Search Results (62)

Effectively leveraging multi-source unmanned aerial vehicle (UAV) observations for reliable object recognition is often compromised by environmental extremes (e.g., occlusion and low illumination) and the inherent physical discrepancies between modalities. To overcome these limitations, we propose DLANet, a lightweight, rotation-aware multimodal object detection framework that introduces a dual-level attention relearning strategy to maximize complementary information from visible (RGB) and thermal infrared (TIR) imagery. DLANet integrates two novel components: the Implicit Fine-Grained Fusion Module (IF²M), which facilitates deep cross-modal interaction by jointly modeling channel and spatial dependencies at intermediate stages, and the Adaptive Branch Feature Weighting (ABFW) module, which dynamically recalibrates modality contributions at higher levels to suppress noise and pseudo-targets. This synergistic approach allows the network to relearn feature importance based on real-time scene conditions. To support industrial applications, we construct the OilLeak dataset, a dedicated benchmark for onshore oil-spill detection. The experimental results demonstrate that DLANet achieves state-of-the-art performance, recording an mAP_0.5 of 0.858 on the public DroneVehicle dataset while maintaining high efficiency, with 39.04 M parameters and 72.69 GFLOPs, making it suitable for real-time edge deployment. Full article

System reliability, which is essential for the normal operation of satellites in orbit, is decisively governed by the performance of solar array, making accurate temperature forecasting of solar array imperative. Reliable solar array temperature forecasting is essential for predictive maintenance and autonomous power-system management. Forecasting relies on temperature telemetry data, which provide comprehensive thermal information. This task remains challenging due to the high-dimensional, long-horizon temperature sequences with inherent cross-variable coupling, whose dynamics exhibit nonlinear and non-stationary behaviors owing to orbital transitions and varying operational modes. In this context, multi-step forecasting is essential, as it better characterizes long-term dynamics of temperature and provides forward-looking trends that are beyond the capability of single-step forecasting. To tackle these issues, we propose a solar array temperature multivariate trend forecasting method based on Cross-Attention Patch Time Series Transformer (CA-PatchTST). Specifically, we decompose temperature variables into trend and residual components using a moving average filter to suppress noise and highlight the dominant component. In addition, the PatchTST model extracts local features and long-term dependencies of the trend and residual components separately through the patching encoders and channel-independent mechanisms. The cross-attention mechanism is designed to capture the correlation between temperature variables of different devices in solar array. Extensive experiments on the real solar array temperature dataset demonstrate that the CA-PatchTST surpasses mainstream baselines in root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE), with ablation studies further confirming the complementary roles of sequence decomposition and cross-attention. Full article

Phased array radar, with its electronic scanning, high reliability, and multifunctionality, has become a core equipment for unmanned aerial vehicle detection, modern air defense, meteorological monitoring, and satellite communication. The T/R module is the core equipment of the active phased array radar, and its performance largely determines the performance of the phased array. At the same time, the application scenario requires relatively high transmission gain and transmission power, so attention should be paid to its heating situation. In addition, the overall size requirements for components are gradually becoming stricter, and miniaturization has become a trend in the development of T/R modules. This paper presents a four-channel T/R module in an X-band based on LTCC technology. In order to reduce weight and have high-density electronic devices, this module uses the latest technologies such as low-temperature cofired ceramic substrate (LTCC), Monolithic Microwave Integrated Chip (MMIC), and the MIC assembly process, and is hermetically sealed. The transmission channel of this module has high gain and high power, and the RF signal is transmitted through an eight-layer LTCC board to reduce interference between adjacent signal transmission lines and reduce the module size at the same time. The method of dividing the transmission and reception channels using a metal shell frame reduces crosstalk between the input and output ports of the transmission channel. Good heat dissipation design ensures the thermal management of the module. The test results show that the size of the TR module is 70 mm × 55 mm × 10 mm, the transmission power is ≥39 dBm, the reception gain is >28 dB, and the noise figure is <3 dB. Full article

This work presents a novel Gallium Nitride (GaN) high-electron-mobility transistor (HEMT)-based ultraviolet (UV) photodetector architecture that integrates advanced material and structural design strategies to enhance detection performance and stability under room-temperature operation. This study is conducted as a fully numerical simulation using the Silvaco Atlas platform, providing detailed electrothermal and optoelectronic analysis of the proposed device. The device is constructed on a high-thermal-conductivity silicon carbide (SiC) substrate and incorporates an n-GaN buffer, an indium nitride (InN) channel layer for improved electron mobility and two-dimensional electron gas (2DEG) confinement, and a dual-passivation scheme combining silicon nitride (SiN) and hexagonal boron nitride (h-BN). A p-GaN layer is embedded between the passivation interfaces to deplete the 2DEG in dark conditions. In the device architecture, the metal contacts consist of a 2 nm Nickel (Ni) adhesion layer followed by Gold (Au), employed as source and drain electrodes, while a recessed gate embedded within the substrate ensures improved electric field control and effective noise suppression. Numerical simulations demonstrate that the integration of a hexagonal boron nitride (h-BN) interlayer within the dual passivation stack effectively suppresses the gate leakage current from the typical literature values of the order of ${10}^{-8}$ A to approximately ${10}^{-10}$ A, highlighting its critical role in enhancing interfacial insulation. In addition, consistent with previous reports, the use of a SiC substrate offers significantly improved thermal management over sapphire, enabling more stable operation under UV illumination. The device demonstrates strong photoresponse under 360 nm ultraviolet (UV) illumination, a high photo-to-dark current ratio (PDCR) found at approximately ${10}^6$, and tunable performance via structural optimization of p-GaN width between 0.40 μm and 1.60 μm, doping concentration from $5\times {10}^{16}$ ${\mathrm{cm}}^{-3}$ to $5\times {10}^{18}$ ${\mathrm{cm}}^{-3}$, and embedding depth between 0.060 μm and 0.068 μm. The results underscore the proposed structure’s notable effectiveness in passivation quality, suppression of gate leakage, and thermal management, collectively establishing it as a robust and reliable platform for next-generation UV photodetectors operating under harsh environmental conditions. Full article

Detecting small targets in infrared imagery remains highly challenging due to sub-pixel target sizes, low signal-to-noise ratios, and complex background clutter. This paper proposes PSHNet, a hybrid deep-learning framework that combines dense spatial heatmap supervision with geometry-aware regression for accurate infrared small-target detection. The network generates position–scale heatmaps to guide coarse localization, which are further refined through sub-pixel offset and size regression. A Complete IoU (CIoU) loss is introduced as a geometric regularization term to improve alignment between predicted and ground-truth bounding boxes. To better preserve fine spatial details essential for identifying small thermal signatures, an Enhanced Low-level Feature Module (ELFM) is incorporated using multi-scale dilated convolutions and channel attention. Experiments on the NUDT-SIRST and IRSTD-1k datasets demonstrate that PSHNet outperforms existing methods in IoU, detection probability, and false alarm rate, achieving IoU improvement and robust performance under low-SNR conditions. Full article

In this paper, a four-channel optical emission module is developed using hybrid integration technology that integrates directly modulated laser (DML) chips, low-noise amplifier (LNA) chips, and control circuits, with dimensions of 24.4 mm × 21 mm × 5.9 mm. This module enables high-gain signal output and minimizes crosstalk between neighboring channels while improving integration. An equivalent circuit model of radio frequency (RF) signal transmission is established, and the accuracy of the model and the effectiveness of the approach to improve signal gain are verified using simulations and experiments. With optimized thermal management, the module has the ability to operate at stable temperatures across an ambient range of −55 °C to 75 °C. The module has a channel wavelength spacing of approximately 1 nm, and the −3 dB bandwidth of each channel exceeds 20 GHz. The crosstalk between neighboring channels is less than −65 dB. In the range of 0.8~25 GHz, the four-channel gain is approximately 15 dB through the integration of the LNA chip. The module achieves a noise figure (NF) of less than 30 dB. Full article

Using chaos for communication can provide more robust channel security, covert transmission, and inherent support for spread-spectrum modulation. Although numerous studies have explored this technology, its practical deployment remains limited due to substantial hardware demands, complex signal processing, and a lack of efficient modulation methods for chaotic signals. In this study, a novel chaotic digital communication system is proposed and studied. A prototype of a frequency-modulated antipodal chaos shift keying (FM-ACSK) system is implemented on an Intel Cyclone V field-programmable gate array (FPGA) along with a complete mathematical model using Matlab R2022a Simulink software. Using FPGAs to implement chaotic oscillators avoids analog system problems such as component drift and high thermal instability while providing determined system parameters, rapid prototyping, and high throughput. The employment of FM over a chaotic modulation layer provides a passband operation (currently at an intermediate frequency of 10.7 MHz) while adding the benefits of carrier frequency offset robustness and constant signal envelope. Within this study, the robustness of FM-ACSK to white noise in the channel was evaluated using bit error rate, which was tested through hardware experiments and simulations. The results show the feasibility and potential performance limitations of this approach to chaotic communication system design. Full article

Micro-electro-mechanical system (MEMS) gas flowmeters are innovative devices that use microfabrication technology to measure gas flow with high precision and sensitivity. With MEMS technology, flow measurement can now be performed more accurately and compactly than ever, using low-power, compact, and highly accurate sensors. MEMS gas flowmeters utilize various principles to measure gas flow, including thermal, Coriolis, and pressure differential methods. A micro-flowmeter was developed by combining a MEMS sensor with a weak signal acquisition technique. High heat isolation and sensitivity can be achieved using a MEMS sensor with a thermal resistor-suspended VO₂ structure. Since SU-8 gum is used for the flow channel, the technology is simple and affordable, making it suitable for batch production. To acquire high-resolution, low-noise data, the device uses a super low bias current operational amplifier, aided by guard ring protection, and a 24-bit high-resolution ADC. The sensor and data acquisition combination shows that the flowmeter has favorable linearity and sensitivity between 0 and 50 mL/min at a specific offset voltage. Biochemical detection and medicine require a high-sensitivity, high-stability, and low-cost flowmeter. Full article

Side-channel attacks (SCAs) are powerful techniques used to recover keys from electronic devices by exploiting various physical leakages, such as power, timing, and heat. Although heat is one of the less frequently analyzed channels due to the high noise associated with thermal traces, it poses a significant and growing threat to the security of very large-scale integrated (VLSI) microsystems, particularly system in package (SiP) technologies. Thermal side-channel attacks (TSCAs) exploit temperature variations, risking not only hardware damage from excessive heat dissipation but also enabling the extraction of sensitive data, like cryptographic keys, by observing thermal patterns. This dual threat underscores the need for a synergistic approach to thermal management and security in designing integrated microsystems. In response, this paper presents a novel approach that improves the early detection of abnormal thermal fluctuations in SiP designs, preventing cybercriminals from exploiting such anomalies to extract sensitive information for malicious purposes. Our approach employs a new concept called Thermal Digital Twin (TDT), which integrates two previously separate methods and techniques, resulting in successful outcomes. It combines the gradient direction sensor scan (GDSSCAN) to capture thermal data from the physical field programmable gate array (FPGA), which guarantees rapid thermal scan with a measurement period that could be close to $10 \mathsf{\mu }\mathrm{s},$ a resolution of ${0.5 }^{\circ }$C, and a temperature range from −${40 }^{\circ }$C to ${140 }^{\circ }$C; once the data are transmitted in real time to a Digital Twin created in COMSOL Multiphysics^® 6.0 for simulation using the Finite Element Method (FEM), the real time required by the CPU to perform all the necessary calculations can extend to several seconds or minutes. This integration allows for a detailed analysis of thermal transfer within the SiP model of our FPGA. Implementation and simulations demonstrate that the Thermal Digital Twin (TDT) approach could reduce the risks associated with TSCA by a significant percentage, thereby enhancing the security of FPGA systems against thermal threats. Full article

As the complexity and integration of electronic devices increase, understanding and mitigating side-channel vulnerabilities will remain a critical area of cybersecurity research. The new and intriguing software-based thermal side-channel attacks and countermeasures use thermal emissions from a device to extract or defend sensitive information, by reading information from the built-in thermal sensors via software. This work extends the Hot-n-Cold anomaly detection technique, applying it in circumstances much closer to the real-world computational environments by detecting irregularities in the Linux command behavior through CPU temperature monitoring. The novelty of this approach lies in the introduction of five types of noise across the CPU, including moving files, performing extended math computations, playing songs, and browsing the web while the attack detector is running. We employed Hot-n-Cold to monitor core temperatures on three types of CPUs utilizing two commonly used Linux terminal commands, ls and chmod. The results show a high correlation, approaching 0.96, between the original Linux command and a crafted command, augmented with vulnerable system calls. Additionally, a Machine Learning algorithm was used to classify whether a thermal trace is augmented or not, with an accuracy of up to 88%. This research demonstrates the potential for detecting attacks through thermal sensors even when there are different types of noise in the CPU, simulating a real-world scenario. Full article

As field effect transistors are reduced to nanometer dimensions, experimental and theoretical research has shown a gradual change in noise generation mechanisms. There are few studies on noise theory for small nanoscale transistors, and Monte Carlo (MC) simulations mainly focus on 2D devices with larger nanoscale dimensions. In this study, we employed MC simulation techniques to establish a 3D device simulation process. By setting device parameters and writing simulation programs, we simulated the raw data of channel current noise for a silicon-based metal–oxide–semiconductor field-effect transistor (MOSFET) with a 10 nm channel length and calculated the drain output current based on these data, thereby achieving static testing of the simulated device. Additionally, this study obtained a 3D potential distribution map of the device channel surface area. Based on the original data from the simulation analysis, this study further calculated the power spectral density of the channel current noise and analyzed how the channel current noise varies with gate voltage, source–drain voltage, temperature, and substrate doping density. The results indicate that under low-temperature conditions, the channel current noise of the 10 nm MOSFET is primarily composed of suppressed shot noise, with the proportion of thermal noise in the total noise slightly increasing as temperature rises. Under normal operating conditions, the channel current noise characteristics of the 10 nm MOSFET device are jointly characterized by suppressed shot noise, thermal noise, and cross-correlated noise. Among these noise components, shot noise is the main source of noise, and its suppression degree decreases as the bias voltage is reduced. These findings are consistent with experimental observations and theoretical analyses found in the existing literature. Full article

The fusion tracking of RGB and thermal infrared image (RGBT) has attracted widespread interest within target tracking by leveraging the complementing benefits of information from both visible and thermal infrared modalities, but achieving robustness while operating in real time remains a challenge. Aimed at this problem, this paper proposes a real-time tracking network based on the attention mechanism, which can improve the tracking speed with a smaller model, and at the same time, introduce the attention mechanism in the module to strengthen the attention to the important features, which can guarantee a certain tracking accuracy. Specifically, the modal features of visible and thermal infrared are extracted separately by using the backbone of the dual-stream structure; then, the important features in the two modes are selected and enhanced by using the channel attention mechanism in the feature selection enhancement module (FSEM) and the Transformer, while noise is reduced by using gating circuits. Finally, the final enhancement fusion is performed by using the spatial channel adaptive adjustment fusion module (SCAAM) in both the spatial and channel dimensions. The PR/SR of the proposed algorithm tested on the GTOT, RGBT234 and LasHeR datasets are 90.0%/73.0%, 84.4%/60.2%, and 46.8%/34.3%, respectively, and generally good tracking accuracy has been achieved, with a speed of up to 32.3067 fps, meeting the model’s real-time requirement. Full article

Secret key exchange relies on the creation of correlated signals, serving as the raw resource for secure communication. Thermal states exhibit Hanbury Brown and Twiss correlations, which offer a promising avenue for generating such signals. In this paper, we present an experimental implementation of a central broadcast thermal-state quantum key distribution (QKD) protocol in the microwave region. Our objective is to showcase a straightforward method of QKD utilizing readily available broadcasting equipment. Unlike conventional approaches to thermal-state QKD, we leverage displaced thermal states. These states enable us to share the output of a thermal source among Alice, Bob, and Eve via both waveguide channels and free space. Through measurement and conversion into bit strings, our protocol produces key-ready bit strings without the need for specialized equipment. By harnessing the inherent noise in thermal broadcasts, our setup facilitates the recovery of distinct bit strings by all parties involved. Full article

The space-borne synthetic aperture radar (SAR) azimuth multi-channel system has extensive applications because it can achieve high-resolution and wide-swath radar imaging. The thermal noise generated by the radar receiver of each channel during operation will cause an imbalance between channels. If the echoes of each channel are quantized with the same number of bits without considering the influence of thermal noise, false targets will appear in the imaging consequences. Considering that the thermal noise generated in the receiver will affect the quantization process of the space-borne SAR azimuth multi-channel system, a new space-borne SAR azimuth multi-channel quantization method is proposed to improve this problem. Firstly, the pure noise power of the receiver is calculated without transmitting the radar signal. The signal power is estimated by subtracting the pure noise power from the total power. Then, the average value of the radar echo signal minus k times the standard deviation is used as the left endpoint of the original data amplitude range, and the average value of the radar echo signal plus k times the standard deviation is used as the right endpoint of the original data amplitude range. The original echo data after adjusting the amplitude range is quantified. This method can effectively reduce the influence of thermal noise and random outliers in the receiver on quantization and suppress the appearance of false targets. Finally, simulation is used to confirm the viability of the suggested quantization approach. Full article

This paper introduces a new simple moment-generating function (MGF) design modelling method to conclude an optimum filter to maximize the Q-factor and increase the link communication span. This approach mitigates the pulse temporal dispersion, particularly the underwater wireless optical communication (UWOC) systems. Hence, some form of equalizing filter design is highly desirable. The model solution environment includes a Double Gamma Function (DGF) water channel impulse response, intersymbol interference (ISI), stochastic Poisson process, and additive Gaussian thermal noise (AGTN). The optimal filters exhibit temporal profiles comparable to those derived by published works based on complex Chernoff Bound (CB) and Modified Chernoff Bound (MCB) methods. The results show the impact of the optimum filter at a signal level and optical receiver level utilizing Eye-Diagrams and BER vs. Q-Factor, respectively. The computation involves four different UWOC propagation channel models for Coastal and Harbor waters. One of the main conclusions indicates that the optimum filter manages the temporal dispersion due to the ISI impairment correctly. Also, the proposed optimum filter reduces eye-opening and the corresponding Q-Factor by less than 15% for a five-times increase in pulse width for the same transmitted optical power level. Full article