Search Results (1,563)

Embedded systems increasingly require on-device deep learning, yet their processors must simultaneously handle real-time sensing, networking administration, and data control. Existing Field-Programmable Gate Array (FPGA) accelerators typically target peak per-operator throughput without addressing concurrent execution demands of real-time embedded platforms. This paper presents a systolic array-based accelerator prototype implemented on the Zynq-7000 SoC integrated directly into PyTorch, enabling dense linear algebra to be delegated to the FPGA chip while Cortex-A9 continues executing the software stack uninterrupted. Unlike traditional accelerators optimized for peak per-operator speed, this design prioritizes asynchronous co-executionbetween the processing system (PS, the dual-core Cortex-A9) and the programmable logic (PL): The PL performs tiled matrix multiplication, while the PS executes preprocessing, orchestration, and I/O data concurrently, increasing effective end-to-end throughput regardless of the relative isolated performance of CPU and FPGA. The proposed module includes high-level-synthesis (HLS)-based matrix multiplication, activation functions, and Advanced eXtensible Interface (AXI)-Stream Direct Memory Access (DMA) interfaces, wrapped as custom PyTorch kernels under the PetaLinux operating system. The results obtained on the PYNQ-Z2 board show that, once the DMA transfer time is included in the measurement, the FPGA path does not surpass Cortex-A9 in isolated per-operator latencies across the evaluated range; the benefit lies instead in delegating the matrix compute to the fabric at low incremental power while the host CPU cores stay available for concurrent tasks. A concurrent workload sweep across matrix sizes from $8\times 8$ to $256\times 256$ confirms that the co-execution mode sustains 98–99% of available PS compute throughput compared with a constant ≈50% in single-core blocking mode; the difference is statistically significant for all evaluated sizes (see Mann–Whitney U: $U=25$, $p=3.97\times {10}^{-3}$, perfect discrimination, $n=5$). A fair dual-core CPU-only baseline attains comparable PS availability, so this figure reflects the dual-core scheduling that co-execution enables rather than a per-operator advantage of the fabric; the accelerator’s distinct role is to perform the matrix arithmetic off the general-purpose cores at low incremental power. The design occupies only 8% of available look-up tables (LUTs) and 5% of digital signal processing (DSP) blocks, maintains 1.69 W power with a junction temperature of 44.5 °C, and achieves 96.10% MNIST accuracy under fixed-point arithmetic. Full article

The phasing out of high-GWP refrigerants and the growing diversity of working fluids used across heat pumps, refrigeration systems, and closed-cycle power applications have made drop-in fluid replacement a question of significant practical interest. Centrifugal compressors are designed around the thermophysical properties of a specific fluid, and the performance penalty is incurred when working fluid is replaced without redesigning the impeller. This study presents a CFD comparison of direct drop-in fluid replacement in a fixed geometry centrifugal compression system. Eight working fluids that span the property range relevant to current drop-in substitutions are evaluated: air, nitrogen, argon, carbon dioxide, R22, R134a, R1234yf, and R1234ze(E). A reference centrifugal impeller was reconstructed in ANSYS BladeGen, meshed in ANSYS TurboGrid using the Automatic Topology and Meshing method, and simulated in ANSYS CFX (2024 R2) as a single periodic passage with Frozen Rotor interfaces and Spalart–Allmaras turbulence closure. Performance maps were generated for each fluid across a range of rotational speeds and mass flow rates, with a common inlet reference condition applied across all cases to isolate the influence of fluid properties from an inlet state. The resulting dataset enables a like-for-like comparison of pressure ratio, efficiency, and shaft power requirement, providing a basis for assessing the aerodynamic implications of drop-in fluid substitution in centrifugal compression systems. Air, nitrogen, argon and carbon dioxide achieved similar peak efficiencies (~88%) and comparable pressure ratios (PR), indicating they can be used as drop-in substitutes without performance loss. Refrigerants R1234yf and R1234ze(E) matched R134a in efficiency (peak ~90%) while offering higher pressure ratios and significantly lower power requirements at peak efficiency. At 20,000 RPM and a mass flow rate of 2 kg/s, compared to a PR of 1.45 for air, nitrogen, carbon dioxide and argon achieved PRs of 1.4, 1.9 and 2.4, respectively. At the same settings, R134a and R1234 refrigerants reached PRs of 5 and 6, respectively. The power requirement was ~8 × 10⁴ W for air and similar fluids, and ~11 × 10⁴ W for refrigerants. Full article

Semi-active suspension systems combine low power consumption, rapid response, and fail-safe operation by reverting to passive mode after control failure, making them important for intelligent chassis and vibration-control systems. With the development of intelligent actuators, nonlinear modeling, and advanced control methods, this technology is expanding from conventional road vehicles to off-road vehicles and agricultural machinery. Compared with passenger cars, agricultural machinery faces stronger random excitation, time-varying loads, muddy environments, resource-constrained controllers, and requirements for operational accuracy. This review focuses on semi-active damping and vibration-isolation systems for off-road and agricultural applications. Mainstream actuators, control-oriented nonlinear damper models, classical, robust, and adaptive control methods, MPC, DRL, and mechanism–data fusion control are compared in terms of hardware constraints, model accuracy, real-time computation, and agricultural adaptability. Applications in seat/cab isolation, tractor and tracked chassis systems, rollover prevention, and precision implements are summarized. The review shows that semi-active suspension in agricultural machinery is evolving beyond the conventional trade-off between ride comfort and handling stability toward multi-objective coordination of safety, ground-contact stability, operational accuracy, operator protection, and energy consumption. Full article

High-channel-count neural recording requires wireless links with high throughput, low power, and compact implementation, yet commercial protocols and phase-locked loop (PLL)-based transmitters often trade data rate against power and complexity. We present a low-power, PLL-less wideband on–off keying (OOK) neural-signal transmitter fabricated in a 180 nm CMOS process. The transmitter employs a free-running inductor–capacitor voltage-controlled oscillator (LC-VCO), a Gilbert mixer for OOK modulation and reverse isolation, and a current-reuse stacked power amplifier. It consumes 8 mA from a 3.3 V supply (26.4 mW), demonstrates modulation and receiver frame acquisition at a maximum raw input rate of 90 Mbps, corresponding to a 180 Mbps Manchester-coded line rate, and tunes from 3.266 to 3.445 GHz. End-to-end bit error rate (BER) was measured at raw rates of 15 and 31.2 Mbps, corresponding to encoded rates of 30 and 62.4 Mbps; the latter matches the in vivo data stream. The transmitter was integrated with a 128-channel recording chip and evaluated in freely moving adult C57 mice. Wireless hippocampal spike and local field potential (LFP) recordings, wired-system comparison, and event-locked LFP analysis support its feasibility for untethered neural recording. Full article

With the large-scale integration of flexible loads, including electric vehicles (EVs), distributed energy storage systems (DSTs), communication base stations (COMs), and internet data centers (IDCs), into distribution networks, the increasing diversity of their operating characteristics is producing increasingly complex impacts on capacity requirements, power flow conditions, and reactive power support capabilities. However, existing studies have predominantly focused on isolated analyses of individual load types and still lack a unified evaluation framework for multiple representative flexible loads, making it difficult to systematically reveal the heterogeneous impacts of their grid integration. To address this gap, this paper develops fine-grained behavioral models for four representative flexible load categories by incorporating their key operational constraints and behavioral characteristics. A multi-dimensional quantitative assessment framework is then established across three dimensions: capacity, power flow and operation, and reactive power support and disturbance. Under a unified distribution network scenario, the impacts of large-scale integration are compared across load types and graduated penetration levels. The results show that different flexible loads exert significantly heterogeneous effects on distribution network operating states: COMs and IDCs are more likely to intensify local capacity pressure, operational fluctuations, and reactive power support burdens; EVs are more prominently associated with peak-period migration and reverse power flow risks; and the overall impact of DSTs remains comparatively moderate. The proposed methodology provides a unified analytical framework for assessing the impacts of multiple flexible load types on distribution networks and offers a reference for subsequent distribution network planning and operational optimization. Full article

Reliable power systems are essential to modern life, as severe storms continue to disrupt grid stability and cause widespread outages. Predicting storm outages enables utilities and emergency managers to pre-stage resources and improve resilience. However, the several days of forecast lead time typically needed for preparedness significantly affect the accuracy of outage predictions. This study investigates the impact of forecast lead time on the error propagation of a Gradient Boosting Machine (GBM)-based outage prediction model (OPM) driven by Weather Research and Forecasting (WRF) model forecasts and analysis predictions. We evaluate three error-analysis scenarios: FFAP (forecast vs. analysis-based outage predictions), FFAO (forecast vs. actual outages), and LFAO (leave-one-storm-out forecast vs. actual outages). Model performance is compared using Mean Absolute Percentage Error (MAPE) and Centered Root-Mean-Square Error (CRMSE) across short (12 h–1 d), medium (2–3 d), and long (4–5 d) forecast lead-time categories, with the long category representing the upper end of the medium-range forecast window relevant to operational preparedness. The results show that forecast lead time substantially affects outage prediction accuracy, but the magnitude depends on the evaluation setup. In the controlled FFAP scenario, CRMSE increased by approximately 110% as lead time increased, from 259 to 543 outages, isolating the effect of weather forecast degradation. In the more operational LFAO scenario, CRMSE was already high at short lead times, increasing from 847 to 920 outages, indicating that model generalization error dominates once storms are unseen. Across scenarios, LFAO errors were 51% higher than FFAO errors at short lead times, highlighting the importance of testing outage models under unseen-event conditions. These results quantify how forecast degradation and model generalization jointly shape the reliability of outage prediction and provide practical guidance for lead-time-aware storm preparedness. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) platforms, such as the CYGNSS constellation, provide unprecedented spatial coverage for monitoring ionospheric scintillation via the $S_4$ index. However, the operational utility of GNSS-R for space weather is substantially degraded by surface-induced signal contamination when sharp land–water boundaries (coastlines) trigger massive, false-positive $S_4$ pseudo-scintillations that imitate true ionospheric plasma irregularities. In this study, a robust machine learning (ML) methodology to autonomously distinguish surface-induced reflections from true atmospheric volumetric scattering was proposed. Using 1 Hz Level 1 continuous Signal-to-Noise Ratio (SNR) time-series data, morphologic features (e.g., maximum amplitude, peak prominence, and standard deviation) were extracted to train a Random Forest (RF) classifier. The model achieves 98% accuracy in differentiating coastal boundaries from ionospheric scintillation, evaluated on a global dataset of over ~450,000 anomalous events. Moreover, a multi-sensor case study of the historic May 2024 G5 geomagnetic storm is presented to validate the geophysical fidelity of the filtered data. The ML-isolated CYGNSS anomalies demonstrate strong spatial correlation with COSMIC-2 Radio Occultation (RO) F2-peak electron density (NmF2) variations and ground-based Rate of TEC Index (ROTI) maps. Furthermore, temporal cross-validation with 1 Hz localized ground magnetometer data in Northwest Mexico reveals positive synchronization between CYGNSS scattering events and localized electrodynamic disturbances. Finally, the results demonstrate that ML-de-aliased GNSS-R data can reliably link the oceanic observational gaps inherent to ground-based networks, offering a powerful new tool for global space weather monitoring. Full article

The issue of thermal runaway in power batteries of new-energy vehicles occurs frequently, posing a serious threat to life and property safety. This study aims to develop a high-performance fire-proof coating to address this problem. Specifically, the research focused on constructing an organic-inorganic composite intumescent fire-resistant coating, with modified halloysites (Ti-HNTs) serving as the key component. In this coating system, the intumescent flame-retardant (IFR) system and Ti-HNTs were employed as the organic and inorganic components, respectively, while water-based epoxy resin emulsion was selected as the matrix material. Through the utilization of XPS, FTIR, and SEM techniques, it was verified that the Ti-HNTs were successfully modified and integrated well with the coating matrix. Following further optimization of the Ti-HNTs proportion and coating thickness, it was determined that the coating containing 4% Ti-HNTs with a designed thickness of 1.5 mm exhibited the optimal fire-proofing performance. In the fire-resistance experiment, after 10 min of testing, the temperature of this coating could reach a minimum of 215.9 °C. Compared to the control group, its heat-insulation effect was enhanced by 49.4%, with an expansion multiplier of 37.7 and a maximum smoke density of 22.55. These results were significantly superior to those of the control group without the addition of Ti-HNTs. SEM analysis indicated that the coating could form a uniform and dense carbon layer, with an inner surface featuring a honeycomb-bubble structure. This SEM-analyzed Ti-HNTs-modified fire-proof coating demonstrated excellent fire resistance and thermal-isolation effects in new-energy vehicle batteries, thus providing reliable fire protection for the batteries. Additionally, impact-resistance tests revealed that the coating could withstand a simulated battery pressure-relief impact without penetration, maintaining its structural integrity and thermal-barrier function. This further validated its reliability for battery fire protection. Full article

The increasing integration of renewable energy resources, distributed energy systems, and intelligent sensing technologies has accelerated the transformation of conventional power grids into interconnected cyber–physical smart energy ecosystems. In this context, the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a key enabler for intelligent monitoring, adaptive energy management, resilient grid operation, and sustainable energy coordination. Although numerous studies have investigated AI, IoT, blockchain, and cybersecurity technologies individually, many existing reviews focus on isolated domains without adequately addressing the interactions between intelligent operational control, communication infrastructures, decentralized coordination, sustainability, and cyber resilience. Accordingly, this paper presents a comprehensive system-level review of AI–IoT-enabled smart energy ecosystems, focusing on smart grids, microgrids, intelligent energy management, blockchain-enabled decentralized coordination, carbon emissions monitoring, and cyber-resilient energy infrastructures. Unlike existing surveys that primarily emphasize individual technologies or algorithmic performance, this work highlights the cross-layer integration and architectural interdependencies between AI-driven operational intelligence, IoT-enabled monitoring, secure communication frameworks, and sustainability-oriented energy management. The paper also discusses key challenges related to interoperability, scalability, cybersecurity, communication latency, and distributed coordination, in addition to future research directions toward resilient, autonomous, and sustainable intelligent energy ecosystems. Full article

Portable sensing systems are increasingly important in biological and chemical analyses because they can provide analytical information at the point of decision-making. While traditional laboratory methods remain crucial for reference measurements, regulatory validation, and high-precision quantification, portable systems emphasize rapid response, convenience, cost-effectiveness, robustness, and relevance to decision-making. This paper views portable sensing systems as integrated analytical platforms rather than isolated sensing elements. The paper discusses recognition elements, including enzymes, antibodies, nucleic acid probes, aptamers, molecularly imprinted polymers, nanomaterials, and hybrid recognition interfaces, as well as electrochemical, optical, mass-sensitive, thermal, field-effect, and hybrid sensing technologies. Furthermore, this paper reviews platform designs, including paper-based analytical devices, chip lab systems, smartphone-assisted sensors, wearable and flexible sensors, handheld instruments, and wireless sensor networks. It explores their applications in sample handling, calibration, data processing, and field deployment. Applications of this technology include point-of-care diagnostics, pathogen detection, wearable health monitoring, agriculture, veterinary medicine, environmental monitoring, food safety, industrial process control, forensic analysis, public safety, and occupational exposure assessment. The report focuses on sample acquisition, miniaturized preparation, reagent storage, matrix interference, calibration transfer, signal conditioning, machine learning, cloud platforms, analytical validation, and decision support. Furthermore, it identifies key obstacles to translating academic prototypes into industrial products, including reproducibility, stability, manufacturability, ease of use, cybersecurity, regulatory approval, and market acceptance. Future development requires fully integrated sample-to-result systems, multimodal sensing, artificial intelligence, sustainable single-use materials, self-powered devices, and system-level validation under real-world operating conditions. Full article

This study presents an integrated design-to-operation framework for a PV–battery solar highway lighting system supported by IoT-based monitoring for highway-scale deployment. The proposed framework combines pole-level PV-battery sizing, annual energy-reliability simulation, road-level techno-economic optimization, and IoT-based digital monitoring. The Shoubra–Banha Freeway in Egypt, a 40 km corridor with a two-sided lighting arrangement, was selected as the case study. In the pole-level phase, dimming strategies, PV capacities, battery sizes, and battery technologies were evaluated using sequential parametric analysis under a reliability constraint of Loss of Load Probability (LLP) below 1%. The S2 aggressive dimming profile achieved the best operating performance, with an LLP of 0.003, energy reliability of 99.70%, and 2.89 kWh annual unmet load. The minimum feasible PV capacity was 0.8 kW, while the smallest acceptable storage capacity was 4.8 kWh nominal capacity, corresponding to approximately 3.84 kWh usable capacity under an 80% allowable depth of discharge. Among the tested battery technologies, LiFePO₄ achieved the best reliability performance, with an LLP of 0.007 and a 99.25% battery deficit coverage ratio. In the road-level phase, three deployment configurations were compared. Case B, using 12 m pole height and 36 m spacing, was selected as the best-balanced solution, requiring 2224 poles, achieving 99.30% energy reliability, an LLP of 0.007, annual PV generation of 3,178,341 kWh, and total CAPEX of approximately 2.88 × 10⁸ EGP, equivalent to about 5.76 million USD based on an assumed exchange rate of 1 USD = 50 EGP. Lastly, the development of an IoT-based monitoring system design utilizing sector gateways, telemetry variables, alarm conditions, MQTT protocols, and dashboard displays was carried out. The scenario for gateways at 5 km intervals was advised due to its better fault isolation, lower gateway workload, and scalability. This indicates that the suggested approach offers a viable, cost-efficient, and technologically enabled solution for automated solar-powered street lighting systems. Full article

Electric vehicles (EVs) are increasingly regarded as a key component of low-carbon mobility and the sustainable energy transition. However, their large-scale deployment raises challenges that extend beyond vehicle technologies and require a system-level understanding of interactions with power networks, energy resources and users. This paper presents a critical review of the literature published since 2012, examining EV development from an integrated energy perspective that includes vehicle technologies, charging infrastructure, power electronics, grid integration, renewable energy coupling and environmental implications. A structured methodology is used to identify and analyze peer-reviewed studies, with particular emphasis on high-impact review articles that consolidate knowledge across disciplines. The analysis shows that, despite significant technological progress, large-scale EV deployment remains constrained by infrastructure limitations, distribution grid readiness, charging coordination strategies, material availability and socio-technical factors. Simulation-based studies play a central role in anticipating these impacts and informing deployment strategies before real-world implementation. Rather than addressing individual components in isolation, this review highlights interdependencies between technologies, control approaches and energy systems. Based on this synthesis, key research priorities and high-level challenges are identified, providing guidance for future research and policy aimed at enabling EVs to effectively support sustainable ambient energy and mobility systems worldwide deployment. Full article

Micro-Unmanned Aerial Vehicles (micro-UAVs) require compact radar arrays under severe Size, Weight, Power, and Cost (SWaP-C) constraints. Spread Spectrum Digital Beamforming (SSDBF) reduces receiver hardware by multiplexing multiple antenna channels before a shared RF chain, but the receiver-side switching operation folds wideband noise and injects switch-related transient noise before digital demultiplexing. This paper develops a hardware-waveform co-design framework that links the transition density of bipolar spreading sequences to high-frequency code energy and switching-event count, while explicitly distinguishing modulation-induced thermal-noise folding from switch-transient injection. Transition density is used as a physically interpretable design surrogate rather than a sufficient statistic for folded noise, and it is constrained jointly with non-zero-shift cross-correlation to preserve spatial isolation under receiver-side timing skew. An $ϵ$-constrained Greedy Coordinate Space Search ($ϵ$-GCSS) algorithm is proposed to synthesize low-transition-density SSDBF code sets. Commercial circuit-level transient simulations and system-level MATLAB R2024b simulations show that the proposed code set reduces the PRN-like transition-density level from about $0.50$ to about $0.19$, lowers the circuit-simulation-derived integrated IF noise power by $9.63$ dB relative to the Gold/PRN-like baseline, and improves the normalized maximum detection range by $8.2$ percentage points at $K=4$ without adding analog front-end hardware. Full article

Natural disasters can split distribution networks into isolated microgrids, and the remaining local storage is often not enough to keep critical loads supplied until repairs start. Mobile emergency power vehicles (MEPVs) are useful in bridging this gap, but their dispatch is easily distorted when travel time is treated as static and battery charging is treated as constant-rate. This paper studies MEPV routing and load restoration under scenario-based road degradation and non-linear constant-current/constant-voltage (CC-CV) charging. A limited-communication setting is also stated explicitly, since post-disaster information is usually intermittent rather than fully real-time. The resulting scheduling problem is solved by an Improved Adaptive Harmony Search (IAHS) algorithm. IAHS keeps the harmony–memory structure but moves binary load decisions through a continuous latent space before threshold decoding, so that the differential update can use population differences without repeatedly disturbing load variables that have already become stable. Tests on a six-area system show higher average recovery and lower dispersion than HS, AHS, GHS, and AHS with standard BDE. Additional road-degradation sensitivity tests and single-MEPV scale-up tests show that the method remains feasible under stronger road deterioration and that computation time grows in a manageable way as the number of load variables increases. Full article

The rapid expansion of electric vehicle (EV) adoption has increased the demand for efficient charging infrastructure and data-driven approaches for understanding charging behavior. Analyzing charging patterns and identifying abnormal charging sessions are essential for improving charging network reliability, infrastructure utilization, and operational efficiency. This study proposes a comprehensive machine learning framework for EV charging behavior analysis and anomaly detection using real-world charging session data collected from six charging bays. Four charging behavior indicators, namely energy consumption (Usage), charging duration (Duration), average charging output power (Average Output), and Energy Consumption Ratio (ECR), were extracted through a feature engineering process. K-Means clustering was employed to identify distinct user behavior groups, while Principal Component Analysis (PCA) was utilized to visualize cluster separability. Isolation Forest was subsequently applied to detect anomalous charging sessions and investigate abnormal charging behavior patterns. Statistical validation was conducted using Analysis of Variance (ANOVA), and Pearson correlation analysis was performed to examine relationships among charging features and anomaly occurrence. The results identified four distinct charging behavior clusters representing moderate users, regular users, inefficient users, and high-power users. Clustering validation achieved a silhouette score of 0.6086, while PCA retained 89.8% of the total variance using two principal components. An anomaly detection analysis revealed that inefficient charging behavior exhibited the highest anomaly occurrence, whereas regular users demonstrated highly consistent charging patterns. Analysis indicated that average charging output power and ECR were the most influential variables contributing to anomaly identification. ANOVA results confirmed statistically significant differences among all identified clusters (p < 0.001), while correlation analysis demonstrated a strong positive relationship between charging power and charging efficiency (r = 0.95). The anomaly detection framework achieved accuracy, precision, recall, and F1-score of 80.0%. The proposed framework provides a comprehensive approach for EV charging behavior characterization, anomaly detection, and charging infrastructure assessment. The findings can support charging network operators in improving charging efficiency, identifying abnormal charging activities, and enabling data-driven management of EV charging systems. Full article