Search Results (27,943)

In this work, we analyze the arithmetic building blocks of DNA basecalling to motivate runtime approximate computing in bio systems-on-chip (BioSoCs). We propose and characterize a reconfigurable compressor-tree multiplier whose operating mode can be selected at runtime to trade energy for controlled arithmetic error. Using a 45 nm CMOS evaluation flow, the proposed design demonstrates a clear power–accuracy trade-off across 64 operating modes, achieving about a 58–61% reduction in multiplier power (per multiply under fixed V/f) relative to an accurate Wallace baseline, with mean relative error distance (MRED) in the 1.05–2.88% range. At the application level, we outline a first-order noise-propagation model and, consistent with prior approximate-inference studies, note that task-level quality loss is often within a few percent (up to 5%), motivating end-to-end basecalling evaluation. Application-level evaluation on a TinyX3 DNA basecaller—a compact Bonito-based model—shows that the proposed multiplier with measured REV = 0.012 and MRED = 1.98% preserves near-baseline performance, with negligible degradation in sequence identity and relative length at low perturbation levels and only gradual accuracy decline (confirming ≤ 5% accuracy drop) emerging as perturbations increase into the moderate regime. Finally, a processor-level case study using convolution microbenchmarks (kernel footprints 9–49 weights per output) shows an 11% improvement in energy per instruction and a 12% reduction in energy per MAC when integrating the proposed multiplier into an embedded RISC-V execution engine. Full article

Vertical-cavity surface-emitting lasers (VCSELs) have emerged as essential light sources for atomic-precision measurement, quantum-secure communication, high-speed optical transmission, and laser coherent scanning detection, owing to their low power consumption, high-quality beam characteristics, and ease of two-dimensional integration. However, the fundamental limitation on linewidth narrowing in VCSELs arises from their inherently short resonator, resulting in a natural linewidth on the order of 50–100 MHz. This limitation prevents conventional VCSELs from meeting the stringent requirements of advanced applications, making the ultra-narrow linewidth a key focus in optoelectronics research. This review analyzes representative achievements and application scenarios of narrow-linewidth VCSELs, evaluates the merits and limitations of industrial-grade devices, and envisions future directions in next-generation optoelectronic systems. Distinct from existing reviews, it integrates key single-mode fabrication techniques, quantitative linewidth requirements across applications, silicon photonic integration, and scalable manufacturing trends, establishing a complete mechanism–technology–application–industry analytical framework. Full article

Recent advancements in deep learning have shown considerable potential to enhance radar target detection, particularly in improving detection probability under complex environmental conditions. However, existing deep learning approaches largely operate in the real number domain, neglecting the complex-valued nature of radar data, and often inherit vision-oriented architectures that fail to address radar-specific challenges—such as sparse target echoes, the necessity for phase preservation, and constraints imposed by scanning radar systems. Meanwhile, conventional radar signal processing methods, including CA-CFAR, are limited by their dependence on idealized statistical models and often underperform in dynamic and cluttered electromagnetic environments.To overcome these issues, this paper proposes Radar Transformer for Detection (RaTDet), an end-to-end detection network that integrates complex-valued convolutional neural networks (CNNs) and Transformers. RaTDet fully leverages complex-valued data to preserve critical phase and amplitude information, enabling automated feature learning directly from raw radar signals. The model operates effectively with very few pulses, making it suitable for resource-constrained scenarios, and can serve as a pre-trained foundation model for various radar downstream tasks. Experimental results demonstrate that RaTDet achieves excellent detection performance, characterized by high detection probability (Pd) and low false alarm rate (Pfa), outperforming both traditional signal processing and conventional deep learning methods. This work bridges the gap between deep learning and radar signal processing, offering a flexible and powerful network for next-generation radar systems. Full article

High-performance medical segmentation models are often benchmarked on high-end GPUs. Such benchmarks do not provide useful performance insights for point-of-care low-end devices. This work, firstly, posits that to achieve improved clinical adoption of AI-powered segmentation models, especially in reduced manpower settings like rural hospitals, we need benchmarks that provide actionable insights on the degree to which high-performance models address five deployment constraints viz: resource-effectiveness for low-end computing devices, clinically acceptable accuracy, clinically compatible execution times, localization of user data, and user-based finetuning. In this work, five state-of-the-art foundation segmentation models and one target-specific model were systematically evaluated on three multi-organ medical datasets. Furthermore, the best-ranking foundation model and target-specific model were benchmarked on three low-end devices. Our findings show that lightweight foundation models provided the best performance trade-off and are easily user-fine-tuned on custom datasets. Target-specific models provide high accuracy out-of-the-box, but may require significant optimisation to deliver comparably fast execution times and user-based finetuning on low-end devices. The methods and results from this research provide actionable insights on high-performance medical segmentation models for low-end computing devices, as a necessary step towards improved adoption in resource-limited clinical settings. Full article

To address the slow curing and low early strength of conventional modified epoxy emulsified asphalt repair materials, this study introduced steel slag aggregate into epoxy emulsified asphalt mixtures. Experimental techniques including heat absorption–heat transfer rate tests, Marshall stability tests, COMSOL numerical simulation, and scanning electron microscopy (SEM) were adopted to analyze rapid and uniform heating under microwave radiation. The influence of steel slag’s chemical composition, content, and particle size on epoxy curing, asphalt demulsification, and early strength of the mixture was systematically examined. Results show that steel slag containing Fe and Mg elements exhibits higher microwave absorption efficiency. When its content exceeds 15%, the heating rate increases by approximately 0.335 °C/s under the tested conditions. Particles sized 0.6~2.36 mm show better wavelength matching with the applied microwave frequency (2.45 GHz), thereby enhancing absorption. After 140 s of microwave radiation, the core temperature of the mixture reaches 110 °C, which is the appropriate temperature to achieve rapid epoxy curing and synchronous asphalt demulsification. These two processes synergistically form a continuous network structure, thereby improving the compactness and initial laboratory Marshall stability of the mixture. Nevertheless, this study has several limitations. The microwave absorption efficiency depends strongly on the specific mineralogy and Fe/Mg content of steel slag, both of which may vary with source. The conclusions are based on laboratory-scale tests under fixed microwave power and mixture proportions. Despite these limitations, the results demonstrate that steel slag can serve as an effective microwave-absorbing component in epoxy emulsified asphalt mixtures, enabling rapid curing and demulsification to accelerate early strength development. Full article

Geomembranes are extensively used for seepage control in the reservoir of pumped-storage power stations due to their superior deformability, ease of construction, and low cost. The deformation behavior of geomembranes under high hydraulic pressure is of great importance for seepage-control design and operational safety evaluation. Nevertheless, existing hydrostatic pressure resistance tests cannot effectively measure the hydraulic bulging deformation of geomembranes subjected to water pressure. This study proposes a non-contact binocular vision method to quantify the hydraulic bulging deformation of geomembranes. The method combines underwater camera calibration, image enhancement, stereo matching, triangulation, and three-dimensional reconstruction to achieve both visualization and accurate measurement of geomembrane deformation. After experimental validation and accuracy calibration, the proposed method was preliminary applied to four geomembrane materials, including HDPE, LLDPE, PVC, and TPO, under hydraulic loading. The results show that the measurement error is less than 5% in the large-deformation range under medium and high water pressures. The method can effectively capture the hydraulic bulging behavior of geomembranes and accurately characterize the deformation features of different materials under high hydraulic pressure. This study provides a practical technical approach for underwater deformation measurement of geomembranes and supports seepage-control design and operational safety monitoring. Full article

Advancements in infrared imaging technology have introduced a novel perspective for inspecting power transmission lines. Nevertheless, the inherent low contrast and indistinct edges of infrared images present significant challenges, rendering the direct application of traditional semantic segmentation algorithms unsatisfactory. To mitigate this problem, we propose a multi-neighborhood densely connected network architecture. This framework incorporates two pivotal modules: the Multi-Head Squeeze-and-Excitation (MHSE) module and the Multi-Neighborhood Feature Fusion (MNFF) module. The MHSE enhances local feature representations by capturing nuanced feature interactions, thereby alleviating the issue of imbalanced global feature weight distribution. The MNFF aggregates feature data from multiple adjacent nodes at each node’s input, which not only facilitates the integration of multi-scale target features but also leverages neighborhood information to precisely localize and amplify features within specific regions. Furthermore, we have built the first Infrared Dataset of Power Transmission Line Suspension Clamp (CLAMPTISS) to substantiate our approach. Empirical evidence demonstrates that our proposed network surpasses state-of-the-art networks across three key metrics: the mean Intersection over Union (mIoU) and localization accuracy (Pd) have increased by 8.3% and 13.3%, respectively, while the false alarm rate (Fa) has decreased by 38.2%. Full article

Human Activity Recognition (HAR) from inertial sensors must run continuously on battery-powered wearables under tight latency, memory, and energy budgets. While tiny Transformers can be effective on inertial time series, end-to-end co-design across quantized inference and heterogeneous low-power platforms remains underexplored. We present STORM (Small Transformer for On-node Recognition of Motion), a deployment-oriented [round-mode=places, round-precision=1]19.7k-parameter 1D Transformer co-designed with X-HEEP, an open-source low-power single-core RISC-V SoC, and a tightly coupled streaming CGRA for nonlinear primitives (e.g., softmax). We build a cross-source 8-class benchmark by harmonizing 3 public datasets under a stringent, deployment-aligned protocol that exposes both cross-subject and cross-source shift. Using 1.280 s windows with 0.640 s stride, the protocol models continuous on-node HAR under cross-dataset generalization. After quantization-aware training and INT8 C inference export, STORM achieves [round-mode=places, round-precision=3]0.799/[round-mode=places, round-precision=3]0.801 accuracy/macro-F1 on this benchmark. Deployed on an FPGA prototype of X-HEEP with the streaming CGRA backend, STORM requires round(6739790/ (100* 1000000)* 1000, 1) ms per inference at 100 MHz, while activity-based power analysis estimates a total inference energy of 632.4 $\mathsf{\mu }$J, satisfying the stride-driven real-time constraint. These results support the practical viability of compact attention-based HAR on low-power wearable-class embedded platforms. Full article

With a modest estimate of 11 million workers and high greenhouse gas emissions, the Indian brick sector is a relevant study for understanding how low-carbon energy transition impacts justice for the society, environment, and livelihoods. This empirical article provides an analysis of the ongoing policy-driven energy efficiency transition and justice trade-offs and benefits in the brick production sector in the state of Bihar. The transition is explored in a larger framework of power relations and vulnerability to determine whether the policies enable or challenge transformative justice for the labour force, nature and future generations. Present policies focus on regulations and financial incentives relevant for entrepreneurs with pre-existing skills, network and financial resources. Further, present policy narratives lack attention to mechanisms that reproduce the socio-economic inequality of the brick labour force, and implications for balancing different livelihood and environmental objectives. We conclude that the findings emphasise the need for integrating a wider variety of social dimensions and relevant support schemes to overcome inequality barriers and safeguard the environment for future generations. Full article

This work proposes a polarization-insensitive scalable wide-angle metasurface array for highly efficient ambient energy harvesting in the 5.8 GHz Wi-Fi band. Inspired by dipole antenna principles, we design an asymmetric planar orthogonal dipole-based metasurface featuring monolithic integration of Schottky diodes (HSMS-2860) at unit cell feed gaps. This novel direct-impedance-matching strategy eliminates conventional matching networks, reducing energy conversion losses while enabling 99% radiation-to-AC efficiency across all polarization angles at 5.8 GHz. The coplanar architecture interconnects metasurface unit cells via inductors, simultaneously establishing low-loss DC channels and suppressing RF leakage. Fabricated as a 5 × 5 array, the prototype achieves 77.9% peak RF-to-DC efficiency with polarization insensitivity at an incident power of 25 dBm. Furthermore, with incident powers of 15 dBm and 20 dBm, the proposed metasurface array attained RF-to-DC conversion efficiencies exceeding 40% and 60%, respectively. This result indicates that the array is capable of achieving high energy harvesting efficiency across a broad power range. This scalable, drill-free, and polarization-insensitive design demonstrates strong potential for harvesting ambient RF energy in real-world multipath environments. Full article

The common root characteristic of CO₂ and air pollutants in the power industry, both derived from fossil fuel combustion, provides a natural basis for their synergistic emission reduction. However, existing studies suffer from the lack of a multi-pollutant synergistic evaluation system and an imperfect emission reduction technology database, which hinder their ability to support low-cost and high-efficiency emission reduction practices in the industry. Targeting the minimization of synergistic emission reduction costs and the maximization of emission reduction effects, this study integrated the process and economic parameters of 11 power generation technologies and 55 pollutant control technologies to establish a full-chain energy conservation and emission reduction technology database for the power industry, through literature research, industry surveys, and data mining. Based on the definition of pollution equivalent in the Environmental Protection Tax Law, we innovatively developed an air pollutant equivalent normalization evaluation method and constructed a two-dimensional coordinate system comprehensive evaluation system for CO₂ and air pollutants, enabling quantitative analysis and visual evaluation of the synergistic emission reduction effects of various technologies. The results show that new energy power generation technologies such as nuclear power and wind power, as well as O₂/CO₂ cycle combustion, ammonia-based desulfurization, and SNCR-SCR combined reduction technologies, exhibit excellent synergistic emission reduction performance for CO₂ and multiple pollutants. In contrast, some conventional pollutant control technologies, such as the limestone-gypsum method and traditional electrostatic precipitation, have significant CO₂ emission increase antagonistic effects. This study also completed the two-dimensional classification of 66 emission reduction technologies based on “emission reduction efficiency-economic cost”, identified application scenarios for different types of technologies, and proposed optimized paths for synergistic emission reduction adapted to the development of the power industry. The research findings fill the gap in quantitative standards for multi-pollutant synergistic emission reduction, provide theoretical support and detailed technical references for emission reduction technology selection and environmental policy formulation in the power industry, and help the industry achieve the dual development requirements of the “double carbon” goal and air quality improvement. Full article

Hollow-core anti-resonant fibers (HC-ARFs) have emerged as a promising platform for next-generation optical systems, offering attractive advantages in low-latency, low-nonlinearity, and high-power handling. However, the development of high-performance functional components, such as polarization beam splitters (PBSs), within this platform faces a significant challenge: the simultaneous achievement of ultra-broad bandwidth, compact device length, high polarization selectivity, and strict single-mode operation remains elusive. To address this challenge, we propose and numerically investigate a novel dual hollow-core anti-resonant fiber (DHC-ARF) based on a hybrid nodal–nodeless architecture. The design integrates three functional units: (1) an asymmetric nested semi-elliptical tube pair that defines the dual cores and serves as the primary wavelength-insensitive coupling channel; (2) nodeless nested circular tubes positioned peripherally to effectively suppress higher-order mode propagation while maintaining low fundamental mode loss; and (3) a selective localized thick-wall region that introduces a polarization-dependent perturbation to the x-polarized supermodes, whose observed behavior is physically consistent with a phase-mismatch effect associated with anti-crossing-like modal interaction near the target wavelength. Through synergistic optimization of these elements, we numerically demonstrate a combination of performance metrics. At the central wavelength of 1.55 µm, the coupling length for the y-polarization (Lcy) is reduced to 6.35 cm, while the coupling length ratio (CLR = $L_{\mathrm{cx}}$/$L_{cy}$) equals 2.001, indicating effective polarization selectivity. Consequently, a device length of 12.7 cm is numerically demonstrated, which is comparable to or shorter than existing ultra-broadband DHC-ARF PBS designs. The proposed PBS is numerically shown to exhibit an ultra-broad bandwidth of 460 nm (spanning 1320 to 1780 nm) with a polarization extinction ratio better than 20 dB, peaking at 53 dB. Furthermore, HOMER (λ) remains above 100 throughout the operating band and exceeds 200 over most of the band, indicating robust single-mode operation. This work not only presents a PBS design with competitive overall performance but also provides a versatile structural paradigm for developing functional components in hollow-core fiber-based integrated optical systems for high-speed communications and precision sensing. It should be noted that this work is based on numerical simulations, and experimental fabrication and validation will be pursued in future work. Full article

Energy consumption is a critical concern for Internet of Things (IoT) platforms lacking abundant resources, particularly for swarm robotic systems that rely on numerous devices operating collaboratively over extended periods. This study presents a comprehensive design strategy for improving processing and communication to enhance system efficiency and reduce energy consumption. We incorporate energy harvesting (photovoltaic and RF), dynamic power management, and energy-efficient communication protocols (e.g., duty cycle, power control, data compression) into two complementary platforms built for swarm robotics: MCU-based nodes (TI MSP430 with LoRa transceiver), which serve as the experimental prototype for validating energy-aware communication, compression, and scheduling mechanisms; edge platforms (Jetson Nano and TX2), which are used for high-level power profiling and system-level evaluation, particularly for computation intensive workloads and comparative analysis. Our technique involves analyzing the device’s energy usage and harvesting processes, developing efficient communication protocols, and validating the system through simulations and hardware prototypes. Experimental results under outdoor and indoor conditions show that the device maintains an energy neutrality ratio well above unity, even with limited ambient energy. Key findings include significant reductions in energy per bit transmitted and reliable long-term operation. These insights pave the way for deploying swarms of autonomous IoT-based robots with minimal maintenance and maximal longevity. Full article

Smart care applications impose increasingly stringent requirements on low-latency execution, privacy preservation, and continuous monitoring. These requirements are driving intelligent services from cloud-centric architectures toward edge-side deployment. When multiple care-related workloads are deployed on resource-constrained edge devices, performance bottlenecks arise not only from model inference itself, but also from process scheduling, inter-process communication, and resource coordination overhead. To address this issue, this paper presents a task scheduling and management platform for multi-workload smart elderly care on a single pure-edge CPU–TPU heterogeneous node. The platform adopts a shared-memory and event-driven synchronization mechanism together with fine-grained process partitioning, thereby establishing a data-sharing and runtime-coordination framework for concurrent multi-workload execution. To evaluate the effectiveness of the proposed platform, experiments were conducted under single-workload, multi-workload, multi-resolution, and long-term runtime settings. The results show that, compared with two baseline schemes, the proposed platform improves the average frame rate by 66.7% and 71.1%, reduces net memory usage by 96.3% and 45.3%, and lowers net power consumption by 46.8% and 37.7%, respectively, under the single-workload setting. Under 10 concurrent workload instances, the system still maintains a stable frame rate of 42.03 ± 0.73 fps, demonstrating strong concurrency scalability. Multi-resolution experiments further indicate that the performance degradation at higher resolutions is mainly constrained by the front-end data supply stage. A continuous 10-day runtime experiment additionally verifies the sustained operating capability and resource stability of the platform under pure-edge deployment. These results demonstrate that node-level shared-memory and event-driven coordination can effectively improve the execution efficiency, scalability, and stability of real-time multi-workload analytics on such pure-edge heterogeneous nodes, providing a useful basis for future extensions to multi-node edge environments and edge–cloud collaborative task scheduling. Full article

Electronic nose platforms based on metal-oxide (MOX) sensors offer potential for low-power gas classification under dynamic operating conditions. This study evaluates a BME688-based digital nose configured with a temperature-modulated heater profile (HP-354) and reduced duty cycle (RDC-5-10) for binary ethylene presence classification in fruit headspace. Seven climacteric fruit types were sealed in bags to allow natural ethylene accumulation and were sampled across multiple sessions over a two-week period. A structured alternating protocol between fruit headspace (Class A) and neutral air (Class B) generated 21 ethylene sessions and 23 neutral-air sessions, comprising 38,882 individual thermal scan cycles (~10 s per cycle). Each full heater cycle was treated as a training instance within BME AI-Studio. A supervised neural-network classifier trained on 70% of cycle-level data achieved 92.9% overall accuracy with a macro F1 score of 91.9% on validation data. Results demonstrate that temperature-modulated MOX signatures enable robust discrimination of biologically generated ethylene from baseline air under realistic headspace variability. This study demonstrated classification feasibility under naturally accumulated fruit emissions while highlighting the need for future concentration-resolved calibration studies. Full article