Embedded System Research Articles

Efficient Hybrid Electric Vehicle Power Management: Dual Battery Energy Storage Empowered by Bidirectional DC–DC Converter

ABSTRACTThis work offers a fuel cell power system with the ability to distribute power to the load from the electrical source and charge an auxiliary battery utilizing regenerative power flows created by the load. The approach is established on a bidirectional closed‐loop DC converter. A bidirectional DC–DC converter is presented as a means of achieving extremely high voltage energy storage systems (ESSs) for a DC bus or supply of electricity in power applications. This paper presents a novel dual‐active‐bridge (DAB) bidirectional DC–DC converter power management system for hybrid electric vehicles (HEVs). The proposed system makes it possible to charge an additional battery with regenerative power flows and distributes power from the electrical source to the load efficiently. The two main stages of the DAB converter, which are the focus of this work, are an interleaved buck/boost converter on the battery and a three‐phase wye‐wye series resonating converter on the DC bus. Each switch's current stress is greatly reduced by this design, which lowers transmission losses and enhances thermal performance. The interleaved buck conversion on the battery allows for lesser current stress in each switch, resulting in lower transmission loss. The increasing complexity and power of automotive embedded electronic systems have made the use of more potent power electronic converters in automobiles necessary. In recent years, many dual volt (42 V/14 V) bidirectional inverter topologies for automotive systems have been presented. However, the majority of them are either inefficient or use a huge number of transistors and magnetic devices in both parallel and series arrangements. As a result, in this study, a bidirectional high‐efficiency inverter with fewer components is provided. The design, modes of operation, and performance metrics of the DAB converter are examined, emphasizing its ability to achieve zero‐voltage switching (ZVS) and zero current switching (ZCS) throughout its operating range. The suggested system seeks to maximize EV power management, guaranteeing high dependability and efficiency. To test all of the aforementioned qualities, an evaluation version was created, with an average efficiency of 97.5%. This research could have a substantial impact on the advancement of power electronic converters for automotive applications, leading to better EV power management, increased system reliability, and increased overall efficiency.

EXPRESS: A Framework for Execution Time Prediction of Concurrent CNNs on Xilinx DPU Accelerator

Deep learning Processor Unit (DPU) is a highly configurable CNN accelerator that supports a variety of CNNs and can be implemented with multiple instances on the same FPGA. Many applications deploy concurrent execution of different CNNs and in such a setting, an execution time predictor can help “optimize” the DPU configurations to meet the performance requirements of different tasks. We characterize CNN execution on DPUs and reduce the variability in execution time due to interference from the operating system. Subsequently, we propose a machine learning-based framework (EXPRESS) to predict the execution time of any given CNN on a DPU configuration, considering CNN, DPU, and bus characteristics. We improvise EXPRESS to support heterogeneous CNNs in EXPRESS-2.0 by making features independent of the number of CNNs. Our entire experimentation is based on data from a real FPGA board for 16 standard CNNs. Our frameworks, EXPRESS and EXPRESS-2.0, significantly outperform state-of-the-art by achieving an average execution time prediction error of 2.2% and 0.7%, respectively. We illustrate the effectiveness of this low prediction error for design space exploration, which is very useful for embedded system application developers.

A Deep Cryptographic Framework for Securing the Healthcare Network from Penetration

Ensuring the security of picture data on a network presents considerable difficulties because of the requirement for conventional embedding systems, which ultimately leads to subpar performance. It poses a risk of unauthorized data acquisition and misuse. Moreover, the previous image security-based techniques faced several challenges, including high execution times. As a result, a novel framework called Graph Convolutional-Based Twofish Security (GCbTS) was introduced to secure the images used in healthcare. The medical data are gathered from the Kaggle site and included in the proposed architecture. Preprocessing is performed on the data inserted to remove noise, and the hash 1 value is computed. Using the generated key, these separated images are put through the encryption process to encrypt what they contain. Additionally, to verify the user’s identity, the encrypted data calculates the hash 2 values contrasted alongside the hash 1 value. Following completion of the verification procedure, the data are restored to their original condition and made accessible to authorized individuals by decrypting them with the collective key. Additionally, to determine the effectiveness, the calculated results of the suggested model are connected to the operational copy, which depends on picture privacy.

From sampling to cellblock: The fully automated journey of cytological specimens.

In recent years, technological innovation have emerged to standardize pathology laboratory processes and reduce the handling of diagnostic samples. Among them is an automatic tissue embedding system that eliminates the need for manual activity in tissue paraffin embedding, thereby improving sample preservation. Unfortunately, this system cannot be used for cytological specimens due to the lack of an effective holder to support the procedure steps. In this study, we evaluated the performance of a commercial polymer matrix to enable and standardize the automatic paraffin embedding of cytological material from different organs and sources. Cytological samples from 40 patients were collected on the matrices and submitted for fully automatic workflow preparation, from formalin fixation until paraffin block, using the Sakura embedding system. Our results demonstrated the feasibility of the automated procedure, from loading cytological sample onto the matrix to obtaining the paraffin cellblock, thereby avoiding manual manipulation of cellular material. All samples resulted adequately processed and paraffin-embedded showing satisfactory tissue permeation by processing reagents, optimal preservation of cytoplasmic and nuclear details, and good quality of staining results on paraffin sections. Automated embedding of cytological samples eliminates the risk of lost specimens, reduces laboratory burden, standardizes procedures, increases diagnostic yield, and ultimately improves patients' management.

Guidance device for visually impaired people based on ultrasonic signals and open hardware

<span>Visual impairment is a complex challenge that affects people of all ages, and it is estimated that around 2.2 billion people worldwide lack adequate access to medical treatment and support. In Latin America, there is a lack of attention to people with visual disabilities, evidenced by poor urban infrastructure and lack of compliance with inclusion laws. Some projects stand out for the use of prototypes with artificial vision technology, global positioning system (GPS) and smart canes. Therefore, the objective of the project is to use ultrasonic sensors and a low-cost electronic device coupled to canes, for obstacle detection and mobility using an open hardware embedded system. The results confirmed the efficiency in the detection and operation of the ultrasonic sensor by activating the light emitting diode (LED), the buzzer and the vibrating motor according to the programmed distances. Challenges were identified, such as adapting the sensor to the tilt of the cane and the importance of accurate calibration of the ultrasonic sensor. The system met its objectives by detecting objects in a range of 2 to 50 cm and providing sound alerts to improve the perception of blind people.</span>

Increasing Renewable Energy Penetration on Low-Voltage Networks: An Expert Knowledge Approach

While South Africa is deemed one of the countries with the highest irradiation levels, it still utilises coal as its primary energy source due to its abundance. Due to the world-wide drive towards carbon neutrality, residential, commercial, agricultural, and industrial consumers are considering small-scale embedded generation systems. The National Rationalised Specifications 097-2-3 document specifies the scale of the embedded generation capacity a consumer is allowed to install. However, specifications do not yet make the required provisions for the addition of energy storage. The effective collective management of the grouped small-scale embedded generation systems could provide a high level of energy security and increase the percentage of renewable energy generation in the total energy mix. Potential challenges come into play when considering the stochastic nature of photovoltaic generation and its effect on the storage capacity and the dispersion in load profiles of the residential units typically present on a low-voltage network. This paper contributes by investigating the utilisation of photovoltaic generation in conjunction with storage as the basis for virtual power plant control, with the aim to safely increase renewable energy penetration and improve energy security, all while remaining within the South African low-voltage regulatory limits. A two-level virtual power plant controller is proposed with the dispersed energy storage units as the primary controllable resources and the dispersed photovoltaic generation as the secondary controllable resources. The objective of the controller is to achieve nodal energy management, energy sharing, and ancillary service provision and finally to increase renewable energy penetration. A representative single-feeder low-voltage network is simulated, and test cases of 50% and 75% renewable energy penetration are investigated as the basis for evaluation. The proposed controller architecture proved to maintain network integrity for both test cases. The adaptability of the controller architecture was also confirmed for a changed feeder topology; in this case, it was a multi-feeder topology. Future work is warranted to inform policy on the allowed levels of renewable energy penetration to be based not only on demand but also on the level of energy storage present in a network.

Research on the Legal Risks of Digital Currency in the Big Data Environment

With the widespread application and growth of blockchain technology, cloud computing, and big data analytics in the financial industry, digital currencies have emerged. However, relevant regulations specifically targeting digital currencies have not been officially promulgated yet. In the process of promoting digital currencies, legal risks such as unclear boundaries of compensation liability, unclear ownership, potential privacy breaches, and financial regulatory challenges still need to be overcome. Therefore, it is essential to prioritize the prevention and management of legal risks associated with digital currencies in the big data environment, ensuring the smooth circulation and use of digital legal currencies across regions and promoting the internationalization and legalization of China’s digital legal currencies.

Intelligence data acquisition based on embedded system in Chinese cuisine cooker (CCICR V1.0)

The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ C$$\\end{document} and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking.

Isokinetic Force Modeling in Sports Based on Embedded System of Motion Recognition Model

With the gradual deepening of sports research, sports training methods integrating more scientific and technological means can greatly improve athletes’ professional ability. Combined with the isokinetic strength training method, it can effectively alleviate muscle recovery and body flexibility, which requires an analysis of the level of sports body promotion, and a more efficient training program for various sports parameters. In this study, taking strength enhancement as an example, the convolutional neural network is innovatively integrated into isokinetic strength training. Using the motion data obtained by wearable sensors in the training, we trained the neural network model by real-time analysis of data samples through an embedded system. Further, we optimized the network parameters to realize the extraction and recognition of EMG depth features. Compared with the traditional isokinetic strength classification model, this model lays a foundation for further constructing personalized strength training guidance methods. After the simulation test, the difference of the model’s accuracy requirement before and after the pre-training is 4.6%, and the difference of the test set before and after the pre-intensive training is [Formula: see text]1%. It is 16.2% higher than the TCAM algorithm, and the gap with STA-LSTM remains between [Formula: see text]. It is verified that the attention concentration control scheme can not only reduce the error rate in visual recognition tasks, but also improve the complexity of the neural network model. The optimized lightweight data structure is especially suitable for embedded systems with limited computing power. All kinds of test projects verify its rationality and make full use of the control ability of the embedded system and the characteristics of parallel computing and programmability, which has great practical application value for the development of the Internet of Things system network. The research results have made a thorough study of the physiological guidance of existing strength training, the identification and evaluation methods of exercise-induced muscle fatigue, and the current situation of the existing isokinetic equipment.

Embedded System‐Based Malaria Detection From Blood Smear Images Using Lightweight Deep Learning Model

ABSTRACTThe disease of malaria, transmitted by female Anopheles mosquitoes, is highly contagious, resulting in numerous deaths across various regions. Microscopic examination of blood cells remains one of the most accurate methods for malaria diagnosis, but it is time‐consuming and can produce inaccurate results occasionally. Due to machine learning and deep learning advances in medical diagnosis, improved diagnostic accuracy can now be achieved while costs can be reduced compared to conventional microscopy methods. This work utilizes an open‐source dataset with 26 161 blood smear images in RGB for malaria detection. Our preprocessing resized the original dimensions of the images into 64 × 64 due to the limitations in computational complexity in developing embedded systems‐based malaria detection. We present a novel embedded system approach using 119 154 trainable parameters in a lightweight 17‐layer SqueezeNet model for the automatic detection of malaria. Incredibly, the model is only 1.72 MB in size. An evaluation of the model's performance on the original NIH malaria dataset shows that it has exceptional accuracy, precision, recall, and F1 scores of 96.37%, 95.67%, 97.21%, and 96.44%, respectively. Based on a modified dataset, the results improved further to 99.71% across all metrics. Compared to current deep learning models, our model significantly outperforms them for malaria detection, making it ideal for embedded systems. This model has also been rigorously tested on the Jetson Nano B01 edge device, demonstrating a rapid single image prediction time of only 0.24 s. The fusion of deep learning with embedded systems makes this research a crucial step toward improving malaria diagnosis. In resource‐constrained settings, the model's lightweight architecture and accuracy enhancements hold great promise for addressing the critical challenge of malaria detection.

HADNet: A Novel Lightweight Approach for Abnormal Sound Detection on Highway Based on 1D Convolutional Neural Network and Multi-Head Self-Attention Mechanism

Video surveillance is an effective tool for traffic management and safety, but it may face challenges in extreme weather, low visibility, areas outside the monitoring field of view, or during nighttime conditions. Therefore, abnormal sound detection is used in traffic management and safety as an auxiliary tool to complement video surveillance. In this paper, a novel lightweight method for abnormal sound detection based on 1D CNN and Multi-Head Self-Attention Mechanism on the embedded system is proposed, which is named HADNet. First, 1D CNN is employed for local feature extraction, which minimizes information loss from the audio signal during time-frequency conversion and reduces computational complexity. Second, the proposed block based on Multi-Head Self-Attention Mechanism not only effectively mitigates the issue of disappearing gradients, but also enhances detection accuracy. Finally, the joint loss function is employed to detect abnormal audio. This choice helps address issues related to unbalanced training data and class overlap, thereby improving model performance on imbalanced datasets. The proposed HADNet method was evaluated on the MIVIA Road Events and UrbanSound8K datasets. The results demonstrate that the proposed method for abnormal audio detection on embedded systems achieves high accuracy of 99.6% and an efficient detection time of 0.06 s. This approach proves to be robust and suitable for practical applications in traffic management and safety. By addressing the challenges posed by traditional video surveillance methods, HADNet offers a valuable and complementary solution for enhancing safety measures in diverse traffic conditions.

Automatic Scheduling Method for Customs Inspection Vehicle Relocation Based on Automotive Electronic Identification and Biometric Recognition

This study presents an innovative automatic scheduling method for the relocation of customs inspection vehicles, leveraging Vehicle Electronic Identification (EVI) and biometric recognition technologies. With the expansion of global trade, customs authorities face increasing pressure to enhance logistics efficiency. Traditional vehicle scheduling often relies on manual processes and simplistic algorithms, resulting in prolonged waiting times and inefficient resource allocation. This research addresses these challenges by integrating EVI and biometric systems into a comprehensive framework aimed at improving vehicle scheduling. The proposed method utilizes genetic algorithms and intelligent optimization techniques to dynamically allocate resources and prioritize vehicle movements based on real-time data. EVI technology facilitates rapid identification of vehicles entering customs facilities, while biometric recognition ensures that only authorized personnel can operate specific vehicles. This dual-layered approach enhances security and streamlines the inspection process, significantly reducing delays. A thorough analysis of the existing literature on customs vehicle scheduling identifies key limitations in current methodologies. The automatic scheduling algorithm is detailed, encompassing vehicle prioritization criteria, dynamic path planning, and real-time driver assignment. The genetic algorithm framework allows for adaptive responses to varying operational conditions. Extensive simulations using real-world data from customs operations validate the effectiveness of the proposed method. Results indicate a significant reduction in vehicle waiting times—up to 30%—and an increase in resource utilization rates by approximately 25%. These findings demonstrate the potential of integrating EVI and biometric technologies to transform customs logistics management. Additionally, a comparison against state-of-the-art scheduling algorithms, such as NSGA-II and MOEA/D, reveals superior efficiency and adaptability. This research not only addresses pressing challenges faced by customs authorities but also contributes to optimizing logistics operations more broadly. In conclusion, the automatic scheduling method presented represents a significant advancement in customs logistics, providing a robust solution for managing complex vehicle scheduling scenarios. Future research directions will focus on refining the algorithm to handle peak traffic periods and exploring predictive analytics for enhanced scheduling optimization. Advancements in the intersection of technology and logistics aim to support more efficient and secure customs operations globally.

Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions

The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method’s output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times.

Network-on-Chip (NoC) Applications for IoT-Enabled Chip Systems: Latest Designs and Modern Applications

Network-on-Chip (NoC) technology has emerged as a critical innovation in the field of integrated circuit design, addressing the growing demands for efficient, scalable, and high-performance on-chip communication. This review provides a comprehensive examination of NoC, highlighting its architecture, key features, and current applications. NoC leverages a network-based approach, utilizing routers and links to interconnect various components on a chip, thereby overcoming the limitations of traditional System-on-chip (SoC) architectures that rely on shared bus systems. The review underscores NoC’s superior communication efficiency, scalability, and reduced latency, which are essential for modern high-performance computing, multi-core processors, and advanced embedded systems. It also explores NoC’s ability to optimize power consumption through dynamic voltage scaling and power gating, making it a favorable choice for energy-efficient designs. Despite its complexity, NoC’s modularity and adaptability make it suitable for a wide range of applications, from artificial intelligence accelerators to high-performance data centers. In conclusion, NoC stands as a transformative technology that enhances the performance and scalability of integrated circuits, paving the way for more sophisticated and powerful electronic systems. As the demand for higher computational capabilities and efficiency continues to rise, NoC is poised to play an increasingly vital role in the advancement of electronic design and architecture.

Route Planning Method of UAV Patrol Based on High Precision Tower Model

The high-precision positioning technology of industrial Unmanned Aerial Vehicle (UAV) plays a vital role in intelligent trajectory planning in power inspection. First, the information on power facilities is collected and processed to form a planning grid to optimize the flight path and correct the trajectory of UAV. However, the preparatory work is more complex, and the algorithm model which needs more detailed and complex terrain database data is not universal enough. To improve the safety of UAV and the efficiency of path planning, we establish a three-dimensional model of the power tower, calculate the coordinate position of the inspection work according to the information of the high-voltage transmission tower, and then determine the corresponding relationship between the inspection point and the flight trajectory combined with the safe distance. Based on the set of random interference semaphores and model rasterization, the discrete error strategy and Euler method are introduced to improve the performance. In each iteration, the best solution is first updated using the execution strategy. We continue to calculate the spatial coordinates of all reference points and provide the coordinate position distribution for the flight mission. The solution of equipment inspection trajectory optimization is realized by using dynamic obstacle perception. The proposed discrete measurement error self-correction algorithm is deployed on the flight platform to guide UAV inspection tasks according to the point cloud coordinate model of transmission equipment in the power system and to correct the flight route in time. To verify the research results, the Algorithm Artemisinin optimization and Whale Optimization Algorithm test verify that the ability of the proposed algorithm to get rid of local optimization is improved by 8.94% and, 12.42% respectively compared with other optimization algorithms. The convergence accuracy is 12.4% and 7.9% higher than other schemes, and it has a better effect in solving numerical problems. The research results using the embedded system to complete the task deployment and unloading provide a reference for trajectory planning and task design in different application scenarios.

Caracterización y Análisis de la Calidad del Agua en Entornos Urbanos mediante la implementación de un Sistema Embebido con Tecnología IoT

This study aims to characterize and analyze water quality in urban environments using an IoT embedded system, focusing on San Camilo’s community of Quevedo, Ecuador, where population growth has increased the demand for drinking water. An embedded system was designed to make use of a microcontroller and sensors to monitor critical water quality parameters in real time. The collected data is transmitted to a cloud platform for storage, analysis, and visualization. Spearman's correlation coefficient was preferred for statistical analysis of the data. This allowed for the identification of patterns and relationships between the variables examined. The data revealed significant associations in non-linear data, facilitating a decision regarding the development of predictive models for water management and treatment. The results demonstrate the feasibility and efficiency of using IoT technologies in the continuous monitoring of water quality, offering a scientific basis for the collection and processing of relevant data on the quality of drinking water in residential areas, providing a robust tool to monitor public health and the comfort of the inhabitants.

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.