Search Results (116)

This article presents a low-cost, high-voltage excitation circuit (EC) for capacitive ultra-sonic transducers (CUTs) based on a dual-path architecture. The proposed design comprises two independent AC excitation channels (AC-branch) that share a regulated DC-bias voltage (DC-branch). The circuit was developed to satisfy a fundamental operational requirement of CUTs: simultaneous application of a static bias voltage and a time-varying drive voltage. Because the electrostatic force depends nonlinearly on the applied voltage, efficient first-harmonic actuation requires the superposition of DC and AC voltage components. To reach this objective, the circuit and the transducer must be treated as a coupled electrical, electrostatic, mechanical, and acoustic system. In the proposed implementation, the DC-branch uses a TL494-PWM controller, a TIP50 switching transistor, a step-up transformer, and a rectifier-filter stage to generate the high-voltage bias of up to 200 V_DC. Each AC-channel employs an LM3886TF amplifier followed by a 1:15 step-up transformer, enabling the generation of excitation signals of up 180 V_peak. A key feature of the proposed architecture is the electrical independence of the two AC-channels, which allows for distinct excitation frequencies with minimal mutual interference. Experimental validation, performed with and without ultrasonic loads, demonstrates the relation between excitation conditions and the acoustic performance of the CUTs. Full article

Fine-grained poisonous mushroom recognition in natural scenes is challenging because of complex backgrounds, subtle morphological differences, and the limited interpretability of model decisions. To address these challenges, this paper proposes Att-FiLM, an attention-enhanced multi-scale Feature-Wise Linear Modulation network for poisonous mushroom image recognition. The model adopts an asymmetric dual-backbone architecture in which a frozen ConvNeXt-Base branch provides global semantic priors, while a trainable EfficientNet-B0 branch learns local discriminative features. Rather than directly concatenating heterogeneous features, Att-FiLM generates scale and shift parameters from semantic features and performs channel-wise modulation on multi-scale EfficientNet features at Stage 2 and Stage 4. This mechanism enables global semantic information to guide local feature learning while reducing feature redundancy and semantic inconsistency. Experimental results show that Att-FiLM achieves an Accuracy of 95.58% and an F1-score of 0.9455 on the poisonous/edible binary classification task. On the 190-class species-level classification task, it achieves a Top-1 Accuracy of 93.63% and a Macro-F1 of 0.9347. Interpretability analysis further shows that decision-relevant responses are frequently associated with morphologically relevant regions, including gills, annuli, volvae, and cap textures. These results indicate that Att-FiLM provides effective recognition performance together with interpretable decision evidence for mushroom recognition in complex natural scenes. Full article

Deep joint source-channel coding (DeepJSCC) has emerged as a promising paradigm for semantic-aware wireless image transmission, achieving strong performance under challenging channel conditions. However, MSE-trained DeepJSCC systems typically achieve high peak signal-to-noise ratio (PSNR) values but suppress high-frequency details, resulting in perceptually blurry reconstructions that fail to capture fine textures and edge information. Existing perceptual enhancement approaches for JSCC systems face significant practical limitations: full transceiver redesign methods require replacing both the transmitter and the receiver with large models (19–31 million parameters), incurring substantial deployment costs; diffusion-based refinement approaches require over 1700 million additional parameters and introduce inference latency exceeding 13 s, rendering them unsuitable for latency-constrained wireless applications; and generic image restoration networks lack channel state awareness and cannot adapt to varying signal-to-noise ratio (SNR) conditions. This paper proposes a lightweight receiver-only perceptual enhancer designed for use with frozen DeepJSCC backbones. The proposed module adopts residual learning with feature-wise linear modulation (FiLM)-based SNR-adaptive modulation to dynamically adjust the enhancement strength under varying channel conditions. A radially weighted FFT magnitude loss is further introduced to guide high-frequency recovery. The enhancer adds only 0.29 million trainable parameters (<1% of the backbone) and requires neither transmitter modification nor backbone retraining. Extensive experiments on the Kodak24 and DIV2K datasets demonstrate a 34.4–37.5% LPIPS reduction over the frozen DeepJSCC baseline under AWGN channels. Supplementary robustness evaluations further show a 30–33% LPIPS reduction under Rayleigh fading, and stable generalization to unseen SNR levels. The receiver-side decoder-plus-enhancer pipeline requires 43 ms at 768 × 512 resolution, corresponding to approximately 23 frames per second. Full article

The rapid proliferation of multi-standard fifth-generation (5G) new radio sub-6 GHz systems has intensified the demand for compact, high-isolation multiplexing components capable of simultaneously routing multiple frequency channels through a single shared antenna port without cross-band interference. This article presents the design and full-wave electromagnetic (EM) simulation of a compact microstrip triplexer operating at 2.2, 2.6, and 3.0 GHz for 5G sub-6 GHz applications. The proposed triplexer employs square open-loop resonators (SOLRs) arranged as three independent three-pole Chebyshev bandpass filter channels. Each channel is synthesized from a standard normalized Chebyshev lowpass filter prototype. The three channels are integrated at a common input port via a T-junction, with the connecting transmission line stubs dimensioned to enforce high inter-channel isolation. The triplexer is implemented on Rogers RT/Duroid 6010LM substrate. Full-wave EM simulation results demonstrate return losses of 21.1, 23.1, and 22.8 dB; insertion losses of 1.08, 1.01, and 0.98 dB; and inter-channel isolations of 45.7, 45.2, and 45.7 dB for ports S₃₂, S₄₂, and S₄₃, respectively. The achieved inter-channel isolation exceeding 45 dB represents a significant improvement over the majority of recently reported microstrip triplexers operating in the sub-6 GHz range. The device occupies a compact circuit area of 0.34λg × 0.52λg. λg is the guided wavelength for the microstrip line impedance at the 2.6 GHz centre frequency of the triplexer. Full article

A previous investigation on limonene (LM), perillyl alcohol (POH), and carveol (CV), focused on the structure–activity relationship and hydroxyl group, documented that the presence of the hydroxyl group influences the pharmacodynamic potency of these agents, inhibiting smooth muscle contraction with the order of potency: POH > CV > LM. That investigation also suggested a mechanism of action, which importantly included activity on the voltage-dependent calcium channel. Here, we investigated whether this structure–activity relationship also applies to nerve excitability (an activity greatly dependent on sodium channels) using compound action potential (CAP) recordings from mouse sciatic nerves and in silico simulations. POH, CV, and LM inhibited both the positive amplitudes and conduction velocities of the two CAP components in a concentration-dependent manner, with IC₅₀ values of 0.8, 1.0, and 4.3 mM (1st component) and 0.6, 0.6, and 3.0 mM (2nd component) for amplitude, and 2.4, 2.4, and 7.1 mM (1st component) and 1.0, 2.6, and 4.3 mM (2nd component) for conduction velocity. The order of pharmacodynamic potency, thus, was POH = CV > LM. In silico simulation demonstrated that POH and CV penetrate the Nav 1.6 and accommodate in the channel at the interface between the selectivity filter and the central cavity, a position very favorable to block the channel pore. In contrast, LM exhibited a markedly different docking profile, suggesting that LM binding is less likely to directly obstruct sodium permeation. In conclusion, the three substances investigated inhibited nerve excitability, but those with a hydroxyl group demonstrated greater pharmacodynamic potency. Full article

Despite rapid advances in laser display technology, existing evaluation frameworks remain confined to isolated physical metrics, decoupled from human visual perception. This study presents the laser display comprehensive testing system (LD-CTS003), a unified platform integrating physical characterization and visual perceptual assessment. Grounded in opponent-process theory, the system implements a complete color conversion pipeline from display RGB through CIE XYZ and LMS to the Derrington–Krauskopf–Lennie space, linking spectral output to retinal cone responses. The hardware architecture features five-axis precision motion and multi-sensor synchronous acquisition, while the software supports both conventional optical measurements and psychophysical experiments. Static image resolution was evaluated via stripe-pattern modulation analysis across viewing distances, and visual contrast sensitivity was measured using Gabor stimuli under varying luminance and eccentricity. The results demonstrate that reduced viewing distances enhance effective resolution, with text display imposing stricter requirements than image display. Contrast sensitivity functions exhibit band-pass profiles, with luminance and eccentricity strongly modulating achromatic and red–green channels, whereas yellow–violet responses remain relatively robust peripherally. By unifying objective metrology and subjective evaluation, this work establishes a perception-oriented framework for laser display quality assessment, providing a physiologically grounded foundation for display optimization and standard development. Full article

Gearbox fault diagnosis under noisy operating conditions remains a critical yet unsolved challenge for industrial condition monitoring. The primary challenge originates from a fundamental conflict: fault signatures are present within certain frequency ranges, yet standard deep learning models process raw vibration signals without recognizing which frequencies are relevant. Moreover, noise affects the entire spectrum uniformly. Existing transformer-based methods for vibration analysis treat time steps as tokens and therefore fail to capture cross-sensor dependencies, while conventional denoising approaches apply fixed filters that cannot adapt to the varying spectral characteristics of different fault types and noise levels. We propose the Adaptive Frequency-Aware Inverted Transformer (AF-iTransformer), a lightweight transformer framework that lets the model learn which frequencies to attend to on a per-sample basis. In particular, we propose a learnable spectral filter that transforms the input signal to the frequency domain via FFT. Then it predicts a soft frequency mask conditioned on signal statistics and applies it before reconstructing the filtered signal through iFFT, allowing the model to suppress noise bands while preserving fault-relevant spectral content dynamically. After adaptive filtering, the architecture employs channel-level tokenization to uniformly represent heterogeneous channels as input tokens, relying on cross-channel attention to automatically learn their distinct contributions to fault diagnosis. Feature-wise linear modulation is introduced to inject signal-level statistics at every encoder layer. Furthermore, the framework utilizes residual attention propagation to stabilize deep training, and an auxiliary spectrum prediction head provides spectral regularization during training. On the UConn Gearbox dataset with nine fault categories, AF-iTransformer achieves 99.63% accuracy on clean data and maintains 95.1% at 0 dB signal-to-noise ratio, substantially outperforming all baselines under noisy conditions. On the SEU gearbox dataset with five fault categories, AF-iTransformer achieves 99.74% clean accuracy. On 2 GB edge GPUs, AF-iTransformer achieves a per-window inference latency of 7.3–13 ms with peak memory below 17 MB, and 1.4–4.7 ms on modern CPUs, confirming its viability for real-time industrial deployment. Full article

Developing white organic light-emitting diodes (WOLEDs) with high exciton utilization, balanced charge transport, and stable complementary emission remains a challenge for solid-state lighting and display applications. Herein, a triplet reverse intersystem crossing (RISC)-assisted strategy is proposed to enhance triplet exciton harvesting to construct efficient hybrid WOLEDs. The increased RISC channels promote the up-conversion of triplet excitons into radiative singlet excitons, thereby improving the overall exciton utilization efficiency. By further introducing an ultrathin PO-01 layer as an orange orange-emitting component, a hybrid WOLED with a current efficiency of 49.1 cd/A and 34.8 lm/W is realized. Moreover, suppressed efficiency roll-offs and stable spectra are achieved due to balanced charge transport. This work provides a practical route toward high-performance WOLEDs. Full article

We demonstrate an underwater visible light communication system using a circularly polarized 520 nm laser transmitter, 32APSK modulation, and a polarization-diverse dual-aperture receiver. An end-to-end post-equalization network, DirectDemodNet, directly maps dual-polarization received waveforms to 32APSK symbol logits, replacing conventional Least Mean Square (LMS) + Volterra equalization. By combining waveform-difference features, dual-scale dilated temporal convolutions, and multi-period positional encoding, DirectDemodNet improves nonlinear compensation and branch fusion. Extensive evaluations are conducted across data rates from 7.5 to 13.75 Gbps over a 1.2 m static underwater channel. Experiments show that DirectDemodNet broadens the forward error correction compliant operating range and provides a maximum net transmission rate gain of 4.095 Gbps over LMS + Volterra at the 7% Hard-decision Forward Error Correction (HD-FEC) threshold. Full article

Field-programmable gate arrays (FPGAs) with on-package high-bandwidth memory (HBM) are an attractive substrate for low-latency, precision-customizable AI inference, yet high-level synthesis (HLS) flows expose little control over how tensors are distributed across many independent memory channels. The AMD Alveo V80 spreads its nominal 820 GB/s across 64 pseudo-channels, each capped at 12.8 GB/s, so delivered bandwidth is governed by an explicit tensor-to-channel partitioning decision that existing framework-based flows do not surface. This paper presents a methodology for HLS-based inference on the V80 built on two coupled contributions: a design-time partitioning framework that assigns each tensor to on-chip (BRAM/URAM) storage, a single HBM channel, or a stripe across several channels according to its reuse and access pattern, and a three-stage HLS flow that separates functional baseline, unroll-and-partition throughput extraction, and precision-aware DSP packing. The methodology is developed through five model kernels—a parameterized GEMM, ResNet-18, ViT-Small, a BERT attention block, and GPT-2 Small—and realized as nine engines spanning GEMM (FP32, INT8, INT4), convolution, attention, layer normalization, SoftMax, pooling, and embedding lookup. All nine engines are synthesized in Vitis HLS 2024.2 and placed, routed, and executed on the physical Alveo V80 at 400 MHz (2.5 ns period), and every engine closes timing with positive worst-case slack. We report per-engine cycle counts, post-route utilization, and post-route dynamic-power estimates, and compare engine latency and energy against NVIDIA Titan RTX (GPU) and Intel Xeon W-3223 (CPU) baselines on identical kernels. A central result, confirmed on silicon, is that INT8 roughly halves the GEMM DSP58 footprint relative to FP32 (27 to 14 slices), whereas INT4 yields no further compute reduction and acts purely as a memory-placement lever. The full-model compositions, the roofline classification, and the tensor-to-channel placement framework are analytical, design-time results rather than end-to-end measured performance. The bandwidth-scaling premise underlying the placement framework is confirmed directly on the V80: a 1-to-64 channel sweep shows aggregate HBM bandwidth scaling near-linearly to within 5–13% of the device peak, and placing the GPT-2 LM-head operand on a single channel versus the eight the framework assigns it yields a measured 6.81× speedup—a direct on-board test of the striping decision. The resulting design guidelines target HLS practitioners working with HBM-equipped FPGAs. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality worldwide, and accurate histopathological tissue segmentation is critical for timely and reliable diagnosis. Healthcare systems represent complex adaptive environments where diagnostic tools must function reliably across heterogeneous clinical settings, varying staining protocols, and resource-constrained infrastructures. However, existing deep learning segmentation models often require substantial computational resources, limiting their deployment in such settings. This study proposes a novel, resource-efficient colorectal histopathology segmentation network (RCHS-Net) designed for robust clinical deployment across diverse and resource-constrained healthcare environments. Methods: RCHS-Net employs a compact multi-scale encoder with channel recalibration blocks, a gland context module (GCM) with three parallel atrous convolutions and lightweight self-attention for multi-scale contextual feature extraction, and a feature pyramid decoder (FPD) for fine-grained spatial reconstruction. To address the demands of real-world healthcare systems, feature-wise linear modulation (FiLM) conditioning enables class-aware segmentation across multiple tissue categories, while MixStyle augmentation improves stain domain generalization across heterogeneous laboratory and scanner conditions. Results: The model was evaluated on two publicly available benchmark datasets: the EBHI-Seg dataset and the GlaS dataset. On EBHI-Seg, RCHS-Net achieved a mean Dice coefficient of 95.20% and a mean IoU of 91.10% across six colorectal tissue classes, with only 243,226 trainable parameters. On the GlaS benchmark, RCHS-Net attained a Dice score of 93.39% and an IoU of 88.32%, outperforming state-of-the-art methods. Conclusions: RCHS-Net demonstrates that high-accuracy histopathology segmentation can be achieved with a compact architecture, offering a scalable and practical solution for AI-assisted cancer diagnosis across the complex, heterogeneous conditions of real-world healthcare systems, supporting scalable and equitable cancer diagnostics globally. Full article

Graph-based narrative extraction relies on a coherence function to score transitions between events, but the coherence metrics in current use are defined operationally and lack an information-theoretic foundation. We study the composite metric $C=\sqrt{A·T}$, where A is the angular similarity of document embeddings and $T=1-d_{\mathrm{JS}}$ is the topic proximity through the Jensen–Shannon distance of soft cluster memberships, and we provide an information-geometric reading of this metric together with an axiomatic characterization of the geometric-mean combinator. On the product manifold ${\mathbb{S}}^{d-1}\times {\Delta }_{+}^{K-1}$, the negative log-coherence decomposes additively into an angular and a topic cost. Because the Riemannian metric tensor induced by the Jensen–Shannon distance on the simplex is proportional to the Fisher information matrix, the topic component is locally consistent with the Fisher–Rao metric singled out by Chentsov’s theorem. Within a parametric family of combinators (the compensability spectrum), the geometric mean is the unique combinator consistent with four natural axioms (a boundary/veto condition, symmetry, log-additivity, normalization), and the construction also motivates a proper product metric $d_{\times }$ that we use as a reference distance. Experiments on four corpora spanning news and academic domains (40 to 6000 documents), three general-purpose embedding families (GPT-4/ada-002, MPNet, MiniLM-L6) plus citation-aware SPECTER2, and three alternative topic models (LDA, soft k-means, GMM) are consistent with the framework: the Fisher identity holds with $R\ge 0.99$, the geometric mean tracks $d_{\times }$ closely ($\rho =0.999$), and a downstream LLM-as-judge consistency check shows that the geometric mean is not empirically dominated by any alternative combinator or single-channel baseline. Sweeping the compensability spectrum, the bottleneck-coherence gap between extracted storylines and random sequences splits into a symmetric component—maximized at the geometric mean on the four corpora above and a fifth, human-navigation corpus—and a displacement term; a cross-modal case study on a human-curated image narrative reproduces the same effect in a second modality. Together, these results provide an information-geometric justification for the composite coherence metric and articulate the conditions under which the geometric mean is the natural choice. Full article

Using a balanced panel of ten major fishing and trading nations (China, Chile, Indonesia, Peru, Thailand, Vietnam, Norway, India, Denmark, and Canada) over the years 2000–2020, this study investigated the relationship between international fishery trade and the fishing footprint, a consumption-based ecological indicator measuring the bioproductive marine area required to sustain seafood consumption. Cross-sectional dependence tests, second-generation panel unit root tests (PANICCA), LM bootstrap cointegration analysis, and long-run coefficient estimation using fully modified OLS (FMOLS), dynamic OLS (DOLS), fixed effects, and method of moments quantile regression (MMQR) are all part of the sequential econometric framework used in this analysis. Findings consistently show that the domestic fishing footprint is positively correlated with imports, domestic production, real GDP, and per capita food consumption, but adversely correlated with fishery exports. Additionally, MMQR estimates show that the negative export link becomes stronger at higher quantiles of the distribution of fishing footprint, indicating that the moderating influence of exports is strongest in nations that are already under a lot of ecological strain. Although the panel data do not allow for direct dissection of these channels, these findings are interpreted considering three potential mechanisms: certification-linked catch limits, aquaculture substitution in export volumes, and distant-water fleet displacement. It is recommended that policymakers include sustainability criteria into import laws, broaden the scope of eco-certification, and make investments in aquaculture to supplement the management of wild-capture fisheries. The findings of this study contribute significantly to the monitoring of global sustainability agendas, particularly aligning with United Nations Sustainable Development Goal (SDG) 12 (Responsible Consumption and Production) and SDG 14 (Life Below Water) by providing empirical evidence on how trade dynamics influence the fishing footprint. Full article

The Moving Picture Experts Group (MPEG) is developing the Video Coding for Machines (VCM) standard to support efficient video compression for machine vision tasks. The VCM standard primarily targets object detection, tracking, and semantic segmentation. Since VCM mainly focuses on object-centric tasks such as detection and tracking, it employs Region-of-Interest (ROI) coding to allocate more bits to foregrounds, while suppressing background regions. This suppression reduces segmentation accuracy by degrading contextual background information. To address this limitation, we propose a luma background restoration method that reconstructs degraded background regions by exploiting the structural correlation between decoded luma and chroma components without relying on complex chroma modeling. The proposed method integrates multi-channel linear modeling with context-based arithmetic coding to efficiently transmit grouped Linear Model (LM) indices for luma restoration. Under VCM test conditions, experimental results show that the proposed method achieves an average Bjøntegaard Delta mean Intersection-over-Union (BD-mIoU) of 7.70, compared with 7.41 achieved by the latest background preservation method. These results demonstrate that the proposed method effectively restores structural background details in luma regions essential for semantic segmentation in VCM frameworks. Full article

With the widespread adoption of multiple-input–multiple-output (MIMO) technology, channel state information (CSI) prediction has become a crucial technique for enhancing the performance of wireless communication systems. Traditional channel prediction methods face performance bottlenecks under high-speed mobility and complex channel conditions, making it difficult to meet the requirements of modern communication systems. To address this issue, this paper proposes a fully complex-valued cross-domain modeling framework, termed a complex-valued multi-scale transformer with time–frequency cross-attention network (CMTF-Net), for MIMO CSI prediction. CMTF-Net integrates a learnable multi-scale short-time Fourier transform (LMS-STFT), complex-valued multi-head self-attention (C-MHSA), and bidirectional cross-domain attention for complex-valued sequences (BCDA-CVS). These modules are designed to preserve amplitude–phase consistency, adapt time–frequency representations to CSI evolution, and enable information interaction between temporal and spectral features. On the simulated Overall Test set, CMTF-Net achieves the lowest MAE of 0.000032 and the highest Corr. ($\rho$) of 0.8230 among the compared methods, while maintaining competitive SE and BER values of 0.4240 and 0.2411 at SNR = 10 dB. On the DICHASUS measured datasets, CMTF-Net also shows favorable Test-ID and Test-OOD performance. For example, on DICHASUS-2186, it obtains Corr. ($\rho$)/SE/BER values of 0.8367/0.4935/0.2243 on Test-ID and 0.8061/0.4697/0.2351 on Test-OOD. These results indicate that CMTF-Net provides a balanced performance profile across prediction accuracy, spatial alignment, and communication-oriented evaluation. Full article