, temperature, and voltage levels with the selected photovoltaic module parameters. These models are implemented in C programming code as an algorithm, embedded in an 8-bit microcontroller, and deliver optimal duty cycle levels across the converter. Implying any low-cost microcontroller can be deployed due to simple computational processes. The performance of the proposed approach is evaluated with the CCPS with a deviation between 2.5 % and 5 % based on tabulated results and graphs. [ABSTRACT FROM AUTHOR]