Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
Embedded systems are integral to the advancement of New Energy Vehicles (NEVs), enabling efficient, safe, and intelligent operations. This paper reviews their key applications in NEVs, including battery management, energy optimization, fault diagnostics, and communication integration. Embedded systems enhance vehicle performance by enabling precise State of Charge (SOC) and State of Health estimation, optimizing energy usage, and ensuring real-time fault detection. However, several challenges remain, such as computational inefficiency, scalability limitations, and the robust integration of multi-sensor data. Opportunities for future development include leveraging artificial intelligence and machine learning to improve adaptive algorithms, designing modular architectures for cost-effective scalability, and advancing hybrid communication protocols for seamless subsystem interaction. By addressing these challenges, embedded systems can further enhance NEV efficiency, safety, and reliability, supporting the global transition to sustainable transportation. This review aims to analyze current research, identify gaps, and propose innovative solutions to drive the evolution of embedded technologies in NEVs....
This paper focuses on the optimization of embedded systems in the Internet of Things technology, vertical domain customization strategies, integration with emerging technologies, cross-platform deployment, and scalability design, as well as other related issues. It employs case analysis and comparative research methods to conduct in-depth research. The research results show that embedded IoT systems significantly improve the intelligence level of agricultural production through hardware innovation and architecture evolution. Edge computing and cloud collaboration architectures address latency and bandwidth bottlenecks, while TinyML and blockchain technology equip devices with greater intelligence and trust. However, protocol fragmentation and security vulnerabilities have become major obstacles to its scale-up. In the future, breakthroughs should be made in the establishment of cross-platform embedded device interoperability standards, the development of lightweight post-quantum encryption algorithms, and the construction of predictive maintenance systems based on digital twins. At the same time, modular design helps to lower the barriers to adoption by small farmers and promote the inclusive development of agricultural IOT....
Dynamic analysis, through rehosting, is an important capability for security assessment in embedded systems software. Existing rehosting techniques aim to provide high-fidelity execution by accurately emulating hardware and peripheral interactions. However, these techniques face challenges in adoption due to the increasing number of available peripherals and the complexities involved in designing emulation models for diverse hardware. Additionally, contrary to the prevailing belief that guides existing works, our analysis of reported bugs shows that high-fidelity execution is not required to expose most bugs in embedded software. Our key hypothesis is that security vulnerabilities are more likely to arise at higher abstraction levels. To substantiate our hypothesis, we introduce LEMIX, a framework enabling dynamic analysis of embedded applications by rehosting them as x86 Linux applications decoupled from hardware dependencies. Enabling embedded applications to run natively on Linux facilitates security analysis using available techniques and takes advantage of the powerful hardware available on the Linux platform for higher testing throughput. We develop various techniques to address the challenges involved in converting embedded applications to Linux applications. We evaluated LEMIX on 18 real-world embedded applications across four RTOSes and found 21 new bugs, in 12 of the applications and all 4 of the RTOS kernels. We report that LEMIX is superior to existing state-of-the-art techniques both in terms of code coverage (∼2X more coverage) and bug detection (18 more bugs)....
Distributed embedded systems are increasingly deployed in critical infrastructure, automotive, industrial, and IoT applications. Ensuring these systems stay updated with security patches, feature enhancements, and bug fixes is essential. This article proposes a live over-the-air (OTA) update framework designed specifically for distributed embedded networks. The framework enables non-disruptive, secure, and reliable software updates with minimal downtime and system risk. It implements a hierarchical architecture with centralized orchestration, decentralized execution, and multi-layered security mechanisms. The system employs redundancy management through a clustered topology with N-1 redundancy principles, allowing updates to proceed in parallel across clusters while maintaining operational continuity. The solution addresses key challenges including network instability through resumable downloads and redundant paths, power interruptions via checkpointing mechanisms, security threats with end-to-end encryption and code signing, and version compatibility through pre-update validation. Testing in real-world environments demonstrated exceptional reliability, minimal downtime, and robust recovery from anomalies. The framework significantly enhances the manageability and security posture of distributed embedded systems across multiple application domains....
Instrument recognition is a crucial aspect of music information retrieval, and in recent years, machine learning-based methods have become the primary approach to addressing this challenge. However, existing models often struggle to accurately identify multiple instruments within music tracks that vary in length and quality. One key issue is that the instruments of interest may not appear in every clip of the audio sample, and when they do, they are often unevenly distributed across different sections of the track. Additionally, in polyphonic music, multiple instruments are often played simultaneously, leading to signal overlap. Using the same overlapping audio signals as partial classification features for different instruments will reduce the distinguishability of features between instruments, thereby affecting the performance of instrument recognition. These complexities present significant challenges for current instrument recognition models. Therefore, this paper proposes a multi-instance multi-scale graph attention neural network (MMGAT) with label semantic embeddings for instrument recognition. MMGAT designs an instance correlation graph to model the presence and quantitative timbre similarity of instruments at different positions from the perspective of multi-instance learning. Then, to enhance the distinguishability of signals after the overlap of different instruments and improve classification accuracy, MMGAT learns semantic information from the labels of different instruments as embeddings and incorporates them into the overlapping audio signal features, thereby enhancing the differentiability of audio features for various instruments. MMGAT then designs an instance-based multi-instance multi-scale graph attention neural network to recognize different instruments based on the instance correlation graphs and label semantic embeddings. The effectiveness of MMGAT is validated through experiments and compared to commonly used instrument recognition models. The experimental results demonstrate that MMGAT outperforms existing approaches in instrument recognition tasks....
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