Search Results (1,513)

With the rapid advancement of information technology, the energy consumption of data centers has become a critical issue. Accurate cooling load prediction is essential for optimizing cooling system operations and improving energy efficiency. However, conventional models often struggle to capture the complex nonlinearities and multi-variable coupling effects inherent in data centers. To address the limitations of existing models in terms of training efficiency and generalization performance, this study proposes a cooling load prediction model that integrates the light gradient boosting machine (LightGBM) algorithm with Bayesian optimization. The model was validated using data generated from an EnergyPlus simulation of a representative medium-scale data center. Comparative analysis demonstrates that the proposed model surpasses naive benchmarks (T-1, T-24, and T-168) and other machine learning models (SVR, XGBoost, and LSTM), achieving superior performance with a Root Mean Squared Error (RMSE) of 4.3234 kW, R² of 0.9999, and Mean Absolute Percentage Error (MAPE) of 0.07%. A noise robustness analysis further reveals that the model maintains excellent performance under realistic uncertainties, achieving an R² above 0.99 and an RPD exceeding 12 even at high noise levels (SNR = 20 dB). The total runtime and Relative Prediction Deviation (RPD) were 33.45 s and 86.2685, respectively, indicating an excellent balance between computational efficiency and robust predictive reliability. The key contribution of this research is the effective integration of LightGBM and Bayesian optimization to provide a highly accurate and efficient tool for data center cooling load prediction. This approach offers a scientific foundation for the intelligent control of cooling systems and energy efficiency optimization in data centers, with direct practical implications for building energy management. Full article

Sensing mutual information (SMI) is widely adopted as a performance metric for integrated sensing and communication (ISAC) to enhance both sensing and communication capabilities. However, conventional approaches derive SMI from amplitude and phase, whereas an explicit evaluation of range mutual information (RMI) remains absent. In this paper, we investigate a novel closed-form approximation of RMI for ISAC. We first derive an explicit expression for the posterior probability density function (PDF) of the target range, which is formulated as a function of the signal’s autocorrelation and cross-correlation. Furthermore, we show that under high signal-to-noise ratio (SNR), the estimated range PDF approximates a Gaussian distribution in the sensing-unconstrained scenario and a truncated Gaussian distribution in the sensing-constrained scenario. Finally, we derive closed-form approximations of the RMI in both scenarios under high SNR. In the sensing-unconstrained scenario, the RMI is proportional to the delay interval, root-mean-square bandwidth, and SNR. In the constrained scenario, we obtain a closed-form RMI approximation by introducing an entropy correction term that quantifies the impact of boundary constraints. Additionally, we employ a maximum likelihood estimation (MLE) method to assess range estimation performance. Simulation results validate the accuracy of the theoretical results and the effectiveness of the proposed approximations. Full article

This paper presents an action potential detector (APD) based on a high-order non-linear energy operator (HONEO). The APD consists of a HONEO, a positive threshold generator, a negative threshold generator, and an XOR. The APD is capable of detecting the half-width of an action potential since it can determine both the positive peak and the negative peak of the action potential by means of the HONEO and two threshold generators. In addition, the signal-to-noise ratio (SNR) of the APD can also be improved due to the two adaptive threshold generators. The circuit is designed in a standard 0.18 $\mathsf{\mu }$m CMOS process with a 1.8 V supply voltage. Pre-layout simulations are performed under typical conditions (TT process corner, 1.8 V supply, 27 ${}^{\circ }$C). The results show that the output amplitudes of the HONEO remain almost constant ($\pm 100$ mV) when the amplitude of the source signal varies from −10 mV to 30 mV at 1 kHz. Across temperature variations from $-20$${}^{\circ }$C to 80 ${}^{\circ }$C, the output amplitude remains within $\pm 12\%$ of the nominal value, demonstrating acceptable stability for the target implantable application. Compared to the conventional NEO, the APD achieves 14–$20\mathrm{dB}$ SNR improvement, a detection accuracy of $97\%$. The power consumption of the APD is approximately $62\mathsf{\mu }\mathrm{W}$. Full article

The coupling of non-stationary marine noise and complex ship-radiated signals makes high-fidelity signal recovery exceptionally difficult. Existing deep learning methods often prioritize objective metrics, such as the Scale-Invariant Signal-to-Noise Ratio (SI-SNR), but fail to maintain the integrity of narrow-band line spectral data. We propose FocuS-MN, an end-to-end framework that combines learnable resampling with Feedforward Sequential Memory Network (FSMN)-based temporal modeling for precise waveform reconstruction. The model is optimized using a two-stage training strategy to ensure stable magnitude estimation and waveform consistency. On the ShipsEar dataset, FocuS-MN shows strong generalization to unseen vessel types. At a −5 dB Signal-to-Noise Ratio (SNR), it achieves a Signal-to-Distortion Ratio (SDR) of 3.77 dB and a Segmental Signal-to-Noise Ratio (SSNR) of 3.83 dB. Power Spectral Density (PSD) analysis further confirms that FocuS-MN recovers fine-grained line spectral structures, proving its effectiveness in both noise suppression and signal fidelity. Full article

To address the challenge of detecting internal defects in medium-thick titanium alloy laser welds, a combined simulation and experimental study on ultrasonic testing was conducted. A finite element model employing a 5 MHz shear wave angle transducer for inspecting titanium alloy welds was established. An ultrasonic testing system was developed, incorporating a DPR300 pulser-receiver (JSR Ultrasonics, Pittsford, NY, USA) and an MSO5204 oscilloscope (RIGOL, Suzhou, China), and was calibrated using standard reference blocks. The inspection results for four prefabricated internal defects at various depths demonstrated that all defects were effectively detected, with the minimum detectable equivalent defect size reaching 1 mm. The measured signal-to-noise ratio (SNR) averaged 17.6 dB, validating the high sensitivity of the proposed system. The mean absolute error for defect localization was 0.438 mm, achieving a positioning accuracy better than 0.5 mm. This study indicates that the pro-posed method enables effective detection and accurate localization of internal defects in titanium alloy laser welds, providing critical technical support for laser welding quality assessment. Full article

Geiger-mode avalanche photodiode (GM-APD) array light detection and ranging (LiDAR) has significant advantages in low-light scenes due to its single-photon-level detection sensitivity. However, it is susceptible to noise, which leads to a decrease in target localization accuracy. Traditional methods rely on long-term accumulation to distinguish signal photons from noise photons, making it difficult to achieve efficient processing, especially in scenarios with sparse echo photons and low signal-to-noise ratio (SNR), where performance is limited. To quickly and accurately obtain three-dimensional (3D) information of the target under such extreme conditions, this paper proposes a method for target detection and temporal window depth estimation based on intensity information guidance. First, noise suppression is performed on the intensity image according to its statistical characteristics, and an outlier detection mechanism based on neighborhood sparsity is introduced to remove outliers, thereby completing the target detection. Next, by exploiting the spatial continuity and reflectivity similarity of the target, local fusion of photon data within the target neighborhood is performed to construct highly consistent “superpixels”. Finally, according to the distribution difference between signal photons and noise photons on the time axis, temporal window screening is applied to the superpixels to extract depth information, and empty pixels are filled using a convex segmentation method to achieve depth estimation of the target. The experimental results demonstrate that under conditions of low photon counts and strong noise, the proposed method significantly outperforms traditional and existing methods in target recovery and depth estimation by effectively integrating target intensity information. Furthermore, this method achieves faster reconstruction speed, enabling high-precision and high-efficiency 3D target reconstruction. Full article

Phase-shifting fringe projection (PSFP) is widely used in industrial inspection and three-dimensional measurement, where $\gamma$ nonlinearity of the projector–camera system is traditionally treated as a phase-error source to be calibrated or compensated. In this work, $\gamma$ nonlinearity is reinterpreted from an imaging perspective and shown to act as a statistical distortion mechanism that reshapes modulation stability, overexposure behavior, and defect saliency in fringe-based imaging. Building on the intrinsic DC–AC decomposition of phase-shifting demodulation, we analyze how $\gamma$ nonlinearity interacts with fringe modulation and frequency-selective transfer. An analytical model reveals that $\gamma$ nonlinearity simultaneously suppresses the fringe fundamental and introduces harmonic leakage, leading to systematic compression of mean modulation contrast in high-brightness regions. As a result, $\gamma$ correction does not necessarily enhance mean-based defect contrast and may even reduce it, contrary to common intuition. We further demonstrate that the primary benefit of $\gamma$ correction lies in statistical stabilization rather than contrast amplification. By introducing modulation-domain saliency formulations and a frequency-domain harmonic energy ratio, a physical link is established between $\gamma$ nonlinearity, overexposure, and defect separability. Controlled experiments on highly reflective sheet-metal specimens confirm that while mean-contrast- and SNR-based saliency metrics often decrease after $\gamma$ correction, separability-based metrics consistently improve due to reduced nonlinear- and saturation-induced variance. Cross-channel and cross-condition analyses further show that modulation and reflectance images respond differently to $\gamma$ correction, yet metric-level separability exhibits consistent improvement across channels. These results clarify the true role of $\gamma$ correction in fringe-based inspection and provide theoretical insight and practical guidance for robust defect imaging under nonlinear and near-overexposure conditions. Full article

As the exponential growth in advanced compute workloads drives intra-datacenter interconnects to ever increasing bitrates, optical networking equipment has risen to the challenge by shifting from NRZ signaling to bandwidth efficient modulation methods such as PAM4. As these modulation schemes introduce an inherent SNR penalty, maintaining low bit error rates (BER) forces optical links to operate at significantly higher optical powers. However, increasing the optical power leads to photodetectors reaching one of their fundamental bottlenecks caused by the space-charge effect, limiting their ability to provide a high-speed response under high-power illumination. This work presents the design, fabrication, and characterization of a waveguide-integrated photodiode with dual optical inputs (DIPD) designed to overcome this limitation. Specifically, we demonstrate that combining a dual-fed architecture with targeted cross-sectional geometric optimizations effectively distributes the photocurrent density to delay the onset of space-charge saturation. Experimental validation demonstrates a high responsivity of ≈0.91 [A/W] (for O-band wavelengths) and a large electro-optic bandwidth (EOBW) of ≈58 [GHz], all under high-power illumination and CMOS driving voltages. Full article

High noise levels in industrial workplaces pose significant challenges to occupational safety, particularly with hearing protection and effective communication. Traditional hearing protection devices, while effectively attenuating harmful noise, often compromise situational awareness by excessively isolating workers from the acoustic environment and preventing the perception of critical auditory cues (e.g., emergency alarms), thereby introducing additional safety risks. This paper presents a smart industrial safety system that integrates Internet of Things (IoT) and artificial intelligence (AI) and is based on intelligent hearing protection devices to (a) selectively attenuate hazardous industrial noise while (b) preserving human speech and (c) reproduce targeted audio notifications to workers near malfunctioning or hazardous machinery. A real-time voice activity detection (VAD) model is employed to distinguish vocal components from background noise to adaptively control digital signal processing filters. Furthermore, indoor localization enables the delivery of targeted audio messages to workers in proximity to relevant events. Experimental evaluations on embedded hardware demonstrate that the selected VAD model operates well within real-time constraints and effectively supports dynamic noise filtering. Objective evaluation of the filtering stage using Mean Opinion Score (MOS), signal-to-noise ratio (SNR), and Harmonics-to-Noise Ratio (HNR) shows consistent quality improvements across all tested conditions, with MOS gains up to +118%, SNR increases between +10.4 and +29.0 dB, and HNR improvements up to +6.22 dB, indicating enhanced speech intelligibility and preservation of voice harmonic structure even under high-noise scenarios. Robustness validation of the VAD module across varying acoustic conditions confirms reliable speech detection performance, achieving perfect classification at +10 dB SNR, very high accuracy at 0 dB (98.3%, ROC AUC 0.998), and stable operation even at $-7$ dB SNR (79.8% accuracy, ROC AUC 0.878). The proposed architecture achieves a balanced trade-off between hearing protection and speech intelligibility while enhancing the effectiveness of safety communications in noisy industrial environments. Full article

High-fidelity star field simulation is paramount for target detection and space situational awareness (SSA) in geostationary and deep-space environments. However, accurately modeling the synergistic effects of ultra-dense stellar backgrounds and complex platform perturbations remains a formidable challenge. This paper proposes an integrated simulation framework that leverages the Gaia catalog to generate high-precision stellar environments. The core methodological novelty lies in the end-to-end coupling of a full optoelectronic imaging chain with dynamic platform disturbances, effectively bridging the gap between theoretical orbital dynamics and realistic sensor responses. Distinguishing itself from conventional models, our approach uniquely integrates radiative transfer and high-fidelity noise suites—including photon shot noise and non-uniform stray light—while utilizing the Gaia catalog to achieve unprecedented precision in simulating dim stars at low magnitudes. The fidelity of the proposed model was quantitatively validated against empirical data from a ground-based wide-field telescope (GTC). Experimental results, derived from multiple simulation realizations, demonstrate high consistency with real-world observations, achieving a Signal-to-Noise Ratio (SNR) error of less than 10% and a sub-pixel centroiding accuracy exceeding 0.01 pixels. This work provides a robust, high-fidelity data synthesis tool that significantly advances the development of target detection algorithms and the performance optimization of space-based optical sensors. Full article

Early and reliable detection of P-wave arrivals is critical for seismic monitoring and earthquake early warning, particularly under low signal-to-noise ratio (SNR) and non-stationary noise conditions. This study presents an automatic detection method based on singular value decomposition (SVD) entropy computed in sliding time windows with local signal filtering. Within this framework, the P-wave onset is interpreted as a local structural change in the signal rather than a simple energy increase. SVD entropy captures the redistribution of energy among dominant signal components, providing high sensitivity to the initial P-wave arrival even at moderate and low noise levels ($SNR\ge 2$). The method was validated using real seismic data from four regional stations operating under different noise conditions. Analysis of detection parameters revealed strong station dependence. For stations affected by low-frequency drift, polynomial detrending was identified as a necessary preprocessing step to ensure a stable entropy response and reliable detection. The proposed approach achieves detection accuracies of up to 93–98% at $SNR\ge 2$, significantly outperforming the classical STA/LTA algorithm and demonstrating performance comparable to modern deep learning methods. Since the method does not require model training or labeled datasets, it provides an interpretable and computationally efficient solution for automatic seismic monitoring. These properties make the proposed approach particularly suitable for real-time seismic monitoring systems and distributed sensor networks operating under limited computational resources. All computational stages were performed at the Farabi Supercomputer Centre of Al-Farabi Kazakh National University. The method requires no model training or labeled data, making it an interpretable, robust, and computationally efficient solution for automatic seismic monitoring and early warning systems. Full article

An integrated control strategy is proposed for direct-drive wave energy conversion (DDWEC) systems to address displacement safety constraints and improve the robustness of sensorless position estimation. Under strong wave excitation, buoy displacement may exceed its stroke limit due to conventional amplitude control, leading to mechanical risks. To mitigate this, a displacement-constrained damping regulation law is introduced, incorporating a displacement-dependent correction factor that retains optimal damping within a safe region and increases additional damping smoothly as the displacement approaches its limit. For sensorless operation, a dual-time-scale adaptive amplitude modulation strategy is developed, based on high-frequency square-wave voltage injection. By decoupling the fast position-estimation loop from the slow injection-amplitude adjustment, the demodulated high-frequency current remains within an optimal band, ensuring a high signal-to-noise ratio (SNR) under disturbances and parameter variations. Simulation results show that displacement boundary violations are eliminated, with a 25.7% reduction in peak displacement and only a 7.65% reduction in average captured power. The injection amplitude is adaptively regulated to maintain the demodulated current within the measurement band, enhancing position-estimation stability and accuracy. A fail-safe boundary for extreme sea states (H_s ≈ 2.2 m) is also identified, ensuring robust operation under varying conditions. Full article

The genus Polygonatum encompasses numerous species with complex phenotypes, necessitating robust molecular markers for accurate species identification and superior germplasm screening. This study identified and developed SSR markers based on transcriptome analysis of three Polygonatum species to assess the genetic diversity of Polygonatum resources. The results showed that a total of 43,217 SSR loci were detected, and 31,703 primer pairs were successfully designed. Characterization of SSR motifs revealed mono-nucleotide repeats (SNRs) were the most frequent (59.45%). Unigenes containing SSRs were annotated across seven databases. In KEGG, 222 pathways were assigned, with genes annotated to carbohydrate metabolism being the most abundant. To validate and apply these markers, 100 primer pairs covering all eight SSR locus types were tested across 21 Polygonatum accessions. Of these, 49 polymorphic markers were identified, revealing high genetic diversity, with average expected heterozygosity (He) and polymorphism information content (PIC) values of 0.763 and 0.718, respectively, alongside significant population differentiation (Fst = 0.307). Cluster analysis grouped 21 accessions into three groups, which correlated with certain agronomic traits. Nine core markers were selected that effectively distinguished six species and intraspecific groups. Notably, the FB-9 marker, associated with polysaccharide biosynthesis, effectively discriminated among six Polygonatum species and also distinguished distinct germplasm resources within P. kingianum var. grandifolium. Overall, the transcriptome-derived SSR markers validated in this study constitute valuable resources for gene function analysis, population genetics research, and variety identification and genetic improvement of Polygonatum. Full article

Unmanned aerial vehicle Synthetic Aperture Radar (UAV SAR) is inevitably affected by hardware performance and complex electromagnetic environments, resulting in noise in the radar echo signal. This causes image blurring and loss of detail, severely limiting the detection performance and imaging quality of UAV SAR. High-repetition-rate UAV SAR can achieve high signal-to-noise ratio (SNR), but the SAR data volume grows exponentially, posing a challenge for large-scale data processing. Furthermore, in the case of high repetition rate, downsampling methods are needed to reduce the amount of raw data, which leads to a decrease in the echo SNR, thus significantly affecting SAR image details. Existing SAR signal processing methods typically involve a series of processing steps on the raw echo data, such as azimuth and range direction processing. However, these traditional methods still have limitations in improving the SNR, especially in complex environments or when the target signal is weak, where their effectiveness is often unsatisfactory. To address these issues, this paper first analyzes the SNR gain in SAR echo data processing and proposes a phase-compensated parameter-adjusted Chebyshev filtering algorithm to improve the SNR of SAR echoes. The algorithm first utilizes azimuth Chebyshev filtering to avoid spectral aliasing during downsampling and fully leverages navigation information provided by the airborne platform to accurately compensate for phase changes between pulses. Then, it employs parameter-adjusted Chebyshev filtering and coherent superposition techniques to combine multiple adjacent pulses into a single pulse with a higher SNR. Finally, the enhanced pulses are combined into a new two-dimensional matrix for subsequent pulse compression and imaging processing. This method can improve the echo SNR while reducing the amount of echo data, minimizing the loss of the original echo SNR and reducing the memory footprint of subsequent imaging processing, thus effectively improving data processing efficiency. The effectiveness of the algorithm is verified through simulation and actual measurement data. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) poses a significant challenge for ISAR imaging. Conventional multi-target imaging methods, such as sequential CLEAN-based techniques, are often hindered by error propagation and sensitivity to noise, leading to degraded performance or even imaging failure, especially at low SNR. To address these issues, this paper proposes a novel robust imaging framework. The framework is built upon two key innovations: a partitioned block-wise compensation mechanism integrated with PSO for simultaneous and precise motion parameters estimation of multiple targets, which avoids local optima and error accumulation; and the application of Sequential Variational Mode Decomposition (SVMD) to adaptively separate and reconstruct signals, thereby suppressing inter-target aliasing and noise interference overlooked in prior studies. Simulations and measured-data experiments confirm that the proposed method maintains clear focusing and superior image quality even at low SNR, outperforming existing techniques in terms of image entropy, contrast, and resolution. This paper provides a robust and effective solution for high-resolution radar surveillance in complex multi-target scenarios. Full article