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In this study, an algorithm based on BFZL(Boundary Fair until Zero Laxit) is proposed to solve the problem of reasonable scheduling of real time mixed task set formed by periodic task and sporadic task. Based on the IBF (Improved Boundary Fair) real-time mixed task algorithm, the relaxation parameter of LLF (Least Laxity First) algorithm is introduced to improve the priority of decision task. A heuristic algorithm based on relaxation and heuristic strategy is proposed to improve task assignment strategy. The experimental results show that the BFZL algorithm can satisfy the real time performance of the system and achieve the purpose of algorithm optimization. Through data comparative analysis, compared with the original algorithm, the proposed algorithm reduces the average response time of sporadic tasks by about 26%, reduces the context switch and migration by about 28% and 50%, respectively. Additionally, the algorithm also has advantages in scheduling overhead. [ABSTRACT FROM AUTHOR]

Diabetes is one of the most common life-threatening diseases in the world. The accurate amount, and on-time injection will directly affect the patient's health. The current standard of care for insulin-dependent diabetes management is paper based, so the patients provided with self-management support through health coaches which consumes time and effort. Smart insulin box as proposed meets the needs of the market by integrating electronic technology and IoT network functionality. The interactive box comprises a particular device with embedded sensors in each compartment. Two Infrared and one DHT11 sensors are used. IR sensors are used for detecting the absence or availability of the insulin pen inside the box. While, DHT11 sensor is used for measuring the internal temperature and humidity of the package. The interactive box not only transmits patient status messages to the cloud but also receives a reminder message to patient mobile phone presented by LCD screen. The system manages the insulin injection process and provides a remote monitoring system for doctors using two individual applications. Doctor application provides remote monitoring and controlling for insulin amount and injection time while patient application used to notify patients about injection time, status, and insulin stock status. The system is laboratory tested and evaluated by the authors. The low cost at 84 USD shows promising results. It has a potential impact on patient health outcomes as a smart, low cost, and interactive management system for the insulin injection process which can be integrated with the existing healthcare systems. [ABSTRACT FROM AUTHOR]

Permanent-magnet synchronous motors (PMSMs) have played a key role in recent years in both industrial and commercial applications. Despite their many significant advantages, such as high efficiency, very good dynamics, and high power density, these types of motors are prone to various types of faults. This article proposes a low-cost microcontroller-based system for PMSM stator winding condition monitoring and fault diagnosis. It meets the demand created by the use of more and more low-budget solutions in industrial and commercial applications. A printed circuit board (PCB) has been developed to measure PMSM stator phase currents, which are used as diagnostic signals. The key components of this PCB are LEM's LESR 6-NP current transducers. The acquisition and processing of diagnostic signals using a low-cost embedded system (NUCLEO-H7A3ZI-Q) with an ARM Cortex-M core is described in detail. A machine learning-driven KNN-based fault diagnostic algorithm is implemented to detect and classify incipient PMSM stator winding faults (interturn short-circuits). The effects of the severity of the fault and the motor operating conditions on the symptom extraction process are also investigated. The results of experimental tests conducted on a 2.5 kW PMSM confirmed the effectiveness of the developed system. [ABSTRACT FROM AUTHOR]

Vector normalization is an important process in several algorithms. It is used in classical physical calculations, mathematical techniques, and machine learning, which has witnessed significant advancements in recent years. Normalization and regularization ensure the stability of solutions and play an important role in algorithm convergence. Normalization typically refers to the division of elements by their norm. Division should not be used in algorithmic implementations because its computational cost is considerably higher than that of multiply–add operations. Based on this, there is a well-known method referred to as the fast inverse square root (FISR) algorithm in floating-point calculations (IEEE754). In deeper-level embedded systems that require fast responses or power efficiency, integer instead of real number arithmetic (floating-point number arithmetic) should be used to increase speed. Conversely, in deeper-level embedded systems that require fast responses or power efficiency, integer arithmetic should be used instead of real number arithmetic (floating-point number arithmetic) to increase speed. Therefore, embedded engineers encounter problems in instances in which they use integer arithmetic for implementation, but real number arithmetic is required to compute vectors and other higher-dimensional algebra. There is no conventional normalization algorithm similar to the FISR algorithm for integer arithmetic; however, the proposed pseudo-normalization achieves vector normalization within a restricted domain using only multiply–add operations and bit shifts. This allows for fast and robust operations, even for low-performance MCUs that do not have power-efficient FPUs. As an example, this study demonstrates the computation of the arctangent (Arctan2 function; atan2(y, x)) with high precision using only integer multiply–add operations. In this study, we proposed a method of vector normalization using only integer arithmetic for embedded systems and confirmed its effectiveness by simulation using Verilog. The research results can contribute to various fields such as signal processing of IMU sensor data, faster artificial intelligence training, and efficient rendering of computer graphics. [ABSTRACT FROM AUTHOR]

Since the invention of the microphone by Barina in 1876, there have been numerous applications of audio processing, such as phonographs, broadcasting stations, and public address systems, which merely capture and amplify sound and play it back. Nowadays, audio processing involves analysis and noise-filtering techniques. There are various methods for noise filtering, each employing unique algorithms, but they all require two or more microphones for signal processing and analysis. For instance, on mobile phones, two microphones located in different positions are utilized for active noise cancellation (one for primary audio capture and the other for capturing ambient noise). However, a drawback is that when the sound source is distant, it may lead to poor audio capture. To capture sound from distant sources, alternative methods, like blind signal separation and beamforming, are necessary. This paper proposes employing a beamforming algorithm with two microphones to enhance speech and implementing this algorithm on an embedded system. However, prior to beamforming, it is imperative to accurately detect the direction of the sound source to process and analyze the audio from that direction. [ABSTRACT FROM AUTHOR]

Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security. [ABSTRACT FROM AUTHOR]

This paper explores the determination of any load or load combination in a power system at any moment. This process requires measurements at the main electric utility service entry of a house, known as nonintrusive measurement. To accurately identify loads, total harmonic distortion, RMS, third harmonic currents, and power consumption are considered their fingerprints. Based on these fingerprints, an algorithm called the competitive decision process is developed and integrated into an embedded system. This algorithm has a two-level decision mechanism. In the first stage, the winner loads with the highest similarity scores from each feature are determined, and the loads with a similarity score higher than 90 move to the second stage to be evaluated. Loads that do not pass the first stage in all features are not considered for the next one. In the second stage, the scores from each feature of the loads passed to this stage are summed, and the load with the highest score is determined. It is experimentally validated that the method significantly detects correct load or load combinations for six residential appliances. Fifty-six type-tests are performed, and each type-test contains ten measurements. As a result, a total success rate of over 97 percent is obtained in all metrics. [ABSTRACT FROM AUTHOR]

Aiming to address the issues of low data execution efficiency and slow storage speed on traditional tourism cloud service platforms, a rural tourism management cloud service platform based on an embedded system is proposed. The embedded device components, such as the ARM processor, NOR Flash memory, and RTL network chip, are utilized to optimize the hardware structure of the cloud service platform. This optimization aims to enhance data processing and storage efficiency, as well as improve the reliability and durability of system operation. The unstructured feature grasping algorithm is employed to gather rural tourism resource data for target demand characterization. Associated algorithms are then utilized to develop data analysis, service recommendation, and tourism management modules, creating a comprehensive tourism cloud service platform. Additionally, the shortest path algorithm is used to enhance database storage speed, improve business docking efficiency, establish a big data monitoring center, comprehensively monitor changes in the scenic area and related industries, and enhance platform service efficiency. It has been proven through experiments that the latency of the tourism management cloud service platform, based on the embedded system, is reduced by almost 46.7% compared to the traditional approach, and the throughput is increased to 0.69 b/s. The practical application effect is positive, as it contributes to the development of tourism resources and the economic growth of rural areas. It also brings about economic and social benefits. [ABSTRACT FROM AUTHOR]

Identifying underperforming photovoltaic (PV) modules is crucial to ensure optimal energy production and financial returns, as well as preventing potential safety hazards in case of severe damage. To this aim, current–voltage (I-V) curve tracing can be employed as in situ monitoring technique for the early detection of faults. In this paper, we introduce a novel low-cost, microcontroller-based I-V tracer for the diagnosis of individual PV modules. The tool features a unique power conditioning circuit, facilitating accurate data acquisition under static conditions as well as the even distribution of the measured points along the I-V curve. A specific active disconnecting circuit enables in situ and on-line measurement without interrupting the string power generation. The designed prototype is used to characterize a set of PV modules under real operating conditions. The measured I-V curves exhibit expected trends, with the measured data closely matching theoretical values and an estimated mean relative error less than 3%. [ABSTRACT FROM AUTHOR]

AbstractBased on the adverse physiological and social consequences associated with sadness, recognizing this emotion is crucial to mitigate its negative impacts. To address this issue, an efficient system was proposed for recognizing different level of sadness using embedded technology. The system captured speech input through a microphone and then processed it on Raspberry Pi microcomputer. The key feature extraction technique used was Bark-Frequency Cepstral Coefficient (BFCC) method. After selecting a set of features, a thorough analysis of the computational algorithms was conducted, and dimensionality reduction method was applied to reduce computational costs, including noise. These features served as a pre-processing stage for Machine Learning (ML) model based on K-Nearest Neighbors (KNN) architecture. The results were then shown on a smartphone application through Bluetooth connectivity. Based on experimental results, the system showed an impressive improvement of 11.97 dB in the signal-to-noise ratio (SNR) compared to feature extraction using Mel-frequency Cepstral Coefficient (MFCC) method. The results also showed a favorable accuracy rate, implying the effectiveness of KNN with a k-value of 5 in accurately identifying different level of sadness. The system particularly excelled in correctly recognizing high and low level of sadness. Therefore, the effective categorization of sadness emotion level could be applied to the early diagnosis of mental disorders such as depression and anxiety, contributing to the reduction of the adverse impact of sadness. [ABSTRACT FROM AUTHOR]

System health monitoring (SHM) of a ball screw laboratory system using an embedded real-time platform based on Field-Programmable Gate Array (FPGA) technology was developed. The ball screw condition assessment algorithms based on machine learning approaches implemented on multiple platforms were compared and evaluated. Studies on electric power consumption during the processing of the proposed structure of a neural network, implementing SHM, were carried out for three hardware platforms: computer, Raspberry Pi 4B, and Kria KV260. It was found that the average electrical power consumed during calculations is the lowest for the Kria platform using the FPGA system. However, the best ratio of the average power consumption to the accuracy of the neural network was obtained for the Raspberry Pi 4B. The concept of an efficient and energy-saving hardware platform that enables monitoring and analysis of the operation of the selected dynamic system was proposed. It allows for easy integration of many software environments (e.g., MATLAB and Python) with the System-on-a-Chip (SoC) platform containing an FPGA and a CPU. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office 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.)

This article describes constructing an embedded system for a painting art and style presentation platform, achieving the automatic integration of digital painting art with traditional art design. The frontend components are designed using the Bootstrap framework, with Django as the web development framework and TensorFlow architecture integrated into the code. Furthermore, the Inception module and residual connections are introduced to optimize the visual geometry group (VGG) network for recognizing and analyzing image texture features. Compared to other models, experimental results indicate that the proposed model demonstrates a 2.6% increase in image style classification accuracy, reaching 87.34% and 95.33% in architectural and landscape image classification, respectively. The system’s operational outcomes reveal that the proposed platform alleviates the burden on the logical function modules of the system, enhances scalability, and promotes the automated fusion of digital painting art with traditional art design expression. [ABSTRACT FROM AUTHOR]

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to tamper with the branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of branch target buffer (BTB) updates every 80 clock cycles. If the number exceeds the set threshold, the BTB will be locked and prevent any process from tampering with the BTB entries, thereby resisting branch prediction analysis (BPA) attacks. Moreover, to prevent attackers from stealing the critical information of branches, the method designs the hybrid arbiter physical unclonable function (APUF) circuit to encrypt and decrypt the directions, addresses, and indexes of branches. This circuit combines the advantages of double APUF and Feed-Forward APUF, which can enhance the randomness of output response and resist machine learning attacks. If attackers still successfully tamper with the branches and disrupt the control flow integrity (CFI), this method detects tampering with the instruction codes, jump addresses, and jump directions in a timely manner through dynamic and static label comparison. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

In the field of wearable electronics, MXenes have emerged as promising two-dimensional (2D) materials, exhibiting exceptional properties such as metallic conductivity, water dispersibility, thermal stability, mechanical stability, and high optical transmittance. In this study, we present a unique flexible transparent conductive electrode (FTCE) composed of MXene, Ag nanowire (AgNW), ultraviolet resin (UV-resin), and polycarbonate (PC). Our fabrication process involves a roll-to-roll process and entirely solution-based methods including UV-resin dispensing, AgNW solution coating, and FTCE dipping in an MXene solution, providing a cost-effective manufacturing approach. Notably, compared to the pure AgNW-based FTCE, the proposed FTCE incorporating an MXene concentration of 5 mg/mL showed a significant enhancement of 40% in electrical conductivity, while the FTCE with a concentration of 2 mg/mL exhibited an improved figure of merit. Furthermore, we successfully demonstrate an embedded system integrating the FTCE-based capacitive touch and proximity sensor. These achievements in optoelectronic performance signify a tremendous potential for the development of high-performance flexible devices. [ABSTRACT FROM AUTHOR]

In Industry 4.0, integrating Industrial Internet of Things (IIoT) and smart manufacturing is crucial for high-quality, efficient, and cost-effective production. However, the performance of IIoT systems can be hindered by unevenly distributed edge service providers (ESPs). To tackle this challenge, we propose an optimized embedded system for edge intelligence and smart manufacturing, leveraging digital twin (DT) technology. Our approach employs a DT-assisted alliance game resource optimization strategy to jointly optimize multidimensional resource allocation, including bandwidth, computing, and caching resources, while considering constraints like maximum delay. The optimization problem maximizes edge terminal utility and ESP utility, transformed into a convex optimization problem with linear constraints. An approximate optimal solution is obtained through an alternating iterative method. Simulation results demonstrate significant enhancements in resource utilization efficiency compared to baseline schemes like Nash equilibrium and large coalition. The proposed scheme is ideal for large-scale edge intelligence and smart manufacturing systems, with benefits increasing alongside the number of ESPs. [ABSTRACT FROM AUTHOR]

Abstract Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.

With the rapid development of information technology, rural financial management is facing the great challenge of digitalization and intelligentization. This study aims to explore the application of embedded systems combined with blockchain technology in rural financial management to provide more efficient and secure solutions. Firstly, the main problems in rural financial management are analyzed, including low data processing efficiency and poor data security. It then explores the potential applications of embedded systems in financial management, as well as the main characteristics and classifications of blockchain technology. Based on this, a model integrating embedded system and blockchain technology is designed, and the effectiveness of the model is verified by experiments. The experimental results show that the system not only significantly improves the data processing speed, but also enhances the data security and improves the user satisfaction. In general, this study provides theoretical and practical support for the technical innovation of rural financial management, and shows a broad prospect in practical application. [ABSTRACT FROM AUTHOR]

This research presents an experimental electric vehicle developed at the Tecnológico Nacional de México Celaya campus. It was decided to use a golf cart-type gasoline vehicle as a starting point. Initially, the body was removed, and the vehicle was electrified, meaning its engine was replaced with an electric one. Subsequently, sensors used to measure the vehicle states were placed, calibrated, and instrumented. Additionally, a mathematical model was developed along with a strategy for the parametric identification of this model. A communication scheme was implemented consisting of four slave devices responsible for controlling the accelerator, brake, steering wheel, and measuring the sensors related to odometry. The master device is responsible for communicating with the slaves, displaying information on a screen, creating a log, and implementing trajectory tracking techniques based on classical, geometric, and predictive control. Finally, the performance of the control algorithms implemented on the experimental prototype was compared in terms of tracking error and control input across three different types of trajectories: lane change, right-angle curve, and U-turn. [ABSTRACT FROM AUTHOR]

The hybrid learning integrated remote laboratory, an innovation in educational technology, is a practical and real-time solution that allows students to access and interact with laboratory equipment remotely via the internet network. This research aims to analyze the implementation and effectiveness of this model in an embedded systems practicum course. The study, conducted using a quasi-experimental method, involved 35 students in the experimental group and 36 in the control group. The learning model in the experimental group was implemented with a differentiated approach, allowing students to participate in face-to-face learning in the laboratory or attend online. The technology in this research was built with an interactive user interface in the form of an e-learning integrated remote laboratory application, providing online students with access to learning materials, discussion forums, assignments, chat, video conference, and an online microcontroller coding editor. The use of the online microcontroller coding editor empowers students to create programs and control physical equipment in the laboratory, such as Arduino modules, several sensors, and output devices, remotely and in real-time. Descriptive analysis and t-tests were used to analyze students' comprehension of the embedded systems course. The test results showed a difference in the average academic achievement of students, with the learning outcomes of the experimental group students being higher than those of the control group. This model, therefore, demonstrates its impact on optimal and practical learning in the embedded system course. [ABSTRACT FROM AUTHOR]

Runtime safety assurance is a method that can ensure security attributes being satisfied during system operation. The simplex architecture-based runtime safety assurance methods are able to provide the safety guarantees even in the presence of unverified complex controllers. The basic idea is to combine the complex controller with a rigorously formalized safe controller, where the former operates in the absence of safety risk. Whenever a potential safety risk is detected, a decision module then completes the switching between the complex controller and the safe controller in order to guarantee the safety. This paper investigates the runtime safety assurance methods for cyber physical systems and summarizes the most relevant work from three aspects: the design of simplex decision-making module, the extension and improvement of simplex architecture, and the application of simplex architecture. This paper also provides an analysis of technical challenges and future directions of simplex architecture-based runtime safety assurance methods, indicating their potential and effectiveness in solving the runtime safety assurance issues in intelligent cyber-physical systems. [ABSTRACT FROM AUTHOR]

Within the field of human–computer interaction, data gloves play an essential role in establishing a connection between virtual and physical environments for the realization of digital human. To enhance the credibility of human‐virtual hand interactions, we aim to develop a system incorporating a data glove‐embedded technology. Our proposed system collects a wide range of information (temperature, bending, and pressure of fingers) that arise during natural interactions and afterwards reproduce them within the virtual environment. Furthermore, we implement a novel traversal polling technique to facilitate the streamlined aggregation of multi‐channel sensors. This mitigates the hardware complexity of the embedded system. The experimental results indicate that the data glove demonstrates a high degree of precision in acquiring real‐time hand interaction information, as well as effectively displaying hand posture in real‐time using Unity3D. The data glove's lightweight and compact design facilitates its versatile utilization in virtual reality interactions. [ABSTRACT FROM AUTHOR]

Smart algorithms for gait kinematic motion prediction in wearable assistive devices including prostheses, bionics, and exoskeletons can ensure safer and more effective device functionality. Although embedded systems can support the use of smart algorithms, there are important limitations associated with computational load. This poses a tangible barrier for models with increased complexity that demand substantial computational resources for superior performance. Forecasting through Recurrent Topology (FReT) represents a computationally lightweight time-series data forecasting algorithm with the ability to update and adapt to the input data structure that can predict complex dynamics. Here, we deployed FReT on an embedded system and evaluated its accuracy, computational time, and precision to forecast gait kinematics from lower-limb motion sensor data from fifteen subjects. FReT was compared to pretrained hyperparameter-optimized NNET and deep-NNET (D-NNET) model architectures, both with static model weight parameters and iteratively updated model weight parameters to enable adaptability to evolving data structures. We found that FReT was not only more accurate than all the network models, reducing the normalized root-mean-square error by almost half on average, but that it also provided the best balance between accuracy, computational time, and precision when considering the combination of these performance variables. The proposed FReT framework on an embedded system, with its improved performance, represents an important step towards the development of new sensor-aided technologies for assistive ambulatory devices. [ABSTRACT FROM AUTHOR]

As the information technology (IT) industry advances, embedded systems are being applied in various industrial sectors. With the expansion of application areas, there is a growing demand for high-precision, high-specification embedded systems, leading to the increased complexity of embedded software. Consequently, software errors can cause system malfunctions, resulting in accidents such as airplane crashes and the sudden acceleration of cars, leading to significant loss of life and property damage. Therefore, measures to ensure the safety and stability of increasing embedded systems malfunctions are necessary. This paper proposes a system that monitors the operation of target embedded systems in real-time and compares the extracted normal operation current/voltage patterns with the current/voltage data of a target embedded system (TES). It compares the operation data of the TES with automatically generated normal operation patterns by forcibly exposing them. It suggests algorithms for immediately detecting and efficiently recovering from the TES malfunctions. The proposed system applies two algorithms. (a) Monitoring TES current: When a malfunction is detected, a monitoring embedded systme (MES) resets the TES to restore normal operation. If malfunctions persist, it controls TES by using an algorithm to shut it down. Additionally, a proportional integral derivation (PID) control is applied to stabilize the current state. (b) Monitoring TES voltage: If a voltage drop occurs, the MES immediately stops the TES operation to minimize damage. The proposed algorithms were validated through experiments. For a normal TES consuming up to 95 mA, an error detection rate of 20% was applied. The TES was reset if it consumed over 114 mA. It was confirmed that the TES was stopped upon detecting the third malfunction. Regarding voltage, when the normal operating voltage of the system was around 5 V, if the TES operating voltage dropped below 4.3 V, it was detected as a malfunction, and the algorithm to stop the TES operation was validated. [ABSTRACT FROM AUTHOR]

A mechanical lock uses the physical movement and force of another object known as the key for 'opening' and 'closing'. Although mechanical locks are popular because they are durable and affordable, replacing them with electronic locks or smart locks have some benefits, which include higher level of security; less risk of illegal duplication of keys; and ease of programming to replace lost or compromised soft keys. Smart locks use electric current to lock and unlock. They can be controlled and monitored remotely through the Internet. While, it is not possible for any lock system to guarantee absolute security, it is important to identify the threats, assess the risks, and mitigate against the threats and risks to achieve improved security. This paper presents the design of a smart lock system with real-time detection and reaction to security breaches. The features of the smart lock include multiple access using tactile buttons, or a password via a keypad, or a fingerprint via a fingerprint sensor, or a password via a mobile app through a Bluetooth module for 'opening' and 'closing'. The smart lock detects intrusion and physical tampering, triggers an alarm, sends an SMS to a mobile phone and gives a report of security breaches and time of occurrence of such breaches. The smart lock is found to function correctly and efficiently. [ABSTRACT FROM AUTHOR]

Within the realm of sustainable agriculture and the protection of crops, the presence of intruders poses a substantial risk, leading to potential crop damages and injuries. In our paper, we conceptualize intruders as objects. Consequently, the implementation of object detection emerges as a crucial safety and field protection measure, effectively mitigating the adverse consequences of such encounters. While deep learning techniques have proven to yield superior results in object detection, their computational and parameter requirements have posed challenges in their widespread implementation. This research paper presents an alert system AgriGuard equipped with a lightweight object detection model for enhancing security in agriculture fields. The proposed embedded system utilizes EmbdYOLOv3 and TinyEmbdYOLOv3 models, modified versions of YOLOv3 and TinyYOLOv3, respectively, to overcome challenges in object detection, such as occluded or look-alike animals. The system integrates ultrasonic sensors, raspberry pi, real-time object detectors, google firebase, and an android application to detect and alert farmers for unauthorized access. Experimental results show that EmbdYOLOv3 outperforms YOLOv3 by 36.85%, and TinyEmbdYOLOv3 outperforms Tiny-YOLOv3 by 14.48% under the same embedded environment. The study highlights the effectiveness of low-power IoT devices and deep learning techniques in providing robust security solutions for the agriculture industry, offering a potential approach for mitigating crop damages and ensuring field safety. [ABSTRACT FROM AUTHOR]

With the development of the embedded system and improvement of the requirements from customers, embedded GUI which was used to communicate between users and embedded system became to be a key in researching the embedded system. This article introduced what is embedded system and what is embedded GUI. It described the porting of an embedded GUI on ARM Linux platform, including the establishment of compile environment, configuring and modifying of compile options and setting of runtime parameters. Above all, this article introduces the development of Embedded Linux GUI, describes the GUI development environment of Qt/Embedded and Qt, one procedure of the app location development based on Qt/Embedded is described in detail. This system adopts the B/S mode and uses the ARM11 development board with S3C6410 as the core. Sensors are used to obtain information, and the obtained data is judged and analyzed. When the preset conditions are not met, an alarm function is triggered. At the same time, data transmission is carried out through the Zigbee wireless module, and the receiving end analyzes and displays the received data. Each device is connected to the Zigbee module, which distinguishes it by assigning different network addresses and constantly transmits the status of the monitoring area to the receiving end. The receiving end distinguishes the monitoring status of different locations based on the different network addresses, increasing mobility. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office 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.)

In computer networks, security issues persist, and addressing hidden security risks is pivotal for ensuring network security. However, traditional single pattern matching algorithms like BM (Boyer-Moore) lack efficiency for network intrusion detection. This study employs multiple pattern matching algorithms to bolster the security of computer network engineering intrusion detection systems (IDS). A computer network intrusion detection system (NIDS) is designed using embedded technology to collect network logs and other pertinent data, subsequently comparing log data packets. The study delves into a multi pattern matching algorithm, AC (Aho-Corasick), which incorporates the SUNDAY algorithm to optimize unnecessary string matching jumps. Furthermore, the AC algorithm and BM algorithm are fused as control methods. Randomly generated 48M text data is utilized for testing purposes, comparing the AC algorithm, AC-BM algorithm, and AC-SUNDAY algorithm. For instance, when the pattern string length is 20 bytes, the memory consumption of the AC algorithm, AC-BM algorithm, and AC-SUNDAY algorithm is 12.2 MB, 9.8 MB, and 6.2 MB respectively. The findings indicate that applying the AC-SUNDAY algorithm in NIDS effectively reduces memory consumption and enhances the efficacy of network intrusion detection. [ABSTRACT FROM AUTHOR]

The investigation of hippocampal traveling waves has gained significant importance in comprehending and treating neural disorders such as epilepsy, as well as unraveling the neural mechanisms underlying memory and cognition. Recently, it has been discovered through both in vivo and in vitro experiments that hippocampal traveling waves are typically characterized by the coexistence of fast and slow waves. However, electrophysiological experiments face limitations in terms of cost, reproducibility, and ethical considerations, which hinder the exploration of the mechanisms behind these traveling waves. Model-based real-time virtual simulations can serve as a reliable alternative to pre-experiments on hippocampal preparations. In this paper, we propose a real-time simulation method for traveling waves of electric field conduction on a 2D plane by implementing a hippocampal network model on a multi-core parallel embedded computing platform (MPEP). A numerical model, reproducing both NMDA-dependent fast waves and Ca-dependent slow waves, is optimized for deployment on this platform. A multi-core parallel scheduling policy is employed to address the conflict between model complexity and limited physical resources. With the support of a graphical user interface (GUI), users can rapidly construct large-scale models and monitor the progress of real simulations. Experimental results using MPEP with four computing boards and one routing board demonstrate that a hippocampal network with a 200 × 16 pyramidal neuron array can execute real-time generation of both fast and slow traveling waves with total power consumption below 500 mW. This study presents a real-time virtual simulation strategy as an efficient alternative to electrophysiological experiments for future research on hippocampal traveling waves. [ABSTRACT FROM AUTHOR]

Multiprocessor system-on-chip (MPSoC) is increasingly used in today’s complicated embedded systems. This paper uses an efficient Hierarchical Interconnection Framework (EHIF) to improve throughput and latency over traditional 3D mesh NoC. The suggested router is hierarchical with one module handling inter-layer communication and intra-layer communication. The EHIF compares well to the traditional 3D mesh in terms of area and power consumption. The EHIF method reduced the execution time without increasing the memory buffer size to optimise time and memory. It achieves a high performance ratio, throughput, low power consumption, latency and delay compared to other methods [ABSTRACT FROM AUTHOR]

Dynamic voltage and frequency scaling (DVFS) is a technique used to optimize energy consumption in ultra-low-power embedded systems. To ensure sufficient computational capacity, the system must scale up its performance settings. The objective is to conserve energy in times of reduced computational demand and/or when battery power is used. Fast Fourier Transform (FFT), Cyclic Redundancy Check 32 (CRC32), Secure Hash Algorithm 256 (SHA256), and Message-Digest Algorithm 5 (MD5) are focused functions that demand computational power to achieve energy-efficient performance. Selected operations are analyzed from the energy consumption perspective. In this manner, the energy required to perform a specific function is observed, thereby mitigating the influence of the instruction set or system architecture. For stable operating voltage scaling, an exponential model for voltage calculation is presented. Statistical significance tests are conducted to validate and support the findings. Results show that the proposed optimization technique reduces energy consumption for ultra-low-power applications from 27.74% to up to 47.74%. [ABSTRACT FROM AUTHOR]

While Siamese object tracking has witnessed significant advancements, its hard real-time behaviour on embedded devices remains inadequately addressed. In many application cases, an embedded implementation should not only have a minimal execution latency, but this latency should ideally also have zero variance, i.e., be predictable. This study aims to address this issue by meticulously analysing real-time predictability across different components of a deep-learning-based video object tracking system. Our detailed experiments not only indicate the superiority of Field-Programmable Gate Array (FPGA) implementations in terms of hard real-time behaviour but also unveil important time predictability bottlenecks. We introduce dedicated hardware accelerators for key processes, focusing on depth-wise cross-correlation and padding operations, utilizing high-level synthesis (HLS). Implemented on a KV260 board, our enhanced tracker exhibits not only a speed up, with a factor of 6.6, in mean execution time but also significant improvements in hard real-time predictability by yielding 11 times less latency variation as compared to our baseline. A subsequent analysis of power consumption reveals our approach's contribution to enhanced power efficiency. These advancements underscore the crucial role of hardware acceleration in realizing time-predictable object tracking on embedded systems, setting new standards for future hardware–software co-design endeavours in this domain. [ABSTRACT FROM AUTHOR]