Search Results (68)

Reliable detection of X-ray tire defects is essential for safety and quality assurance in manufacturing. However, low contrast and high noise make traditional methods unreliable. This paper presents DyReCS-YOLO, a dynamic re-parameterized channel-shuffle network based on YOLOv8. The model introduces a C2f_DyRepFusion module combining dynamic convolution and a shuffle-and-routing mechanism, enabling adaptive kernel adjustment and efficient cross-channel interaction. Experiments on an industrial X-ray tire dataset containing 8326 images across 58 defect categories demonstrate that DyReCS-YOLO achieves an mAP@0.5 of 0.741 and mAP@0.5:0.95 of 0.505, representing improvements of 4.5 and 2.8 percentage points over YOLOv8-s, and 9.2 and 7.7 percentage points over YOLOv11-s, respectively. The precision increases from 0.698 (YOLOv8-s) and 0.668 (YOLOv11-s) to 0.739, while maintaining real-time inference at 189.5 FPS, meeting industrial online detection requirements. Ablation results confirm that the combination of dynamic convolution and channel shuffle improves small-defect perception and robustness. Moreover, DyReCS-YOLO achieves an mAP@0.5 of 0.975 on the public MT defect dataset, verifying its strong cross-domain generalization. Full article

The manual inspection of electric water heater enamel is inefficient and unreliable, a challenge stemming from the tank’s narrow (approx. 50 mm) aperture that creates extremely dim, non-uniform lighting. Existing enhancement algorithms struggle with such complex industrial imagery. To address this, we propose an integrated hardware-software system: the three-axis Image Acquisition Robot (IAR) and Interactive Visualization Enhancement Software (IVES). Using this system, we constructed and released the first Heater Tank Inner Wall (HTIW) dataset, containing 900 real-world images. We further introduce jar-RetinexNet, a Retinex-based network featuring a Feature Preservation Attention Module (FPAM), a Cascaded Channel-Spatial Attention Module (CSAM) for precise decomposition, and a Random Affine Generation (RAG) module for generalization. Experiments show that jar-RetinexNet significantly outperforms state-of-the-art methods, achieving the best no-reference quantitative scores on our HTIW dataset: a BRISQUE of 25.4457 and a CLIPIQA of 0.3160. Full article

Vegetation indices (VIs) are a widely adopted and straightforward tool for non-contact estimation of chlorophyll and carotenoid content in plant leaves. However, VI-based method accuracy depends critically on instrument configuration and calibration procedures. This study aimed to evaluate the sensitivity of VI-based pigment assessment to variations in spectral channel parameters (central wavelength and bandwidth) as well as to changes in calibration details defined by the specific VI formula. Pigment content was measured in leaves of Lactuca sativa L. and Cucumis sativus L. at contrasting developmental stages using VI-based reflection spectroscopy across the 450–950 nm spectral range with various protocols and spectrophotometry as the reference method. VI values were calculated with varying central wavelength and widths of spectral bands, and across different VI formulas. Comparative analysis of the obtained measurements revealed that even minor shifts in central wavelengths of less than 20 nm or the use of an alternative index formula could lead to relative errors of 42–77% in the estimation of chlorophylls and carotenoids content, while changes in bandwidth had a much smaller impact, resulting in only 2–5% relative errors. Even with identical parameters of spectral channels, the choice of an appropriate VI and its regression model could introduce significant errors, ranging from 36% to 86%. These findings highlight the critical role of instrument specifications and calibration models in the VIs-based method accuracy and stability, as measurement errors can lead to suboptimal agronomic decisions. Moreover, our study underscores that comparing results from different sensors or platforms can be unreliable unless the channel parameters and calibration details are clearly specified. Therefore, standardization and transparency in VIs assignment is vital to ensure reproducibility and cross-compatibility in non-destructive pigment monitoring by using various devices. Full article

Batch normalization (BN) has become a foundational component in modern deep neural networks. However, one of its disadvantages is its reliance on batch statistics that may be unreliable or unavailable during inference, particularly under test-time domain shifts. While batch-statistics-free affine transformation methods alleviate this by learning per-sample scale and shift parameters, most treat samples independently, overlooking temporal or sequential correlations in streaming or episodic test-time settings. We propose LSTM-Affine, a memory-based normalization module that replaces BN with a recurrent parameter generator. By leveraging an LSTM, the module produces channel-wise affine parameters conditioned on both the current input and its historical context, enabling gradual adaptation to evolving feature distributions. Unlike conventional batch-statistics-free designs, LSTM-Affine captures dependencies across consecutive samples, improving stability and convergence in scenarios with gradual distribution shifts. Extensive experiments on few-shot learning and source-free domain adaptation benchmarks demonstrate that LSTM-Affine consistently outperforms BN and prior batch-statistics-free baselines, particularly when adaptation data are scarce or non-stationary. Full article

The surface quality of industrial gaskets directly impacts sealing performance, operational reliability, and market competitiveness. Inadequate or unreliable defect detection in silicone gaskets can lead to frequent maintenance, undetected faults, and security risks in downstream systems. This paper presents VST-YOLOv8, a trustworthy and secure defect detection framework built upon an enhanced YOLOv8 architecture. To address the limitations of C2F feature extraction in the traditional YOLOv8 backbone, we integrate the lightweight Mobile Vision Transformer v2 (ViT v2) to improve global feature representation while maintaining interpretability. For real-time industrial deployment, we incorporate the Gating-Structured Convolution (GSConv) module, which adaptively adjusts convolution kernels to emphasize features of different shapes, ensuring stable detection under varying production conditions. A Slim-neck structure reduces parameter count and computational complexity without sacrificing accuracy, contributing to robustness against performance degradation. Additionally, the Triplet Attention mechanism combines channel, spatial, and fine-grained attention to enhance feature discrimination, improving reliability in challenging visual environments. Experimental results show that VST-YOLOv8 achieves higher accuracy and recall compared to the baseline YOLOv8, while maintaining low latency suitable for edge deployment. When integrated with secure industrial control systems, the proposed framework supports authenticated, tamper-resistant detection pipelines, ensuring both operational efficiency and data integrity in real-world production. These contributions strengthen trust in AI-driven quality inspection, making the system suitable for safety-critical manufacturing processes. Full article

The “insecurity of the network” characterizes each agent as being remotely controlled through unreliable network channels. In such an insecure network, the output signal can be altered through carefully designed adversarial attacks to produce erroneous results. To address this, this paper proposes a neural network (NN) adaptive secure control scheme for cyber-physical systems (CPSs) via attack reconstruction strategies, where the attack reconstruction strategy serves as the solution to the NNs estimation problem on the insecurity of the network. Consequently, by introducing a novel error transformation, an NN-adaptive secure control method is formulated as the framework of backstepping. Based on the Lyapunov stability theory and defined error transformation, it is proven that the above secure control process reaches the expected trajectory, and all the signals are bounded in closed-loop systems. Finally, its effectiveness is verified via a simulation of attitude control of two-joint robots. Full article

In recent years, the demand for maritime wireless communication has been increasing, particularly in areas such as ship operations management, marine resource utilization, and safety assurance. However, due to the difficulty of deploying base stations(BSs), maritime communication still faces challenges in terms of limited coverage and unreliable communication quality. As the number of users on ships and offshore platforms increases, along with the growing demand for mobile communication at sea, conventional terrestrial base stations struggle to provide stable connectivity. Therefore, existing maritime communication primarily relies on satellite communication and long-range Wi-Fi. However, these solutions still have limitations in terms of cost, stability, and communication efficiency. Satellite communication solutions, such as Starlink and Iridium, provide global coverage and high reliability, making them essential for deep-sea and offshore communication. However, these systems have high operational costs and limited bandwidth per user, making them impractical for cost-sensitive nearshore communication. Additionally, geostationary satellites suffer from high latency, while low Earth orbit (LEO) satellite networks require specialized and expensive terminals, increasing hardware costs and limiting compatibility with existing maritime communication systems. On the other hand, 5G-based maritime communication offers high data rates and low latency, but its infrastructure deployment is demanding, requiring offshore base stations, relay networks, and high-frequency mmWave (millimeter-wave) technology. The high costs of deployment and maintenance restrict the feasibility of 5G networks for large-scale nearshore environments. Furthermore, in dynamic maritime environments, maintaining stable backhaul connections presents a significant challenge. To address these issues, this paper proposes a low-cost nearshore wireless communication solution utilizing tethered balloons as coastal base stations. Unlike satellite communication, which relies on expensive global infrastructure, or 5G networks, which require extensive offshore base station deployment, the proposed method provides a more economical and flexible nearshore communication alternative. The tethered balloon is physically connected to the coast, ensuring stable power supply and data backhaul while providing wide-area coverage to support communication for ships and offshore platforms. Compared to short-range communication solutions, this method reduces operational costs while significantly improving communication efficiency, making it suitable for scenarios where global satellite coverage is unnecessary and 5G infrastructure is impractical. Additionally, conventional uniform power allocation or channel-gain-based amplification methods often fail to meet the communication demands of dynamic maritime environments. This paper introduces a nonlinear dynamic power allocation method based on channel gain information to maximize downlink communication efficiency. Simulation results demonstrate that, compared to conventional methods, the proposed approach significantly improves downlink communication performance, verifying its feasibility in achieving efficient and stable communication in nearshore environments. Full article

Indonesia’s commitment to the early retirement of coal-fired power plants (CFPPs) underscores the urgent need to transition to renewable energy due to coal’s significant contribution to environmental degradation and rising CO₂ emissions. Despite this urgency, several challenges impede the widespread adoption of renewable energy, including disparities in energy access, inadequate policy implementation, unreliable government financing mechanisms, and lack of education and awareness, especially due to the current incorporation of hydrogen and nuclear energy. To overcome these barriers, a robust policy framework is essential, complemented by progressive policy enactment. This study examines Indonesia’s evolving energy landscape, highlighting key challenges and opportunities for the implementation of renewable energy. The findings emphasize that a comprehensive and integrated roadmap is critical to unlocking Indonesia’s renewable energy potential. The roadmap includes strengthening governance, fostering public–private collaborations, and securing diverse financing channels, while offering targeted incentives, such as tax breaks and financial benefits. Furthermore, conducting pre-feasibility studies and regional assessments for emerging energy sources, like hydrogen and nuclear power, is crucial to accurately evaluate potential risks and opportunities. By addressing gaps in regulatory framework and enforcing effective policy measures, Indonesia can facilitate public–private partnerships, promote technology transfer, and develop skilled workforce as an effort to transition into a sustainable and diversified energy future. Full article

Gate dielectrics are essential components in nanoscale field-effect transistors (FETs), but they often face significant instabilities when exposed to harsh environments, such as radioactive conditions, leading to unreliable device performance. In this paper, we evaluate the performance of ultrascaled transition metal dichalcogenide (TMD) FETs equipped with vacuum gate dielectric (VGD) as a means to circumvent oxide-related instabilities. The nanodevice is computationally assessed using a quantum simulation approach based on the self-consistent solutions of the Poisson equation and the quantum transport equation under the ballistic transport regime. The performance evaluation includes analysis of the transfer characteristics, subthreshold swing, on-state and off-state currents, current ratio, and scaling limits. Simulation results demonstrate that the investigated VGD TMD FET, featuring a gate-all-around (GAA) configuration, a TMD-based channel, and a thin vacuum gate dielectric, collectively compensates for the low dielectric constant of the VGD, enabling exceptional electrostatic control. This combination ensures superior switching performance in the ultrascaled regime, achieving a high current ratio and steep subthreshold characteristics. These findings position the GAA-VGD TMD FET as a promising candidate for advanced radiation-hardened nanoelectronics. Full article

In distributed radar, a coherent system often gains attention for its higher detection potential in contrast to its non-coherent counterpart. However, even for a distributed coherent radar, it is difficult to coherently accumulate local observations in the searching mode if target returns in local channels are decorrelated. In order to obtain the superiority of coherent processing while overcoming the real implementation difficulties of a coherent framework, this paper studies a distributed coherent detection algorithm for fusion detection. It is utilized in detecting a target during tracking while a target is searched for in a non-coherent manner. From historic observations on target tracking, relative phase delays in different channels are predicted by a phase lock loop and then used to compensate phases for observations in the current frame. Moreover, to enhance the detection performance of distributed radar during tracking, a switching rule between phase prediction-based coherent and non-coherent processing is proposed based on their detection performance. Numerical results indicate that the switching operation can improve the detection probability during tracking, and the non-coherent operation can still provide a moderate detection performance if the phase prediction is unreliable. Full article

With the development of mobile communication technology and the proliferation of the number of Internet of Things (IoT) terminal devices, a large amount of data and intelligent applications are emerging at the edge of the Internet, giving rise to the demand for edge intelligence. In this context, federated learning, as a new distributed machine learning method, becomes one of the key technologies to realize edge intelligence. Traditional edge intelligence networks usually rely on terrestrial communication base stations as parameter servers to manage communication and computation tasks among devices. However, this fixed infrastructure is difficult to adapt to the complex and ever-changing heterogeneous network environment. With its high degree of flexibility and mobility, the introduction of unmanned aerial vehicles (UAVs) into the federated learning framework can provide enhanced communication, computation, and caching services in edge intelligence networks, but the limited communication bandwidth and unreliable communication environment increase system uncertainty and may lead to a decrease in overall energy efficiency. To address the above problems, this paper designs a UAV-assisted federated learning with a privacy-preserving and efficient data sharing method, Communication-efficient and Privacy-protection for FL (CP-FL). A network-sparsifying pruning training method based on a channel importance mechanism is proposed to transform the pruning training process into a constrained optimization problem. A quantization-aware training method is proposed to automate the learning of quantization bitwidths to improve the adaptability between features and data representation accuracy. In addition, differential privacy is applied to the uplink data on this basis to further protect data privacy. After the model parameters are aggregated on the pilot UAV, the model is subjected to knowledge distillation to reduce the amount of downlink data without affecting the utility. Experiments on real-world datasets validate the effectiveness of the scheme. The experimental results show that compared with other federated learning frameworks, the CP-FL approach can effectively mitigate the communication overhead, as well as the computation overhead, and has the same outstanding advantage in terms of the balance between privacy and usability in differential privacy preservation. Full article

In order to explore more spectrum resources to support sensors and their related applications, cognitive wireless sensor networks (CWSNs) have emerged to identify available channels being underutilized by the primary user (PU). To improve the detection accuracy of the PU signal, cooperative spectrum sensing (CSS) among sensor paradigms is proposed to make a global decision about the PU status for CWSNs. However, CSS is susceptible to Byzantine attacks from malicious sensor nodes due to its open nature, resulting in wastage of spectrum resources or causing harmful interference to PUs. To suppress the negative impact of Byzantine attacks, this paper proposes a beta distribution function (BDF) for CSS among multiple sensors, which includes a sequential process, beta reputation model, and weight evaluation. Based on the sequential probability ratio test (SPRT), we integrate the proposed beta reputation model into SPRT, while improving and reducing the positive and negative impacts of reliable and unreliable sensor nodes on the global decision, respectively. The numerical simulation results demonstrate that, compared to SPRT and weighted sequential probability ratio test (WSPRT), the proposed BDF has outstanding effects in terms of the error probability and average number of samples under various attack ratios and probabilities. Full article

This research deals with the design problems of LED-based spectrally tunable light sources (LSTLSs). The study aims to assess the reliability of popular models for the spectral modeling of LED radiation and a typically used curve-fitting criterion (R²) in the development of high-quality multi-emitter LED retrofits for incandescent photometric illuminant. The research methodology involves modeling each LED channel using Lorentz and Gaussian functions and combining multiple channels to approximate the desired spectral power distribution (SPD). After the optimization, 20 various LED sets were designed, which allowed us to replicate the SPD of CIE illuminant A with a very high R² value. Two sets were constructed and measured to recognize the reliability of the simulation approach. The results suggest that for planning the LSTLS for photometric applications, these models are unreliable as they do not reflect the real effect of changes in the characteristics of the components nor reveal the share of various spectral ranges. Therefore, the decisions made on these criteria may not be the best solutions in the context of specific applications. Full article

Circulating tumor cells (CTCs) are a type of cancer cell that spreads from the main tumor to the bloodstream, and they are often the most important among the various entities that can be isolated from the blood. For the diagnosis of cancer, conventional biopsies are often invasive and unreliable, whereas a liquid biopsy, which isolates the affected item from blood or lymph fluid, is a less invasive and effective diagnostic technique. Microfluidic technologies offer a suitable channel for conducting liquid biopsies, and this technology is utilized to extract CTCs in a microfluidic chip by physical and bio-affinity-based techniques. This effort uses functionalized magnetic nanoparticles (MNPs) in a unique microfluidic chip to collect CTCs using a hybrid (physical and bio-affinity-based/guided magnetic) capturing approach with a high capture rate. Accordingly, folic acid-functionalized Fe₃O₄ nanoparticles have been used to capture MCF-7 (breast cancer) CTCs with capture efficiencies reaching up to 95% at a 10 µL/min flow rate. Moreover, studies have been conducted to support this claim, including simulation and biomimetic investigations. Full article

As the demand for sensing and monitoring the marine environment increases, the Ocean Mobile Internet of Things (OM-IoT) has gradually attracted the interest of researchers. However, the unreliability of communication links represents a significant challenge to data transmission in the OM-IoT, given the complex and dynamic nature of the marine environment, the mobility of nodes, and other factors. Consequently, it is necessary to enhance the reliability of underwater data transmission. To address this issue, this paper proposes a reinforcement learning-based adaptive network coding (RL-ANC) approach. Firstly, the channel conditions are estimated based on the reception acknowledgment, and a feedback-independent decoding state estimation method is proposed. Secondly, the sliding coding window is dynamically adjusted based on the estimates of the channel erasure probability and decoding probability, and the sliding rule is adaptively determined using a reinforcement learning algorithm and an enhanced greedy strategy. Subsequently, an adaptive optimization method for coding coefficients based on reinforcement learning is proposed to enhance the reliability of the underwater data transmission and underwater network coding while reducing the redundancy in the coding. Finally, the sampling period and time slot table are updated using the enhanced simulated annealing algorithm to optimize the accuracy and timeliness of the channel estimation. Simulation experiments demonstrate that the proposed method effectively enhances the data transmission reliability in unreliable communication links, improves the performance of underwater network coding in terms of the packet delivery rate, retransmission, and redundancy transmission ratios, and accelerates the convergence speed of the decoding probability. Full article