Search Results (28)

We develop the binary-shadow method for exact CNOT counting and apply it to arbitrary wire permutations. The Heisenberg evolution of rotated local Z observables converts every CNOT gate into an elementary transvection over ${\mathbb{F}}_2$, and for a wire permutation, the resulting binary shadow is rigid: it must equal the associated permutation matrix. This reduces the exact CNOT cost of a wire permutation in the CNOT+local model to the transvection length of its permutation matrix. The remaining problem is classical. The relevant mathematical input is the transvection-length theory of permutation matrices, or equivalently, the CNOT-only synthesis of permutation circuits. Combining the binary-shadow reduction with the graph-theoretic link-middle-cut theorem for cycle matrices yields an exact formula: if $\sigma \in S_n$ has $c\left(\sigma \right)$ disjoint cycles, then $\mathrm{CNOT-cost}\left(W_{\sigma }\right)={\mathsf{\ell }}_{\mathrm{tr}}\left(P_{\sigma }\right)=3\left(n-c\left(\sigma \right)\right).$ The novelty is therefore not the CNOT-only permutation formula by itself, but the transfer of that exact lower bound to the CNOT+local model: arbitrary one-qubit gates may rotate the local Pauli axes, but they cannot reduce the CNOT count of a wire permutation. In particular, the n-qubit cyclic SWAP gate $S_n|x_1{⟩}_1|x_2{⟩}_2\cdots |x_n{⟩}_n=|x_n{⟩}_1|x_1{⟩}_2\cdots {|x_{n-1}⟩}_n$ requires exactly $3(n-1)$ CNOT gates, even when arbitrary one-qubit gates are allowed at zero cost. Thus, the exact values for $n=2,3,4,5,6,\dots$ are $3,6,9,12,15,\dots$. We also give explicit optimal factorizations for $n=4$ and $n=5$, and show more generally that each additional wire in a cyclic shift costs exactly three more CNOT gates. Full article

Surge reliability is a crucial aspect of silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) reliability. This study investigates the degradation behavior and mechanisms of switching characteristics in 1.2 kV planar-gate SiC MOSFETs under repetitive surge current. A surge current test platform is established to conduct surge tests on the device, while monitoring the evolution of its switching characteristics. The results indicate that after 4000 surge current cycles, the device’s turn-on delay time (t_d(on)), rise time (t_r), and turn-on loss (E_ON) show no significant changes. In contrast, the turn-off delay time (t_d(off)), fall time (t_f), and turn-off loss (E_OFF) increase by 9%, 7.5%, and 8.3%, respectively. Switching characteristics variations are closely linked to the reduction in threshold voltage (V_TH) and the increase in gate-source capacitance (C_GS) and gate-drain capacitance (C_GD). The degradation of these parameters stems from the accumulation of positive trapped charge in the gate oxide layer above the channel and junction field-effect transistor (JFET) region. The increase in charges results from the combined effects of negative gate bias and cyclic high temperature induced by repetitive surge current. This study provides a theoretical basis for the comprehensive understanding of the impact of surge current on SiC MOSFET performance. Full article

Hyperspectral image classification (HSIC) is a crucial task for remote sensing applications, requiring accurate pixel-level labeling while effectively capturing both spectral and spatial information. Traditional convolutional neural network architectures often struggle to balance local texture detail and global contextual consistency, and existing neural architecture search (NAS) methods rarely incorporate attention mechanisms, limiting their performance. To address these challenges, this study proposes a multi-scale Transformer-based NAS framework (TR-NAS) for fine-grained hyperspectral image classification. The framework combines local cube sampling, shallow and deep multi-scale convolutions, and a searchable Transformer module that adaptively selects global, local window, and multi-scale attention operators. Lightweight enhanced convolution operators, including dual-gated (DG-Conv) and mixed depthwise (MixConv) convolutions, are incorporated to improve spectral discrimination and scale robustness. Extensive experiments on the PU and Hanchuan datasets demonstrate that TR-NAS achieves superior classification accuracy, stability, and boundary consistency compared to traditional methods and existing NAS architectures, showing improved robustness to spectral similarity and spatial heterogeneity in complex remote sensing scenes. Full article

Continuous-variable quantum communication (CVQC) operates under finite-resolution inference (finite data windows, calibration uncertainty, and estimator tolerances) and hardware control/readout limits that can be exploited by structured and adversarial disturbances. We study a feedback-inspired phase-space modulation strategy for implementation-layer resilience under DoS-like receiver-observable stress (e.g., fluctuation inflation, phase reference destabilization, or interface non-idealities), rather than proposing a protocol-level security proof. We propose a phase-first framework in which the defender selects a phase-space rotation angle $\theta$ (and, in principle, a squeezing parameter r) to minimize a receiver-observable centered second-moment degradation proxy, emphasizing containment rather than disturbance inversion. Because platforms expose different native observables, we evaluate phase-first modulation using two complementary tracks: (i) in theory/simulation, we monitor basis-dependent quadrature variance and covariance-derived summaries formed from mean-subtracted second moments so that $\Delta E_{\mathrm{cov}}$ reflects covariance inflation rather than coherent displacement; (ii) in the X8_01 hardware workflow, the readout is Fock sampling; thus, we use the shot-to-shot standard deviation ${\sigma }_N\left(\theta \right):=\sqrt{\widehat{Var}\left(N\left(\theta \right)\right)}$, where $N\left(\theta \right)$ denotes the shot-level detected count random variable at fixed $\theta$. In the reported hardware workflow, this shot-level count is formed by aggregating the returned Fock counts prior to postprocessing. We emphasize that ${\sigma }_N\left(\theta \right)$ is not claimed to estimate $Tr\left(V\right)$; it is an implementation-layer variability proxy aligned with the available readout. Our experimental validation is restricted to phase-only control instantiated as offline phase selection via one-dimensional grid search over $\theta$. Across numerical simulations and hardware phase-angle scans on Xanadu’s X8_01 photonic quantum processor, we find that static operating points can be brittle under strong DoS-like stress, whereas optimized phase selection can materially reduce a receiver-observed degradation proxy even without real-time feedback. Since $Tr\left(V\right)$ is invariant under pure rotations for phase-independent additive noise and ideal photon-number probabilities are invariant under a terminal Fock-basis phase gate, any observed $\theta$-dependence is interpreted operationally as evidence of a phase-dependent effective disturbance/measurement channel at the receiver interface. Simulation-only analyses indicate additional upside when squeezing is available, motivating future extensions incorporating higher-rate re-optimization, feedback-assisted architectures, and extended Gaussian control when available. Full article

Low-overlap indoor RGB-D point cloud registration remains vulnerable to hard failures because robust recovery and deployment latency are rarely achieved by one registrar. We present a budget-aware rescue-routing framework that keeps PointDSC+FCGF as the fast primary path and separates deployable pre-rescue gates from frozen-candidate selector analysis. On 3DLoMatch, the frozen selector DRACO-Stack reaches strict success of 0.5205 vs. 0.4278 for PointDSC+FCGF, while the deployable DRACO-Gate reaches 0.4801; a matched accuracy-only CoFiNet reconstruction on the same 1781 pairs reaches 0.5390. On 3DMatch, DRACO-Route activates 369/1623 pairs and reaches 0.8885 at 721.18 ms, compared with 0.8694 for PointDSC+FCGF and 0.9082 at 8310.48 ms for always-on RegTR. Redwood is used as public-transfer validation, where PointDSC+FCGF reaches 0.1425 vs. 0.1043 for PointDSC+FPFH. The results support selective indoor hard-tail rescue under an explicit runtime budget, without claiming a universal scene-free router or a new backbone. Full article

In recent years, deep learning-based vulnerability detection has drawn wide attention for its data-driven ability to analyze code semantics and learn vulnerability patterns without predefined models. However, data distribution differences across projects limit model generalization. Transfer learning provides a solution, yet most studies ignore expert-designed metrics. This paper proposes Decpvd, a data-driven cross-project software vulnerability detection method based on a dual-feature stacking ensemble. It builds an adaptive and transferable model using only code and vulnerability label data from source and target projects. It extracts code semantic features via Gated Graph Neural Networks, incorporates expert metrics from tools, performs cross-domain data-driven modeling with TrAdaBoost, and adaptively fuses the two features through stacking, overcoming fixed-weight fusion limitations. Experiments on six cross-project groups from three real datasets (FFmpeg, LibTIFF, LibPNG) show that Decpvd achieves an average AUC of 0.814, significantly outperforming mainstream baselines. Full article

This study introduces SquareSwish, a smooth, self-gated activation $f\left(x\right)=x\sigma {\left(x\right)}^2$, and benchmarks it against ten established activations (ReLU, LeakyReLU, ELU, SELU, GELU, Snake, LearnSnake, Swish, Mish, Hard-Swish) across six CNN architectures (EfficientNet-B1/B4, EfficientNet-V2-M/S, ResNet-50, and Xception) under a uniform transfer-learning protocol. Two geographically grounded datasets are used in this study. FuelRiskMap-TR comprises 7686 satellite images of urban fuel stations in Türkiye, which is semantically enriched with the OpenStreetMap context and YOLOv8-Small rooftop segmentation (mAP@0.50 = 0.724) to support AI-enabled, ICT-integrated risk screening. In a similar fashion, FuelRiskMap-UK is collected, comprising 2374 images. Risk scores are normalized and thresholded to form balanced High/Low-Risk labels for supervised training. Across identical training settings, SquareSwish achieves a top-1 validation accuracy of 0.909 on EfficientNet-B1 for FuelRiskMap-TR and reaches 0.920 when combined with SELU in a simple softmax-probability ensemble, outperforming the other activations under the same protocol. By squaring the sigmoid gate, SquareSwish more strongly attenuates mildly negative activations while preserving smooth, non-vanishing gradients, tightening decision boundaries in noisy, semantically enriched Earth-observation settings. Beyond classification, the resulting city-scale risk layers provide actionable geospatial outputs that can support inspection prioritization and integration with municipal GIS, offering a reproducible and low-cost safety-planning approach built on openly available imagery and volunteered geographic information. Full article

To enhance the monitoring and conservation efforts for China’s Class II endangered species, specifically the wild giant salamander and its ecosystems, this study addresses the urgent need to counteract the rapid decline of its wild population caused by habitat loss and insufficient surveillance. We present an innovative localization system based on passive Ultra-High-Frequency Radio Frequency Identification (UHF RFID) technology, employing a Double-Transform (D-Tr) methodology that integrates an enhanced 3D LANDMARC algorithm with GAIN generative adversarial networks. This system effectively reconstructs missing Received Signal Strength Indicator (RSSI) data due to environmental barriers by applying a log-distance path loss model. The D-Tr framework simultaneously generates RSSI sequences alongside their first-order differential characteristics, allowing for a comprehensive analysis of spatiotemporal signal relationships. Field tests conducted in the Hubei Xianfeng Zhongjian River Giant Salamander National Nature Reserve reveal that the positioning error consistently remains within 10 cm, with average accuracy improvements of 20.075%, 15.331%, and 12.925% along the X, Y, and Z axes, respectively, compared to traditional time-series models such as long short-term memory (LSTM) and gated recurrent unit (GRU). This system, designed to investigate the behavioral patterns and movement paths of farmed giant salamanders, achieves centimeter-level tracking of their cave-dwelling activities. It provides essential technical support for quantitatively assessing their daily activity patterns, habitat choices, and population trends, thereby promoting a shift from passive oversight to proactive monitoring in the conservation of endangered species. Full article

This work investigates the impact of load-dump stress on p-GaN HEMTs under floating gate condition. The experiments show that preconditioning the device with a small load-dump stress (150 V, @t_d = 100 ms and t_r = 8 ms) enhances its robustness against a larger stress (190 V, @t_d = 100 ms and t_r = 8 ms). If a large load-dump stress (≥160 V, @t_d = 100 ms and t_r = 8 ms) is applied directly to the device’s drain, the device will burn out. This occurs because the rapidly changing drain voltage during a load-dump event can generate a capacitive coupling current, leading to transient positive charge accumulation in the gate region. Consequently, the channel under the gate is turned on, allowing a large current to flow through it. The coexistence of high current and high voltage leads to substantial Joule heating within the device, resulting in eventual burnout. When a small load-dump stress is initially applied, the resulting charging of electron traps in the gate region increases the threshold voltage. As a result, the device can withstand a larger load-dump stress before the channel turns on, which explains the device’s enhanced robustness. This work clarifies the failure threshold of p-GaN HEMTs under the load-dump stress, providing key support for improving the devices’ reliability in the practical applications. It can provide a basis for adding necessary protective measures in device circuit design, and clarify the triggering voltage threshold of protective measures to ensure that they can effectively avoid device damage due to the load-dump stress. Full article

Three-dimensional NAND Flash has adopted the cell-over-peripheral (COP) structure to increase storage density. Unlike the conventional structure, the COP structure cannot directly increase the channel potential via substrate bias during the erase operation. Therefore, the gate-induced drain leakage (GIDL) erase method, which utilizes band-to-band tunneling (BTBT) to raise the channel potential, is employed. However, compared to bulk erase, the BTBT-based erase method requires a longer time to generate holes in the channel, leading to erase speed degradation. To address this issue, we propose a structure which enhances the erase speed by surrounding the bitline (BL) PAD with SiGe. In the case of a SiGe thickness (t_SiGe) of 13 nm, the lower bandgap of SiGe increases the BTBT generation rate, boosting the channel potential rise at the end of the erase voltage ramp-up by 861% compared to the Si-only structure, while limiting the reduction in read on-current to within 4%. We modeled the voltage and electric field across the SiGe layer, as well as BTBT generation rate and GIDL current in the SiGe layer, by varying t_SiGe, Ge composition ratio (SiGe_X), and the voltage difference between V_BL and V_{GIDL_TR}. Full article

As the relationship between energy and information loss and reversible gates was revealed, much interest in reversible gate design arose, and as quantum-dot cellular automata (QCA) gained attention as a next-generation nano circuit design technology, various reversible gates based on QCA emerged. The proposed study optimizes the performance and design costs of existing QCA-based reversible gates including TR, RUG, PQR, and URG. According to most indicators, the proposed circuits showed significant improvement rates and outperformed existing studies. In particular, the proposed optimal TR, RUG, PQR, and URG showed performance improvements of 266%, 265%, 300%, and 144% in Cost_AD, respectively, compared with the best existing circuit. This shows outstanding improvement and superiority in terms of area and delay, which are the most important factors in the performance of nano-scale circuits that are becoming extremely miniaturized. Additionally, the exceptionally high-output polarization of the proposed circuits is an important indicator of the circuit’s expansion and connection and increases the circuit’s reliability. Full article

In hyperspectral image classification (HSIC), every pixel of the HSI is assigned to a land cover category. While convolutional neural network (CNN)-based methods for HSIC have significantly enhanced performance, they encounter challenges in learning the relevance of deep semantic features and grappling with escalating computational costs as network depth increases. In contrast, the transformer framework is adept at capturing the relevance of high-level semantic features, presenting an effective solution to address the limitations encountered by CNN-based approaches. This article introduces a novel adaptive learnable spectral–spatial fusion transformer (ALSST) to enhance HSI classification. The model incorporates a dual-branch adaptive spectral–spatial fusion gating mechanism (ASSF), which captures spectral–spatial fusion features effectively from images. The ASSF comprises two key components: the point depthwise attention module (PDWA) for spectral feature extraction and the asymmetric depthwise attention module (ADWA) for spatial feature extraction. The model efficiently obtains spectral–spatial fusion features by multiplying the outputs of these two branches. Furthermore, we integrate the layer scale and DropKey into the traditional transformer encoder and multi-head self-attention (MHSA) to form a new transformer with a layer scale and DropKey (LD-Former). This innovation enhances data dynamics and mitigates performance degradation in deeper encoder layers. The experiments detailed in this article are executed on four renowned datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and the University of Pavia (UP). The findings demonstrate that the ALSST model secures optimal performance, surpassing some existing models. The overall accuracy (OA) is 99.70%, 89.72%, 97.84%, and 99.78% on four famous datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and University of Pavia (UP), respectively. Full article

Utilizing multi-modal data, as opposed to only hyperspectral image (HSI), enhances target identification accuracy in remote sensing. Transformers are applied to multi-modal data classification for their long-range dependency but often overlook intrinsic image structure by directly flattening image blocks into vectors. Moreover, as the encoder deepens, unprofitable information negatively impacts classification performance. Therefore, this paper proposes a learnable transformer with an adaptive gating mechanism (AGMLT). Firstly, a spectral–spatial adaptive gating mechanism (SSAGM) is designed to comprehensively extract the local information from images. It mainly contains point depthwise attention (PDWA) and asymmetric depthwise attention (ADWA). The former is for extracting spectral information of HSI, and the latter is for extracting spatial information of HSI and elevation information of LiDAR-derived rasterized digital surface models (LiDAR-DSM). By omitting linear layers, local continuity is maintained. Then, the layer Scale and learnable transition matrix are introduced to the original transformer encoder and self-attention to form the learnable transformer (L-Former). It improves data dynamics and prevents performance degradation as the encoder deepens. Subsequently, learnable cross-attention (LC-Attention) with the learnable transfer matrix is designed to augment the fusion of multi-modal data by enriching feature information. Finally, poly loss, known for its adaptability with multi-modal data, is employed in training the model. Experiments in the paper are conducted on four famous multi-modal datasets: Trento (TR), MUUFL (MU), Augsburg (AU), and Houston2013 (HU). The results show that AGMLT achieves optimal performance over some existing models. Full article

The von Neumann architecture with separate memory and processing presents a serious challenge in terms of device integration, power consumption, and real-time information processing. Inspired by the human brain that has highly parallel computing and adaptive learning capabilities, memtransistors are proposed to be developed in order to meet the requirement of artificial intelligence, which can continuously sense the objects, store and process the complex signal, and demonstrate an “all-in-one” low power array. The channel materials of memtransistors include a range of materials, such as two-dimensional (2D) materials, graphene, black phosphorus (BP), carbon nanotubes (CNT), and indium gallium zinc oxide (IGZO). Ferroelectric materials such as P(VDF-TrFE), chalcogenide (PZT), Hf_xZr_1−xO₂(HZO), In₂Se₃, and the electrolyte ion are used as the gate dielectric to mediate artificial synapses. In this review, emergent technology using memtransistors with different materials, diverse device fabrications to improve the integrated storage, and the calculation performance are demonstrated. The different neuromorphic behaviors and the corresponding mechanisms in various materials including organic materials and semiconductor materials are analyzed. Finally, the current challenges and future perspectives for the development of memtransistors in neuromorphic system applications are presented. Full article

Intelligent instruments are common components in industrial machinery, and fault diagnosis in IoT systems requires the handling of real-time sensor data and expert knowledge. IoT sensors cannot collect data for the diagnosis of all fault types in a specific instrument, and long-distance data transfer introduces additional uncertainties. However, because industrial equipment has complex fault causes and performances, it is typically difficult or expensive to obtain exact fault probabilities. Therefore, in this study, we proposed an innovative failure detection and diagnosis model for intelligent instruments in an IoT system using a Bayesian network, with a focus on handling uncertainties in expert knowledge and IoT monitoring information. The model addresses the challenge of complex fault causes and performances in industrial equipment, which make the obtainment of exact fault probabilities difficult or expensive. The trapezoidal intuitionistic fuzzy number (TrIFN)-based entropy method was applied in order to aggregate expert knowledge to generate priority probability, and the Leaky-OR gate was used to calculate CPT. The effectiveness of the proposed strategy was demonstrated through its application to an intelligent pressure transmitter (IPT) using the GeNIe software. Full article