Search Results (76)

Accurate State of Charge (SOC) estimation is challenged by battery aging and complex internal dynamics. This work introduces a novel framework, Mamba-PG, that leverages the Mamba architecture to integrate internal gas pressure—a direct indicator of electrochemical state—for high-accuracy SOC estimation. The core innovation is a specialized pressure-aware gating mechanism designed to adaptively fuse the pressure signal with conventional electrical data. On a public dataset, our model achieved a state-of-the-art Mean Absolute Error (MAE) of 0.386%. Furthermore, we demonstrate that the gating mechanism learns a physically-plausible and interpretable strategy, dynamically adjusting the pressure signal’s influence based on its magnitude and the battery’s aging state. This study validates that the synergy of novel physical signals with efficient, interpretable architectures like Mamba presents a robust path toward next-generation Battery Management Systems. Full article

The emergent field of embedded computing presents a challenging scenario for ubiquitous music (ubimus) design. Available tools demand specific technical knowledge—as exemplified in the techniques involved in programming integrated circuits of configurable logic units, known as field-programmable gate arrays (FPGAs). Low-level hardware description languages used for handling FPGAs involve a steep learning curve. Hence, FPGA programming offers a unique challenge to probe the boundaries of ubimus frameworks as enablers of fast and versatile prototyping. State-of-the-art hardware-oriented approaches point to the use of high-level synthesis as a promising programming technique. Furthermore, current FPGA system-on-chip (SoC) hardware with an associated onboard general-purpose processor may foster the development of flexible platforms for musical signal processing. Taking into account the emergence of an FPGA-based ecology of tools, we introduce the ubimus plugging framework. The procedures employed in the construction of a modular- synthesis library based on field-programmable gate array hardware, ModFPGA, are documented, and examples of musical projects applying key design principles are discussed. Full article

This review is motivated by the urgent need to improve soil organic carbon (SOC) assessment methods, which are vital for enhancing soil health, addressing climate change, and promoting carbon farming. By employing a structured approach that involves a systematic literature search, data extraction, and analysis, 86 relevant studies were identified. These studies were evaluated to address the following specific research questions: (a) What are the state-of-the-art approaches in sampling, modeling, and data acquisition? and (b) What are the key challenges, open issues, potential advancements, and future directions needed to enhance the effectiveness of carbon farming practices? The findings indicate that while traditional SOC assessment techniques remain foundational, there is a significant shift towards incorporating model-based methods, machine learning models, proximal spectroscopy, and remote sensing technologies. These emerging approaches primarily serve as complementary to laboratory analyses, enhancing the overall accuracy and reliability of SOC assessments. Despite these advancements, challenges such as soil spatial and temporal variability, high financial costs, and limitations in measurement accuracy continue to hinder progress. This review also highlights the necessity for scalable, cost-effective, and precise SOC measurement tools, alongside supportive policies and incentives that encourage farmer adoption. Finally, the development of a “System-of-Systems” approach that integrates sampling, sensing, and modeling offers a promising pathway to balancing cost and accuracy, ultimately supporting carbon farming practices. Full article

Deep learning accelerators play a crucial role in enhancing computation-intensive AI applications. Optimizing system resources—such as shared caches, on-chip SRAM, and data movement mechanisms—is essential for achieving peak performance and energy efficiency. This paper explores the trade-off between last-level cache (LLC) and scratchpad memory (SPM) usage in accelerator-based SoCs. To evaluate this trade-off, we introduce a high-speed simulator for estimating the timing performance of complex SoCs and demonstrate the benefits of SPM utilization. Our work shows that dynamic reconfiguration of the LLC into an SPM with prefetching capabilities reduces cache misses while improving resource utilization, performance, and energy efficiency. With SPM usage, we achieve up to 13× speedup and a 10% reduction in energy consumption for CNN backbones. Additionally, our simulator significantly outperforms state-of-the-art alternatives, running 3000× faster than gem5-SALAM for fixed-weight convolution computations and up to 64,000× faster as weight size increases. These results validate the effectiveness of both the proposed architecture and simulator in optimizing deep learning workloads. Full article

This comprehensive state-of-the-art literature review investigates the status of the electric vehicle (EV) market share and the key factors that affect EV adoption with a focus on the shared vision of vehicle electrification and the smart city movement. Investigating the current scenarios of EVs, this study observes a rapid increase in the number of EVs and charging stations in different parts of the world. It reports that people’s socio-economic features (e.g., age, gender, income, education, vehicle ownership, home ownership, and political affiliation) significantly influence EV adoption. Moreover, factors such as high driving range, fuel economy, safety technology, financial incentives, availability of free charging stations, and the capacity of EVs to contribute to decarbonization emerge as key motivators for EV purchases. The literature also indicates that EVs are predominantly used for short-distance travel and users commonly charge their vehicles at home. Most users prefer fast chargers and maintain a high state of charge (SOC) to avoid unforeseen situations. Despite the emergent trend, there is a disparity in charging infrastructure supply compared to the growing demand. Thus, there is a pressing need for more public charging stations to meet the surging charging demand. The integration of smart charging stations equipped with advanced technologies to optimize charging patterns based on energy demand, grid capacity, and people’s demand can help policymakers leverage the smart city movement. This paper makes valuable contributions to the literature by presenting a conceptual framework articulating the factors of EV adoption, outlying their role in achieving smart cities, suggesting policy recommendations to integrate EVs into smart cities, and proposing suggestions for future research directions. Full article

With the exponential growth of the Internet of Things (IoT), ensuring robust end-to-end encryption is paramount. Current cryptographic accelerators often struggle with balancing security, area efficiency, and power consumption, which are critical for compact IoT devices and system-on-chips (SoCs). This work presents a novel approach to designing substitution boxes (S-boxes) for Advanced Encryption Standard (AES) encryption, leveraging dual quad-bit structures to enhance cryptographic security and hardware efficiency. By utilizing Algebraic Normal Forms (ANFs) and Walsh–Hadamard Transforms, the proposed Register Transfer Level (RTL) circuitry ensures optimal non-linearity, low differential uniformity, and bijectiveness, making it a robust and efficient solution for ASIC implementations. Implemented on 65 nm CMOS technology, our design undergoes rigorous statistical analysis to validate its security strength, followed by hardware implementation and functional verification on a ZedBoard. Leveraging Cadence EDA tools, the ASIC implementation achieves a central circuit area of approximately 199 μm². The design incurs a hardware cost of roughly 80 gate equivalents and exhibits a maximum path delay of 0.38 ns. Power dissipation is measured at approximately 28.622 μW with a supply voltage of 0.72 V. According to the ASIC implementation on the TSMC 65 nm process, the proposed design achieves the best area efficiency, approximately 66.46% better than state-of-the-art designs. Full article

Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks. Full article

Lithium-ion batteries have revolutionized the portable and stationary energy industry and are finding widespread application in sectors such as automotive, consumer electronics, renewable energy, and many others. However, their efficiency and longevity are closely tied to accurately measuring their SOC and state of health (SOH). The need for precise algorithms to estimate SOC and SOH has become increasingly critical in light of the widespread adoption of lithium-ion batteries in industrial and automotive applications. While the benefits of lithium-ion batteries are undeniable, the challenges related to their efficient and safe management cannot be overlooked. Accurate estimation of SOC and SOH is crucial for ensuring optimal battery management, maximizing battery lifespan, optimizing performance, and preventing sudden failures. Consequently, research and development of reliable algorithms for estimating SOC and SOH have become an area of growing interest for the scientific and industrial community. This review article aims to provide an in-depth analysis of the state-of-the-art in SOC and SOH estimation algorithms for lithium-ion batteries. The most recent and promising theoretical and practical techniques used to address the challenges of accurate SOC and SOH estimation will be examined and evaluated. Additionally, critical evaluation of different approaches will be highlighted: emphasizing the advantages, limitations, and potential areas for improvement. The goal is to provide a clear view of the current landscape and to identify possible future directions for research and development in this crucial field for technological innovation. Full article

This paper presents operation and control systems for a new modular on-board charger (OBC) based on a SEPIC converter (MSOBC) for electric vehicle (EV) applications. The MSOBC aims to modularise the battery units in the energy storage system of the EV to provide better safety and improved operation. This is mainly achieved by reducing the voltage of the battery packs without sacrificing the performance required by the HV system. The proposed MSOBC is an integrated OBC which can operate the EV during traction and braking, as well as charge the battery units. The MSOBC is composed of several submodules consisting of a full-bridge voltage source converter connected on the ac side and SEPIC converter installed on the battery side. The SEPIC converter controls the battery segments with a continuous current because it has an input inductor which can smooth the battery’s currents without the need for large electrolytic capacitors. The isolated version of the SEPIC converter is employed to enhance the system’s safety by providing galvanic isolation between the batteries and the ac output side. This paper presents the necessary control loops to ensure the optimal operation of the EV with the MSOBC in terms of charge and temperature balance without disturbing the required modes of operation. The mathematical analyses in this paper are validated using a full-scale EV controlled by TMS320F28335 DSP. Full article

Currently, the selection of web services with an uncertain quality of service (QoS) is gaining much attention in the service-oriented computing paradigm (SOC). In fact, searching for a service composition that fulfills a complex user’s request is known to be NP-complete. The search time is mainly dependent on the number of requested tasks, the size of the available services, and the size of the QoS realizations (i.e., sample size). To handle this problem, we propose a two-stage approach that reduces the search space using heuristics for ranking the task services and a bat algorithm metaheuristic for selecting the final near-optimal compositions. The fitness used by the metaheuristic aims to fulfil all the global constraints of the user. The experimental study showed that the ranking heuristics, termed “fuzzy Pareto dominance” and “Zero-order stochastic dominance”, are highly effective compared to the other heuristics and most of the existing state-of-the-art methods. Full article

Light detection and ranging (LiDAR) technology, a cutting-edge advancement in mobile applications, presents a myriad of compelling use cases, including enhancing low-light photography, capturing and sharing 3D images of fascinating objects, and elevating the overall augmented reality (AR) experience. However, its widespread adoption has been hindered by the prohibitive costs and substantial power consumption associated with its implementation in mobile devices. To surmount these obstacles, this paper proposes a low-power, low-cost, single-photon avalanche detector (SPAD)-based system-on-chip (SoC) which packages the microlens arrays (MLAs) and a lightweight RGB-guided sparse depth imaging completion neural network for 3D LiDAR imaging. The proposed SoC integrates an 8 × 8 SPAD macropixel array with time-to-digital converters (TDCs) and a charge pump, fabricated using a 180 nm bipolar-CMOS-DMOS (BCD) process. Initially, the primary function of this SoC was limited to serving as a ranging sensor. A random MLA-based homogenizing diffuser efficiently transforms Gaussian beams into flat-topped beams with a 45° field of view (FOV), enabling flash projection at the transmitter. To further enhance resolution and broaden application possibilities, a lightweight neural network employing RGB-guided sparse depth complementation is proposed, enabling a substantial expansion of image resolution from 8 × 8 to quarter video graphics array level (QVGA; 256 × 256). Experimental results demonstrate the effectiveness and stability of the hardware encompassing the SoC and optical system, as well as the lightweight features and accuracy of the algorithmic neural network. The state-of-the-art SoC-neural network solution offers a promising and inspiring foundation for developing consumer-level 3D imaging applications on mobile devices. Full article

Wireless communication systems have undergone significant development in recent years, particularly with the transition from fourth generation (4G) to fifth generation (5G). As the number of wireless devices and mobile data usage increase, there is a growing need for enhancements and upgrades to the current wireless communication systems. CMOS transceivers are increasingly being explored to meet the requirements of the latest wireless communication protocols and applications while achieving the goal of system-on-chip (SoC). The radio frequency power amplifier (RFPA) in a CMOS transmitter plays a crucial role in amplifying RF signals and transmitting them from the antenna. This state-of-the-art review paper presents a concise discussion of the performance metrics that are important for designing a CMOS PA, followed by an overview of the trending research on CMOS PA techniques that focuses on efficiency, linearity, and bandwidth enhancement. Full article

This review is an outlook on CAR-T development up to the beginning of 2023, with a special focus on the European landscape and its regulatory field, highlighting the main features and limitations affecting this innovative therapy in cancer treatment. We analysed the current state of the art in the EU and set out a showcase of the field’s potential advancements in the coming years. For this analysis, the data used came from the available scientific literature as well as from the European Medicines Agency and from clinical trial databases. The latter were investigated to query the studies on CAR-Ts that are active and/or relevant to the review process. As of this writing, CAR-Ts have started to move past the “ceiling” of third-line treatment with positive results in comparison trials with the Standard of Care (SoC). One such example is the trial Zuma-7 (NCT03391466), which resulted in approval of CAR-T products (Yescarta™) for second-line treatment, a crucial achievement for the field which can increase the use of this type of therapy. Despite exciting results in clinical trials, limitations are still many: they regard access, production, duration of response, resistance, safety, overall efficacy, and cost mitigation strategies. Nonetheless, CAR-T constructs are becoming more diverse, and the technology is starting to produce some remarkable results in treating diseases other than cancer. Full article

Migraine is a complex neurological disorder and a major cause of disability. A wide range of different drug classes such as triptans, antidepressants, anticonvulsants, analgesics, and beta-blockers are used in acute and preventive migraine therapy. Despite a considerable progress in the development of novel and targeted therapeutic interventions during recent years, e.g., drugs that inhibit the calcitonin gene-related peptide (CGRP) pathway, therapy success rates are still unsatisfactory. The diversity of drug classes used in migraine therapy partly reflects the limited perception of migraine pathophysiology. Genetics seems to explain only to a minor extent the susceptibility and pathophysiological aspects of migraine. While the role of genetics in migraine has been extensively studied in the past, the interest in studying the role of gene regulatory mechanisms in migraine pathophysiology is recently evolving. A better understanding of the causes and consequences of migraine-associated epigenetic changes could help to better understand migraine risk, pathogenesis, development, course, diagnosis, and prognosis. Additionally, it could be a promising avenue to discover new therapeutic targets for migraine treatment and monitoring. In this review, we summarize the state of the art regarding epigenetic findings in relation to migraine pathogenesis and potential therapeutic targets, with a focus on DNA methylation, histone acetylation, and microRNA-dependent regulation. Several genes and their methylation patterns such as CALCA (migraine symptoms and age of migraine onset), RAMP1, NPTX2, and SH2D5 (migraine chronification) and microRNA molecules such as miR-34a-5p and miR-382-5p (treatment response) seem especially worthy of further study regarding their role in migraine pathogenesis, course, and therapy. Additionally, changes in genes including COMT, GIT2, ZNF234, and SOCS1 have been linked to migraine progression to medication overuse headache (MOH), and several microRNA molecules such as let-7a-5p, let-7b-5p, let-7f-5p, miR-155, miR-126, let-7g, hsa-miR-34a-5p, hsa-miR-375, miR-181a, let-7b, miR-22, and miR-155-5p have been implicated with migraine pathophysiology. Epigenetic changes could be a potential tool for a better understanding of migraine pathophysiology and the identification of new therapeutic possibilities. However, further studies with larger sample sizes are needed to verify these early findings and to be able to establish epigenetic targets as disease predictors or therapeutic targets. Full article

Thermal vision-based devices are nowadays used in a number of industries, ranging from the automotive industry, surveillance, navigation, fire detection, and rescue missions to precision agriculture. This work describes the development of a low-cost imaging device based on thermographic technology. The proposed device uses a miniature microbolometer module, a 32-bit ARM microcontroller, and a high-accuracy ambient temperature sensor. The developed device is capable of enhancing RAW high dynamic thermal readings obtained from the sensor using a computationally efficient image enhancement algorithm and presenting its visual result on the integrated OLED display. The choice of microcontroller, rather than the alternative System on Chip (SoC), offers almost instantaneous power uptime and extremely low power consumption while providing real-time imaging of an environment. The implemented image enhancement algorithm employs the modified histogram equalization, where the ambient temperature sensor helps the algorithm enhance both background objects near ambient temperature and foreground objects (humans, animals, and other heat sources) that actively emit heat. The proposed imaging device was evaluated on a number of environmental scenarios using standard no-reference image quality measures and comparisons against the existing state-of-the-art enhancement algorithms. Qualitative results obtained from the survey of 11 subjects are also provided. The quantitative evaluations show that, on average, images acquired by the developed camera provide better perception quality in 75% of tested cases. According to qualitative evaluations, images acquired by the developed camera provide better perception quality in 69% of tested cases. The obtained results verify the usability of the developed low-cost device for a range of applications where thermal imaging is needed. Full article