Embedded System Research Articles

A Linear Method to Fit Equivalent Circuit Model Parameter Values to HPPC Relaxation Data From Lithium-Ion Cells

Abstract Battery management systems require mathematical models of the battery cells that they monitor and control. Commonly, equivalent circuit models are used. We would like to be able to determine the parameter values of their equations using simple tests and straightforward optimizations. Historically, it has appeared that nonlinear optimization is required to find the state-equation time constants. However, this article shows that the relaxation interval following a current or power pulse provides data that can be used to find these time constants using linear methods. After finding the time constants, the remaining parameter values can also be found via linear regression. Overall, only linear algebra is used to find all of the parameter values of the equivalent circuit model. This yields fast, robust, and simple implementations, and even enables application in an embedded system, such as a battery management system, desiring to retune its model parameter values as its cell ages.

Optimizing secure multimedia communication in embedded systems a parallel convolutional neural network approach to RIS and D2D resource allocation

With the rapid development of Internet of Things (IoT) services, technologies that leverage multimedia computer communication for information sharing in embedded systems have become a research focus. To address the challenges of low spectral efficiency and poor network flexibility in multimedia computer communications, this paper proposes a resource allocation scheme based on parallel Convolutional Neural Network (CNN). The scheme optimizes the base station beamforming vector and the Reconfigurable Intelligent Surface (RIS) phase shifts to maximize the secure transmission rate for cellular users (CUs), while ensuring normal and secure communication for device-to-device (D2D) users. First, to mitigate interference caused by D2D users reusing CU spectrum resources, the RIS phase shifts and beamforming vectors are optimized to suppress interference and enhance system secrecy rates. Second, to maximize the CU secrecy rate, the paper proposes a parallel CNN-based resource allocation model that considers base station transmission power, RIS reflection coefficients, and D2D communication rate constraints, incorporating multi-scale residual modules in the convolutional layers of the model. Simulation results demonstrate that the proposed CNN-based resource allocation scheme significantly improves the secrecy rate of embedded system communications, ensuring secure multimedia computing, and outperforms traditional methods.

Real-Time Embedded Control of Vehicle Dynamics Using ESP32: A Discrete Nonlinear Approach

This article explores the application of the Espressif ESP32 System-On-Chip (SoC) for managing vehicle dynamics through real-time digital proportional–integral (PI-like) control. We present the development of advanced driving assistance algorithms for Active Front Steering (AFS) and Rear Torque Vectoring (RTV) on this cost-effective, commercially available embedded system. Using digital PI-like control algorithms designed for AFS and RTV, the primary ESP32 board receives and processes steering signals, executing a discrete-time control model of the vehicle dynamic to enable dynamic adjustments to steering and torque. To enhance simulation realism, a secondary ESP32 is employed to generate the steering signal, effectively mimicking a steer-by-wire system via its analog output ports. This configuration facilitates the simulation and evaluation of control algorithms in a realistic test environment, ensuring enhanced vehicle dynamic stability and maneuverability under various conditions. Additionally, simulations are conducted using MATLAB 2023a and CarSim 2017.1 to compare the efficacy and benefits of the implementation. Our objective is to establish a platform for evaluating discrete controllers capable of real-time vehicle operation. This methodology accelerates and reduces the cost of improving vehicle system stability and responsiveness, enabling the immediate verification and fine-tuning of control parameters as needed.

AttackDefense Framework (ADF): Enhancing IoT Devices and Lifecycles Threat Modeling

Threat modeling (TM) is essential to manage, prevent, and fix security and privacy issues in our society. TM requires a data model to represent threats and tools to exploit such data. Current TM data models and tools have significant limitations preventing their usage in real-world scenarios. For example, it is challenging to TM embedded devices with current data models and tools as they cannot model their hardware, firmware, and low-level software. Moreover, it is impossible to TM a device lifecycle or security-privacy tradeoffs as these data models and tools were developed for other use cases (e.g., software security or user privacy). We fill this relevant gap by presenting the AttackDefense Framework (ADF), which provides a novel data model and related tools to augment TM. ADF’s building block is the AD object that can be used to represent heterogeneous and complex threats. Moreover, ADF provides automations to process a collection of AD objects, including ways to create sets, maps, chains, trees, and wordclouds of AD objects. We present ADF , a toolkit implementing ADF composed of four modules (Catalog, Parse, Check, and Analyze). We confirm that the data model and tools provided by ADF are useful by running an extensive set of experiments while threat modeling a crypto wallet and its lifecycle. Our experiments involved seven expert groups from academia and industry, each using the ADF on an orthogonal threat class. The evaluation generated 175 high-quality ADs covering ISA/IEC 62433-4-1 SecDev Lifecycle, side-channels, fault injection, microarchitectural attacks, speculative execution, pre-silicon testing, invasive physical chip modifications, Bluetooth protocol and implementation threats, and FIDO2 authentication.

CO-TSM: A Flexible Model for Secure Embedded Device Ownership and Management

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development.

Solid–liquid separation state control system for shale shaker based on computer vision

Shale shakers are crucial in drilling operations, separating solid–liquid mixtures, protecting equipment, improving efficiency, and supporting environmental protection and resource recovery. Typically, the solid–liquid separation status on shaker screens is assessed through manual inspection and experiential judgment, with the screen tilt angle adjusted manually. This method has inherent delays, failing to promptly detect and address screen surface abnormalities. To overcome this, a computer vision-driven intelligent control system has been proposed, enhancing drilling sieving operations’ efficiency. The system uses computer vision and servo motors to monitor and adjust the sieving state. Twin industrial cameras capture images, which undergo preprocessing like perspective correction, grayscale conversion, noise reduction, and illumination equalization. Local threshold segmentation and morphological analysis distinguish between liquid and solid states. The segmented images’ contours and area analysis precisely determine the separation state. Results are transmitted to motors via an industrial computer to adjust the sieve net angle. Servo motors on both sides manipulate the sliding block lifting mechanism for precise tilt adjustment. Experiments demonstrate that the method achieves an average accuracy of 93.635% in identifying mud edges. The system’s high efficiency and reliability ensure accurate identification and adjustment of the solid–liquid separation state, thereby optimizing drilling sieving workflows.

Improved yolov5 algorithm combined with depth camera and embedded system for blind indoor visual assistance

To assist the visually impaired in their daily lives and solve the problems associated with poor portability, high hardware costs, and environmental susceptibility of indoor object-finding aids for the visually impaired, an improved YOLOv5 algorithm was proposed. It was combined with a RealSense D435i depth camera and a voice system to realise an indoor object-finding device for the visually impaired using a Raspberry Pi 4 B device as its core. The algorithm uses GhostNet instead of the YOLOv5s backbone network to reduce the number of parameters and computation of the model, incorporates an attention mechanism (coordinate attention), and replaces the YOLOv5 neck network with a bidirectional feature pyramid network to enhance feature extraction. Compared to the YOLOv5 model, the model size was reduced by 42.4%, number of parameters was reduced by 47.9%, and recall rate increased by 1.2% with the same precision. This study applied the improved YOLOv5 algorithm to an indoor object-finding device for the visually impaired, where the searched object was input by voice, and the RealSense D435i was used to acquire RGB and depth images to realize the detection and ranging of the object, broadcast the specific distance of the target object by voice, and assist the visually impaired in finding the object.

Brain Tumor Segmentation from Optimal MRI Slices Using a Lightweight U-Net

The timely detection and accurate localization of brain tumors is crucial in preserving people’s quality of life. Thankfully, intelligent computational systems have proven invaluable in addressing these challenges. In particular, the UNET model can extract essential pixel-level features to automatically identify the tumor’s location. However, known deep learning-based works usually directly feed the 3D volume into the model, which causes excessive computational complexity. This paper presents an approach to boost the UNET network, reducing computational workload while maintaining superior efficiency in locating brain tumors. This concept could benefit portable or embedded recognition systems with limited resources for operating in real time. This enhancement involves an automatic slice selection from the MRI T2 modality volumetric images containing the most relevant tumor information and implementing an adaptive learning rate to avoid local minima. Compared with the original model (7.7 M parameters), the proposed UNET model uses only 2 M parameters and was tested on the BraTS 2017, 2020, and 2021 datasets. Notably, the BraTS2021 dataset provided outstanding binary metric results: 0.7807 for the Intersection Over the Union (IoU), 0.860 for the Dice Similarity Coefficient (DSC), 0.656 for the Sensitivity, and 0.9964 for the Specificity compared to vanilla UNET.

Design and testing of a low-cost mobile platform for contactless building surveying

After the completion of building construction, evaluating the accuracy of the distance between the lower edge of electrical switches and the one-meter line of the building is a crucial metric for assessing construction quality. Currently, the traditional measurement method basically uses manual measurement and is inefficient. To overcome this problem, we present a low-cost, non-contact mobile platform for building measurement, based on a 2D laser rangefinder and RGB camera. The system mainly consists of six key components: a mobile chassis, dual-axis gimbal, laser rangefinder, RGB camera, industrial control computer, and an automatic one-meter line calibration device, which can achieve high-precision distance measurement. Firstly, a sensorless control method for brushless DC motors (BLDCM) based on the Extended Kalman Filter (EKF) algorithm is proposed, which effectively improves the accuracy and robustness of rotor position estimation. Secondly, an automatic calibration device is developed to ensure the laser line consistently remains at a height of one meter from the ground. Finally, by combining image processing technology and 2D laser range data, the distance between the lower edge of the electrical switch and the building’s one-meter line is precisely measured using a triangulation algorithm. Experimental results show that the system’s measurements, compared to manual measurements, have a relative error percentage of 0.57%, while the automatic calibration device has a maximum error of only 0.2 centimeter. Therefore, the system shows great potential for widespread application in the field of building measurement.

Implementation of Active Noise Canceling for Noise Reduction using Embedded System

Abstract This paper presents the utilization of active noise canceling (ANC) technology using an embedded system for a sound insulation box. The ANC system for the insulation box is designed to replace conventional heavyweight passive sound absorber systems. A feedback filtered-x least mean square (FxLMS) algorithm is utilized to create the opposing-noise signal to counteract the incoming noise entering the box, so that reducing the noise level inside the box. In our previous work, we delved into a similar topic of using ANC for a sound insulation box, but the prior system had limited performance due to its inability to operate in real time. In this study, the FxLMS algorithm is implemented on an ARM Cortex-based microcontroller development board. A feedback structure is utilized, with an error microphone positioned in the middle of the box The ANC system’s capability was evaluated for pure tone noise in the frequency range of 63 Hz to 630 Hz. The experimental findings indicate that the ANC system has the capability to cut back the noise level up to 4 dB for some frequencies.

An efficient face and mask detection by embedded device via thermal imaging camera

Abstract Since 87.9% of COVID-19 patients develop a fever symptom, the mandatory wearing of masks, temperature screening, and isolation of individuals with fever are the most import methods for community epidemic prevention. This study develops a thermal imaging thermometer embedded system with face, face mask, and temperature detection capabilities using low-end CPU devices. The hardware includes a Raspberry Pi 4, FLIR Lepton 3.5 LWIR Micro Thermal Camera Module, display screen, and alarm. The system is characterized by its low cost and lightweight design, and it only needs to detect necessary areas around the face area during face mask and temperature detection, effectively avoiding external interference and further enhancing the effectiveness of epidemic prevention. The face detection model is based on the YOLO-Fastest architecture and has been improved, referred to as Pi-fast. In terms of performance on the training and testing sets, our proposed Pi-fast achieves Mean Average Precision (mAP) values of 99.64% and 93.18%, F1 score values of 0.94 and 0.87, and Intersection over Union (IoU)values of 78.07% and 65.98%, respectively. Regarding system performance, the frame rate of FLIR Lepton 3.5 Thermal Camera Module hardware specifications is 8.7 FPS. When using YOLO-Fastest and proposed Pi-fast model for detection on the proposed thermal imaging system, the frame rates are 6.58 FPS and 7.32FPS, respectively. The proposed Pi-fast model outperforms YOLO-Fastest about 0.74 FPS (11.2%). The system can detect objects at a distance of about 7 meters from the lens in thermographic camera with a resolution of only 160×120 pixels, making it suitable for multi-person screening and improving the screening process’s efficiency. The system can effectively perform temperature screening and face mask detection, making it suitable for office buildings, schools, medical clinics, and other public areas. During the severe epidemic period, the proposed thermal imaging embedded system that was actually used for epidemic prevention work at I-Shou University, improving screening efficiency and reducing labour costs.

What are the Embedded Systems

Embedded systems have become the cornerstone of modern technology, powering everything from intelligent buildings to autonomous vehicles. These unique computer systems made to carry out particular functions inside larger systems, enhance efficiency and functionality across various industries. As we delve into the world of embedded systems, we will explore their applications, essential components, distinctive characteristics, and the challenges associated with their design. Specifically, we will focus on optimizing design metrics and discussing the three key technologies that underpin embedded system development.

Instrumentation in Tractor for Evaluating Performance of a Tractor Drawn Rotavator

Performance measurement of agricultural machinery is essential for enhancing their efficiency for which they are operated. The performance related data helps the operator to select optimum operating parameters which reduce fuel consumption and increase field productivity. This study was aimed to develop various sensing units and install them on the tractor for evaluating performance of a tractor drawn rotavator. A microcontroller based embedded system was developed for continuous recording of wheel slip, actual forward speed, throttle opening, engine speed, velocity ratio, draft and power take-off (PTO) torque related data while carrying out tillage operation with rotavator. The wheel slip (from 1.5% to 35%) and actual forward speed of the tractor (up to 6 km h-1) was measured using inductive proximity sensors. For measuring throttle opening (0 to 100%), engine speed (750 to 2500 rpm), draft (65 to 350 kg) and PTO torque (up to 420 N-m) values, a potentiometer, magnetic pickup sensor, S-type loadcell and PTO torque transducer, respectively, were used. For computing velocity ratio, peripheral speed of rotavator was measured using PTO speed data received from PTO torque transducer, and actual forward speed of tractor was measured using inductive proximity sensor. The developed sensing units were calibrated under both dynamic and static conditions, and were also verified in laboratory and field conditions. Results showed that output signals received from the sensing units varied linearly with applied load. The mean absolute percentage error (MAPE) between measured and actual values was found to be below 5%. From the measured parameters, specific energy requirement and total power required for carrying out tillage operation could be computed. The power requirement was found to increase with increase in both gear and throttle settings. The specific energy requirement was minimum when rotavator was operated in L2 gear of the tractor. The minimum specific energy of 21 Wh m-3 was found at L2 gear and 75% throttle opening.

Improved Performance of the Permanent Magnet Synchronous Motor Sensorless Control System Based on Direct Torque Control Strategy and Sliding Mode Control Using Fractional Order and Fractal Dimension Calculus

This article starts from the premise that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) control strategy, is characterized by the fact that the internal flux and torque control loop usually uses ON–OFF controllers with hysteresis, which offer easy implementation and very short response times, but the oscillations introduced by them must be cancelled by the external speed loop controller. Typically, this is a PI speed controller, whose performance is good around global operating points and for relatively small variations in external parameters and disturbances, caused in particular by load torque variation. Exploiting the advantages of the DTC strategy, this article presents a way to improve the performance of the sensorless control system (SCS) of the PMSM using the Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead–Lag under constant flux conditions. Sliding Mode Control (SMC) and FOSMC are proposed under conditions where the flux is variable. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system performs better is verified. The article also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance for the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional-Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM-SCS based on the DTC strategy in a complete Hardware-In-the-Loop (HIL) implementation.

AI and Cloud Computing in Healthcare: Transforming Patient Care with AWS and Java

A new era in healthcare is being ushered in by the convergence of artificial intelligence (AI) and cloud computing, which will significantly improve patient care through improved diagnosis, individualized treatment planning, and effective patient management. This article explores the potent combination of Java and Amazon Web Services (AWS) and how these technologies may be used to develop cutting-edge AI-driven healthcare solutions that optimize healthcare operations and dramatically enhance patient outcomes. Together with Java's robust and flexible programming environment, healthcare providers can leverage AWS's scalable infrastructure and advanced machine learning capabilities to create and implement sophisticated AI models that analyze massive amounts of medical data at a speed and accuracy never seen before. These developments are making it possible to make more accurate diagnoses, customized treatment plans, and proactive patient monitoring, which can result in better clinical judgment and possibly life-saving measures. But even as we consider the enormous positive effects these technologies have on society, we also have to face the ethical problems they raise, such as issues with data privacy, potential biases in algorithms, and unequal access to cutting-edge medical treatments. The goal of this thorough investigation is to present a fair assessment of the revolutionary potential of artificial intelligence (AI) and cloud computing in the healthcare industry, while also discussing the wider ramifications and obligations associated with incorporating these formidable technologies into our healthcare systems.

Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study.

In the last few decades, major progress has been made in the medical field; in particular, new treatments and advanced health technologies allow for considerable improvements in life expectancy and, more broadly, in quality of life. As a consequence, the number of elderly people is expected to increase in the following years. This trend, along with the need to improve the independence of frail people, has led to the development of unobtrusive solutions to monitor daily activities and provide feedback in case of risky situations and falls. Monitoring devices based on radar sensors represent a possible approach to tackle postural analysis while preserving the person's privacy and are especially useful in domestic environments. This work presents an innovative solution that combines millimeter-wave radar technology with artificial intelligence (AI) to detect different types of postures: a series of algorithms and neural network methodologies are evaluated using experimental acquisitions with healthy subjects. All methods produce very good results according to the main parameters evaluating performance; the long short-term memory (LSTM) and GRU show the most consistent results while, at the same time, maintaining reduced computational complexity, thus providing a very good candidate to be implemented in a dedicated embedded system designed to monitor postures.

Energy-aware bio-inspired spiking reinforcement learning system architecture for real-time autonomous edge applications.

Mobile, low-cost, and energy-aware operation of Artificial Intelligence (AI) computations in smart circuits and autonomous robots will play an important role in the next industrial leap in intelligent automation and assistive devices. Neuromorphic hardware with spiking neural network (SNN) architecture utilizes insights from biological phenomena to offer encouraging solutions. Previous studies have proposed reinforcement learning (RL) models for SNN responses in the rat hippocampus to an environment where rewards depend on the context. The scale of these models matches the scope and capacity of small embedded systems in the framework of Internet-of-Bodies (IoB), autonomous sensor nodes, and other edge applications. Addressing energy-efficient artificial learning problems in such systems enables smart micro-systems with edge intelligence. A novel bio-inspired RL system architecture is presented in this work, leading to significant energy consumption benefits without foregoing real-time autonomous processing and accuracy requirements of the context-dependent task. The hardware architecture successfully models features analogous to synaptic tagging, changes in the exploration schemes, synapse saturation, and spatially localized task-based activation observed in the brain. The design has been synthesized, simulated, and tested on Intel MAX10 Field-Programmable Gate Array (FPGA). The problem-based bio-inspired approach to SNN edge architectural design results in 25X reduction in average power compared to the state-of-the-art for a test with real-time context learning and 30 trials. Furthermore, 940x lower energy consumption is achieved due to improvement in the execution time.

Design and implementation in power-efficient and thermal conductivity of vedic processor for embedded applications

An embedded system that handles critical applications requires power economy, performance and thermal management, and an efficient Vedic processor with highly efficienct thermal conductivity. The proposed contemporary processor architecture has been designed to use Vedic mathematics and elaborate power and thermal optimization approaches to formulate an Arithmetic Logic Unit (ALU). This ALU is based on Vedic Sutras to enable quick computation, high precision floating point operations, and low power consumption. Since thermal management leads to energy loss in the form of heat, the embedded processor’s thermal conductivity is enhanced using novel materials and unique ways of heat dissipation. This improves performance as well as power efficiency. The proposed approach uses extensive simulation thermal analysis by EDA tools to build an optimised embedded processor that consumes 35% less power, has 40% high thermal conductivity, is 25% faster, and utilizes 15% less area when compared to conventional embedded processors. This paper helps in furthering embedded systems research by showing how old mathematical ideas can be used in a new engineering approach to solve current technology issues. The suggested novel embedded system is more efficient and thermally resistant for IOT, mobile computing, and industrial control systems.

BUFIT: FINE-GRAINED DYNAMIC BURST FAULT INJECTION TOOL FOR EMBEDDED field programmable gate array TESTING

Fault injection (FI) is a well-known method to attack embedded systems, particularly advanced FPGAs and microcontrollers physically. The FPGA-based embedded system constitutes SRAM for configuration data storage. Multiple-bit upset is a main threat for FPGAs due to technology scaling and complex application bit files. Space environments additionally incur radiation threats to these devices. This paper proposes burst error modeling and a burst fault injection tool (BUFIT) to address these issues. BUFIT has been proposed with fine-grained and coarse-grained circuits. Built-in instrumented FPGA-based FI is proposed for effectively injecting MBUs in configuration memory with space adaptive rate for accurately estimating soft errors. Evaluation of proposed BUFIT on Kintex-7 target FPGA to various OR 1200-based workloads is given to analyze the speed up of the proposed technique. Results on the OR 1200 processor show that BUFIT is three and two orders of magnitude faster than existing DPR and SCFIT techniques. It uses only 0.4 % CLB overhead and has negligible impact on FFs of target SFPGAs.

A Machine Vision Method for Identifying Blade Tip Clearance in Wind Turbines.

This paper introduces a machine vision method for measuring the blade tip clearance in a wind turbine. An industrial personal computer (IPC) is installed in the nacelle of the wind turbine to continuously receive video data from a digital camera mounted at the bottom of the nacelle. Using the open-source computer vision (OpenCV) digital image processing library data base, the real-time trajectory of the turbine blades is determined from the video data. Furthermore, fast Fourier transform (FFT) analysis is performed for determining the operating frequency of the blades in the images. The amplitude analysis performed at this operating frequency reveals the pixel-based blade tip clearance, which is then used to calculate the actual clearance of the wind turbine. This value is subsequently transmitted to the main controller of the wind turbine. The main controller can enhance the operational safety of the wind turbine by implementing appropriate pitch control strategies to restrict and safeguard the blade tip clearance. The results obtained by conducting experiments on a 2.0 MW wind turbine unit validate the effectiveness of the proposed identification method. In this method, the blade tip clearance can be calculated effectively in real time, and both the video sampling rate and communication speed meet the requirements for controlling the blade pitch.