Search Results (241)

The increasing computational demands of deep neural network inference drive the need for energy-efficient hardware accelerators that minimize data movement between memory and processing units. Compute-in-memory (CIM) architectures address this bottleneck by embedding computation directly within memory arrays, reducing the overhead of repeated weight transfers in conventional von Neumann systems. Conventional 6T SRAM-based digital CIM bitcells incur significant transistor overhead as arrays scale, motivating exploration of reduced-transistor bitcell alternatives. We propose a compact 4T+2T SRAM-based digital CIM bitcell implemented in 45 nm CMOS, combining a 4T SRAM storage cell with a 2T multiplier for bitwise multiply-and-accumulate (MAC) operations. The proposed design reduces transistor count from 8 to 6 compared to the 6T+2T reference, lowering parasitic capacitance and hardware overhead without compromising memory or computation functionality. Transient simulations confirm correct write, read, and CIM operations. The bitcell achieves a read delay of 26.91 ps, read power of 1.351 nW, and read energy of 0.005403 fJ—reductions of 98.7%, 86.5%, and 73.1% over the 6T+2T reference, respectively. For CIM operation, bitwise multiplication power decreases from 1.772 µW to 0.8014 µW and energy from 10.63 fJ to 4.808 fJ, representing a 54.8% reduction in both metrics, with only a marginal CIM delay increase of 3.13 ps. Monte Carlo analysis across 100 samples confirms robust write behavior under process variation, with write delay ranging from 55.02 to 69.59 ps and write energy from 0.05870 to 0.06557 fJ. Static noise margin analysis yields an SNM of 83.7 mV under nominal conditions, confirming stable data retention. These results demonstrate that the proposed 4T+2T bitcell offers strong transistor efficiency, energy savings, and computational correctness, making it a promising candidate for area-efficient digital CIM architectures targeting edge AI inference. Full article

Very High Frequency (VHF) radio communication systems face significant challenges in modern electromagnetic environments, including spectrum congestion, dynamic interference, and varying channel conditions. Existing adaptive approaches rely on static rule-based switching or single-cycle optimization, which cannot accumulate operational experience across decision cycles. This paper proposes a digital twin-enabled online learning framework (DT-MAB) for adaptive waveform selection in tactical VHF communication. The framework employs a contextual multi-armed bandit algorithm (Lin-UCB) that continuously learns the mapping from channel conditions to optimal configurations, with the digital twin serving as a virtual exploration sandbox that screens candidate configurations before physical deployment—preventing link disruptions during exploratory actions. An expanded configuration space of 63 candidates (7 waveforms × 3 MAC protocols × 3 power levels) is constructed, and a hierarchical performance evaluation model combining voice quality, bit error rate, communication delay, and transmission range is developed using the Analytic Hierarchy Process (AHP) as the reward function for online learning. Experimental results across 10 random seeds demonstrate that DT-MAB achieves the lowest mean cumulative regret, reducing regret by 29% relative to MAB without a digital twin and by 16.5% relative to PSO-based optimization on average. Ablation experiments confirm that removing virtual exploration increases performance drop events by 49% (from 250 ± 79 to 373 ± 6), demonstrating that the digital twin is a functionally indispensable component of the online learning architecture. Full article

Time synchronization is a fundamental requirement for the reliable operation of Control and Data Acquisition Systems (CODASs) in large-scale fusion experiments such as the Divertor Tokamak Test (DTT). Distributed diagnostics, sensors, and control subsystems must share a unified time reference to guarantee deterministic data acquisition and stable plasma control. This paper presents the FPGA-based implementation and evaluation of a synchronization system that combines the IEEE 1588 Precision Time Protocol (PTP) with Pulse Per Second (PPS) generation. The proposed platform is built on Zynq UltraScale+ Kria KR260 System-on-Modules (SOMs) running a customized PetaLinux distribution with LinuxPTP utilities. Hardware timestamping is enabled through the integrated Timestamping Unit (TSU) in the Gigabit Ethernet MAC, while a hardware logic module generates PPS signals from the synchronized PTP clock. Experimental validation demonstrates nanosecond-level synchronization with an RMS timing accuracy of approximately 8.5 ns. A detailed analysis of PPS offset, network path delay, and servo adjustments confirms stability of the timing system. The proposed design offers a low-cost, flexible, fully customizable and controllable solution for distributed diagnostic and control systems in fusion facilities. Full article

Full-duplex (FD) technology holds great promise for enhancing the spectral efficiency of Mobile Ad Hoc Networks (MANETs) and Wireless Sensor Networks (WSNs). However, the practical performance gain of FD over Half-Duplex (HD) is highly sensitive to the dynamic nature of traffic loads and residual self-interference. Existing Optimal Dynamic Selection Strategies (ODSS) often rely on static workload assumptions within a single time window, failing to capture long-term traffic fluctuations. Consequently, applying instantaneous switching strategies in highly bursty environments necessitates excessively frequent mode switching (e.g., the switching frequency can approach the total number of time windows), incurring prohibitive signaling overhead and unignorable MAC-layer adaptation delays. To overcome these concrete bottlenecks, this paper proposes a comprehensive traffic-aware adaptive duplex mode selection framework. First, we model the multi-scale dynamic workload using Dynamic Activated Probability in Short-term (DAPS) and Long-term (DAPL), effectively characterizing both bursty traffic (via Beta distribution) and Markov-modulated stable traffic. Second, by integrating physical layer performance analysis, we define the Break-even Workload Point (BWP) to partition traffic into Oversaturated (OZ) and Unsaturated (UZ) Workload Zones (WZs). Furthermore, to handle unknown future traffic with low complexity, we propose the Pre-scheduling Duplex selection based on the Workload zone Estimation (PDWE) algorithm. PDWE leverages a Hidden Markov Model (HMM) combined with a Rollout algorithm to estimate hidden traffic states and adaptively pre-schedule duplex modes. Simulation results demonstrate that the proposed strategy achieves near-optimal throughput (approximately 91% of the ideal ODSS) while reducing the duplex switching frequency by two orders of magnitude compared to instantaneous switching strategies. This approach offers a robust cross-layer solution for next-generation self-organizing networks. Full article

Energy efficiency has become a primary bottleneck in hardware platforms supporting machine learning workloads, particularly as modern inference and training tasks demand sustained high-throughput computation. This challenge is further amplified in energy-harvesting and intermittently powered systems, where the available energy budget varies over time. This work introduces a run-time configurable multiply–accumulate (MAC) architecture that dynamically adjusts arithmetic precision to match instantaneous energy availability. The proposed design relies on an internally adaptive multiplier based on bit-level logic compression, enabling controlled modulation of power consumption while preserving numerical robustness. Crucially, the MAC maintains a fixed external operand interface, allowing for seamless precision adaptation without operand reformulation or datapath disruption. The architecture is implemented in System Verilog and evaluated using both ASIC synthesis in a 90 nm CMOS technology and FPGA deployment. Experimental results demonstrate approximately a fourfold improvement in power–delay product (PDP) relative to full-precision operation, with only limited degradation in inference accuracy. Full article

Sine and cosine wave synthesis is utilized for generating sinusoidal-like values in the digital domain. While this task is commonly handled through software, dedicated hardware like Direct Digital Synthesis (DDS) is also available. However, both methods rely on memory resources, such as look-up tables and Read-Only Memories (ROMs), which face latency limitations related to additional memory access times on top of additional $Si$ area. With the advent of real-time arithmetic for sine wave approximation, this paper presents a digital module that employs iterative multiply-accumulate (MAC) operations for sine and cosine synthesis. To support the integration of this module into Systems-on-Chip (SoCs), Field-Programmable Gate Arrays (FPGAs), and standalone Application-Specific Integrated Circuits (ASICs), a comprehensive figure of merit (FoM) comparison against various ROM-less methods is provided. When implemented on a Xilinx (AMD) XC7A100T-3CSG324 FPGA, the proposed architecture compared to other ROM-less solutions like the Taylor approximation, achieves 80.80% lower resource utilization, 80.89% reduced propagation delay, and 36.66% higher accuracy in sine and cosine wave approximation, both operating as 32-bit systems with one sample per clock cycle. Furthermore, the proposed sine accelerator, accompanying control and communication IPs, and custom firmware were deployed on an FPGA-based function generator platform and experimentally validated. Full article

Retinol-binding protein 4 (RBP4), an adipokine secreted by adipose tissues, has been implicated in metabolic inflammation and insulin resistance. Type 2 diabetes (T2D) is a recognized risk factor for osteoarthritis, with both conditions characterized by chronic low-grade inflammation, suggesting potential links between metabolic disorder and joint degeneration. This study aimed to investigate whether inflammatory and metabolic stresses regulate RBP4 expression and function in joint-related cells. Murine immature chondrocyte cells (iMACs) and the mouse AT805 teratocarcinoma cell line, clone 5, that differentiates into chondrogenic cells (ATDC5), were used as in vitro models for chondrocyte cells. Rbp4 mRNA expression increased during differentiation of iMACs, with 3.6- and 2.2-fold elevations observed on days 7 and 14, respectively (p < 0.01 vs. undifferentiated controls). Inflammatory stimulation with interleukin-6 (IL-6) significantly increased Rbp4 mRNA expression in ATDC5 cells (p < 0.05 vs. vehicle), along with elevated expression of catabolic and inflammatory mediators, including monocyte chemoattractant protein-1 (Mcp1), cyclooxygenase-2 (Cox2), and matrix metalloproteinase-3 (Mmp3) (p < 0.05 vs. vehicle). Pharmacological inhibition of RBP4 using fenretinide (FEN) attenuated chondrogenic differentiation marker expression, reduced glycosaminoglycan synthesis during chondrogenic differentiation, and mitigated high-glucose-induced catabolic responses, as indicated by reduced Mcp2 (p = 0.04) and Mmp13 (p = 0.01) expression in ATDC5 cells treated with FEN compared with cells treated with the vehicle under high-glucose conditions. Furthermore, in RAW 264.7 cells, a murine macrophage cell line commonly used as an in vitro model for osteoclastogenesis, FEN significantly reduced the expression of osteoclast differentiation markers, dendritic cell-specific transmembrane protein (DC-Stamp), nuclear factor of activated T-cells, cytoplasmic 1 (Nf-atc1), cathepsin k (Cath.k), and tartrate-resistant acid phosphatase (Trap) under osteoclastogenic conditions (p < 0.01 vs. vehicle). Collectively, these findings suggest that RBP4 functions as a metabolic–inflammatory mediator influencing both cartilage and bone-remodeling processes. This study reveals a previously unrecognized role of RBP4 in regulating osteoclast-associated pathways. Targeting RBP4 may, therefore, represent a promising therapeutic strategy for delaying or preventing osteoarthritis progression, particularly in metabolically compromised conditions. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

Ad hoc cognitive radio-enabled Industrial Internet of Things (CR-IIoT) networks offer dynamic spectrum access (DSA) to mitigate the spectrum shortage in wireless communication. However, spectrum utilization is limited by the spectrum availability and resource constraints. In the ad hoc CR-IIoT context, this challenge is further complicated by bandwidth fragmentation arising from small IIoT packet transmissions within primary user (PU) slots. For resource-constrained ad hoc CR-IIoT networks, a medium access control (MAC) scheme is essential to enable opportunistic channel access with a low computational complexity. This work proposes a lightweight DTDMA-assisted MAC scheme (LDCRM) to minimize the queuing delay and maximize transmission opportunities. LDCRM employs a lightweight channel-selection mechanism, an adaptive minislot duration strategy, and spectrum-energy-aware distributed clustering to optimize both energy and spectrum utilization. DTDMA scheduling was formulated using a multiple knapsack problem (MKP) framework and solved using a greedy heuristic to minimize the queuing delay with a low computational overhead. The simulation results under an ON/OFF PU-sensing model showed that LDCRM outperformed CogLEACH and DPPST achieving up to 89.96% lower queuing delay, maintaining a higher packet delivery ratio (between 58.47 and 92.48%) and achieving near-optimal utilization of the minislot and bandwidth. An experimental evaluation of the clustering stability and fairness indicated a 56.25% extended network lifetime compared to that of E-CogLEACH. These results demonstrate LDCRM’s scalability and robustness for Industry 4.0 deployments. Full article

Applications for intelligent cooperative Unmanned Aerial Vehicle (UAV) swarms are rapidly expanding. Efficient and reliable communication is critical for realizing this swarm intelligence, especially in remote areas lacking infrastructure where ad hoc networking is a prevalent approach. However, in such long-distance scenarios, significant propagation delays pose a fundamental challenge to Medium Access Control (MAC) protocols like carrier sense multiple access with collision avoidance (CSMA/CA). This paper theoretically compares random backoff and p-persistent to determine the optimal strategy for these conditions. We present analytical models for both strategies. The model for random backoff reveals its optimal performance is dependent on network topology, making it ill-suited for dynamic swarms. In contrast, our model for p-persistent yields an optimal transmission probability that is independent of the network topology. Simulation results validate our models, showing p-persistent achieves significantly higher throughput (over 40% improvement in an 80-node swarm). We conclude that the topology-independent characteristic of p-persistent makes it a more feasible, more robust, and superior solution for long-distance, dynamic UAV swarm networks. Full article

Pivotal Response Treatment (PRT) is a naturalistic developmental behavioral intervention designed to strengthen autistic children’s social communication skills. Few studies have examined which children benefit the most from PRT and which characteristics are associated with meaningful progress. We analyzed data from 23 children with autism and significant language delay who had been randomized to receive PRT in a previously completed 24-week randomized controlled trial of parent training and clinician-delivered intervention. Participants were categorized as intervention responders and non-responders based on the demonstration of meaningful improvement (or lack thereof) in social communication using the MacArthur-Bates Communicative Development Inventories (MCDI) Reliable Change index scores and clinician determination based on review of language samples and the Clinical Global Impressions—Improvement Scale (CGI). Baseline child characteristics associated with being a responder were assessed. Sixteen participants were responders on the language sample, ten on the MCDI, and sixteen on the CGI. Nine were consistent responders across all three measures; six were consistent non-responders. Verbal ability at baseline was associated with being a responder across all measures. In our small sample, baseline verbal ability was associated with being a responder to PRT, though categorization as a responder differed somewhat based on outcome measure. Future research should explore responder profiles specifically in children who are nonspeaking to inform the development of more effective supports for this group. Full article

In intelligent meter reading and other applications, power line communication can use relay technology to solve the problem of cross-station or long-distance reliable communication. This study investigates the combined impact of the physical and Media Access Control (MAC) layers on power line relay communication system performance. To this end, cross-layer modeling, optimization, and simulation analysis integrating both layers are conducted. Based on the CSMA algorithm of IEEE 1901 protocol, a cross-layer performance analysis model of two-hop relay power line communication system is established considering the influence of non-ideal channel transmission at physical layer and competitive access at MAC layer on system performance. In order to reduce the high collision probability caused by two competitions of packets in the above scheme, an improved two-hop transmission algorithm based on CSMA-TDMA is proposed. The cross-layer performance of the system under different single-hop and two-hop schemes is compared, and the mechanism of how parameters such as the MAC layer and the physical layer affect the cross-layer performance of the power line communication system is analyzed. And the optimal power allocation factor is obtained by using the sequential quadratic programming method for the joint system throughput and packet loss rate optimization model with the two-hop power constraint. Simulation results show that the two-hop transmission scheme based on CSMA-TDMA can avoid the second-hop competition and backoff process, and has better performance in terms of throughput, packet loss rate, and delay. Full article

This paper presents a resilient and energy-efficient framework for jamming mitigation in cluster-based wireless sensor networks (WSNs), addressing a critical vulnerability in hostile or interference-prone environments. The proposed approa ch integrates dynamic cluster reorganization, adaptive MAC-layer behavior, and multipath routing strategies to restore communication capabilities and sustain network functionality under jamming conditions. The framework is evaluated across heterogeneous topologies using Zigbee and Bluetooth Low Energy (BLE); both stacks were validated in a physical testbed with matched jammer and traffic conditions, while simulation was used solely to tune parameters and support sensitivity analyses. Results demonstrate significant improvements in Packet Delivery Ratio, end-to-end delay, energy consumption, and retransmission rate, with BLE showing particularly high resilience when combined with the mitigation mechanism. Furthermore, a comparative analysis of routing protocols including AODV, GAF, and LEACH reveals that hierarchical protocols achieve superior performance when integrated with the proposed method. This framework has broader applicability in mission-critical IoT domains, including environmental monitoring, industrial automation, and healthcare systems. The findings confirm that the framework offers a scalable and protocol-agnostic defense mechanism, with potential applicability in mission-critical and interference-sensitive IoT deployments. Full article

Operating systems such as Windows, Linux, and macOS include built-in commands that enable administrators to perform essential tasks. These same commands can be exploited by attackers for malicious purposes that may go undetected by traditional security solutions. This research identifies an unmitigated risk of misuse of a standard command to disconnect network services on victim devices. Thus, we developed a novel Proof-of-Concept (PoC) malware named DCmal-2025 and documented every step of its lifecycle, including the core idea of the malware, its development, impact, analysis, and possible countermeasures. The proposed DCmal-2025 malware can cause a Denial-of-Service (DoS) condition without exploiting any software vulnerabilities; instead, it misuses legitimate standard commands and manipulates the routing table to achieve this. We developed two types of DCmal-2025: one that triggers a DoS immediately and another that initiates it after a predefined delay before restoring connectivity. This study evaluated 72 antivirus detection rates of two malware types (DCmal-2025 Type 1 and Type 2) written in C and Rust using VirusTotal. The source code for both types was undetected by any of the antivirus engines. However, after compiling the source code into executable files, only some Windows executables were flagged by general keywords unrelated to DCmal-2024 behaviour; Linux executables remained undetected. Rust significantly reduced detection rates compared to C—from 7.04% to 1.39% for Type 1 and from 9.72% to 4.17% for Type 2. An educational institution was chosen as a case study. The institution’s network topology was simulated using the GNS3 simulator. The result of the case study reveals that both malware types could cause a successful DoS attack by disconnecting targeted devices from all network-based services. The findings underscore the need for enhanced detection methods and heightened awareness that unexplained network disconnections may be caused by undetected malware, such as DCmal-2025. Full article

Perfluorooctane sulfonate (PFOS), a member of the per- and polyfluoroalkyl substance (PFAS) family, has been associated with adverse health effects, including potential links to autism spectrum disorder (ASD). This study investigates the impact of PFOS on metabolic, immunologic and behavioral profiles in BTBR-mt^B6 mice, a mouse strain that models ASD, to provide insights into the role of PFOS in ASD development and related health concerns. Three-month-old male and female BTBR-mt^B6 mice were divided into two groups (n = 6) and received daily administration of either 1 mg/kg PFOS or vehicle over a three-month period by gavage. Metabolic assessments included measurements of body weight and weekly blood glucose levels, glucose and insulin tolerance tests, organ weights, and body compositions (free fluid, fat and lean tissue). Immune profiling was conducted via flow cytometric analysis of splenic leukocytes, while behavioral evaluations included grooming, sniffing, and three-chamber social interaction tests. PFOS exposure disrupted glucose homeostasis, with both sexes exhibiting elevated blood glucose levels. Male mice showed impaired glucose tolerance, delayed glucose level recovery, and increased insulin resistance, while females displayed decreased insulin resistance. Additionally, PFOS exposure led to liver enlargement in both sexes. Behavioral assessments revealed heightened grooming in PFOS-treated males, commonly interpreted as stress- or ASD-related repetitive behaviors, whereas females exhibited reduced grooming, reflecting altered behavioral responses to exposure. Immune alterations were also sex specific. PFOS-treated males exhibited decreased granulocytes, increased macrophages, and enhanced surface expressions of B220 and CD40L. PFOS-treated females showed increased macrophages, B-cells, cytotoxic T-cells and CD25⁺ T-cell subsets, with enhanced surface expression of B220 and CD8, and reduced surface expression of Mac-3. In addition, PFOS exposure reduced spleen weight in females. Taken together, PFOS exposure induced significant physiological and behavioral changes in BTBR-mt^B6 mice, with sex-specific differences observed. These results raise concern that PFASs may contribute to the development or exacerbation of metabolic, immune and neurodevelopmental disorders, highlighting the need for sex-specific human risk assessment in environmental toxicology. Full article