Search Results (125)

Multiphase flow metering is widely employed in the oil and gas industry, particularly for measuring gas-liquid-solid multiphase flow at drilling outlets. Low-energy gamma flowmeters offer relatively high metering accuracy, with phase fraction errors for gas, liquid, and solid typically within ±10%. However, in practical applications, fluid viscosity often causes substances to adhere to the photon detector, leading to measurement deviations that can reach 18% or more. To overcome this limitation, this paper proposes a hybrid Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-Transformer regression framework, referred to as D-Transformer. DBSCAN removes isolated abnormal detector responses before overlapping time-series windows are generated, while the Transformer captures temporal dependencies among operating variables, raw phase-fraction readings, and multi-energy photon counts. Under experiment-wise five-fold evaluation, D-Transformer obtains $R^2$ values of 0.982, 0.985, and 0.981 and RMSE values of 0.0134, 0.0122, and 0.0138 for the gas, liquid, and solid phase fractions, respectively. Component ablations and baseline comparisons show that the complete framework outperforms the no-ResNet, no-DBSCAN, CNN-GRU-Attention, CNN-LSTM, ridge-regression, and uncorrected-flowmeter alternatives. Full article

News recommendation is a critical technology that helps users efficiently find content of interest from large candidate pools. Its core objective is to accurately model user reading interests. However, current news recommendation systems typically suffer from two key limitations: (i) they fail to suppress noise from a frequency-domain perspective, and (ii) they lack effective calibration for popularity bias within the embedding space. In this work, we propose a novel frequency-domain denoising and popularity-bias correction recommendation network (FDDP-RN) to address both challenges simultaneously. Our approach introduces spectral analysis into the news encoder. Specifically, we design a filtering mechanism that combines truncation and scaling to enhance high-frequency semantic components, improve text feature representation accuracy, and suppress redundant low-frequency components. In addition, we introduce a norm-scaling factor that dynamically calibrates the embedding distribution of cold-start news items, placing them on an equal footing with popular news items. This effectively improves the exposure of long-tail content without requiring extra user interactions. We conduct extensive experiments on three public datasets, namely, MIND-small, MIND-large, and Adressa. The quantitative results demonstrate that FDDP-RN achieves state-of-the-art performance. Notably, on the Adressa dataset, our model achieves an AUC of 75.36% and an nDCG@10 of 50.11%, outperforming the strongest baseline. Furthermore, cold-start fairness diagnostics on the MIND-small dataset reveal that our mechanism increases the top-10 long-tail exposure rate from 15.3% to 18.1% and reduces the exposure Gini coefficient from 0.991 to 0.987, indicating a better balance among recommendation accuracy, diversity, and fairness. Full article

To address the key issues of uneven distribution of functional groups, large dispersion of holographic storage performance across different regions, and poor consistency of storage capacity in traditional PQ/PMMA holographic storage photopolymers, this paper introduces a low-viscosity reactive diluent, tripropylene glycol diacrylate (TPGDA), to modify the matrix. Leveraging the viscosity-reducing and double-bond crosslinking properties of TPGDA, the molecular diffusion behavior of the system was regulated. The effects of TPGDA doping ratio, the ratio of photosensitizer PQ to thermal initiator AIBN, and post-curing process on the holographic performance uniformity of the material were systematically investigated. The uniformity was quantitatively evaluated by the variance of diffraction efficiency at different points. Visible light absorption spectra and Fourier-transform infrared (FT-IR) spectroscopy were employed to reveal the modification mechanism from the perspective of functional group distribution. Actual-data read/write tests were conducted using a collinear holographic storage system. The experimental results show that the optimal TPGDA doping concentration is 40 wt%. For the optimized formulation TPGDA:MMA:AIBN:PQ = 8 g:12 g:0.20 g:0.18 g, the modified material achieves an average diffraction efficiency of 76.58%, and the diffraction efficiency variance decreases from 23.49 (pristine matrix) to 1.64, indicating a significant improvement in performance uniformity. Compared with pure PQ/PMMA, the modified material exhibits an approximately 1.95-fold increase in maximum diffraction efficiency, a 2-fold increase in recording rate, and a 1.6–1.75-fold increase in refractive index modulation. FT-IR spectroscopy confirms that TPGDA optimizes the spatial distribution uniformity of C=C and C=O functional groups. In collinear holographic measurements, the bit error rate (BER) variance of the modified sample is reduced by 40% relative to the pristine matrix, achieving homogeneous storage performance across the entire area while maintaining comparable signal-to-noise ratio (SNR) and BER. Additional short-time UV post-curing can further enhance the diffraction efficiency and refractive index modulation, and a thinner substrate can avoid performance fluctuations caused by incomplete thermal curing of thick samples. This study achieves directional optimization of the holographic uniformity of PQ/PMMA through reactive diluent viscosity reduction modification, providing a new strategy for the formulation design and engineering preparation of high-consistency holographic storage photopolymers. Full article

We study an agent who tracks several independent, unobserved, slowly drifting states and is paid by how well a chosen action matches each state but who can process only a bounded amount of information per period. The payoff environment is deliberately symmetric—quadratic matching losses, Gaussian drift, Gaussian observation noise—and the agent’s objective contains no asymmetry: we treat both the risk-neutral (linear) objective and the long-run log-growth (Kelly) objective. Within this symmetric environment, we show that the value of attentionis sharply asymmetric in the sign of the agent’s surprise. Because the matching payoff is maximized when action equals state, a surprisingly low payoff is strong evidence of a state mismatch that is worth correcting, whereas a surprisingly high payoff is evidence either of noise or of a match already achieved—in both cases carrying little decision-relevant information. We prove (Theorem 1) that the posterior expected mismatch, and hence the value of information, is strictly decreasing in the realized payoff, negligible for good surprises and rising steeply for bad ones, with a correspondingly asymmetric slope. We then show that an information-constrained agent optimally adopts a threshold attention policy (Theorem 2), which, under one explicit and standard bridge—that valuation inherits attention weight, as in salience and rational-inattention theories of choice—projects onto a reference-dependent value function with a kink at the expected payoff and a loss-side slope strictly steeper than its gain-side slope (Corollary 1): precisely the signature of loss aversion. The mechanism supplies the structure of loss aversion—its sign, its reference point, and how it varies with the environment—while its magnitude is one calibrated parameter that places the implied coefficient in the empirical range. Risk aversion follows as a corollary (Theorem 3): the kink induces first-order risk aversion over small symmetric gambles, inverting the usual hierarchy in which (second-order) risk aversion is primitive, and loss aversion is an add-on. The mechanism is immune to the Rabin calibration critique. Simulations benchmark the myopic policy against the computed optimum, map the mechanism’s robustness across noise tails, and locate the implied coefficient; we close with extensions to endogenous gain-seeking in convex (“gold-rush”) environments, population heterogeneity through learned priors, and a reading of hedonic affect as the Lagrange multiplier that prices a scarce attentional resource. Full article

Approximately 140,000 of the estimated one million orphaned wells in the United States have been documented, leaving the majority unaccounted for. These undocumented wells emit atmospheric methane and allow for hydrocarbon and brine groundwater migration. Many wells are difficult to locate due to subsequent covering or the removal of their surface casing, making manual identification impractical. Professional geophysical and remote sensing methods to locate orphaned wells are financially and technically inaccessible to the public, limiting their scalability. Accessible methods for identifying wells have been introduced, including drone and smartphone surveys, as well as artificial intelligence. Smartphone magnetometers are a low-cost alternative for locating steel-cased wells with greater spatial resolution than aerial magnetometry and portability than traditional handheld magnetometers. This study reviews existing techniques for orphaned well detection and presents smartphone magnetometry as a reliable well location tool. The resulting data from dynamic smartphone magnetic surveys exhibited limited background noise and a more precise well target than professional aerial surveys, while lowering cost and operational difficulty. Diffusion modeling generated synthetic data near the well, improving the resolution of anomalous magnetic readings. Smartphone surveys require minimal expertise, finances, and equipment, representing a simple method for a large-scale effort to address orphaned wells. Full article

Noisy, expensive, gradient-free optimisers—simulated annealing chief among them—almost never report how confident one should be in the configuration they return, and reinforcement-learning hyperparameter tuning, where the noise is large and the budget tight, is the setting where this silence hurts most. The contribution of this paper is a mechanism for uncertainty quantification, not a faster optimiser: we equip simulated annealing with a calibrated credible interval over the value of the recovered configuration, and we are explicit that this comes at an optimisation cost that only some landscapes repay. We introduce Kalman-Annealing (KA), a minimal modification of simulated annealing in which a one-dimensional Kalman filter—the canonical probabilistic numerical method—is interleaved with the Metropolis acceptance step. The filter denoises each return before acceptance, and a short terminal refinement of the best visited state converts the run into a calibrated credible interval over the value of the recovered hyperparameter. A single analytical identity, $Q_t=c{T}_t^2$, couples the filter process noise to the cooling schedule and absorbs the only free parameter of the filter into one already present in the metaheuristic. Under standard cooling assumptions the credible intervals are calibrated and the posterior variance contracts at a rate compatible with simulated-annealing convergence. On synthetic benchmarks (a noisy five-dimensional quadratic and the noisy Branin function, 200 seeds each) and on hyperparameter tuning of REINFORCE on three classic-control tasks (10 seeds each), the empirical $90\%$ coverage of KA’s credible intervals lies within sampling error of the nominal level—a property none of the baselines provides—and the optimiser overhead is close to four orders of magnitude below that of Gaussian-process Bayesian optimisation. The interval cannot be extracted for free from an unmodified SA run: an interval built from the trailing evaluations of the vanilla trajectory fails to calibrate in every reading we test, and the repair that does calibrate is exactly KA’s terminal-refinement phase grafted onto the unfiltered chain, at the same cost in diverted evaluations. Honest scoreboard: On simple regret, KA is at best on par with vanilla simulated annealing on the unimodal synthetic (the nominal advantage does not survive correction for multiple comparisons) and loses to the SA family, to CMA-ES and to Gaussian-process Bayesian optimisation on the multi-modal and ill-conditioned synthetics and on the informative reinforcement-learning tasks, under both REINFORCE and PPO. We trace this gap quantitatively to the filter acting as a low-pass, with a mean Kalman gain near one half, on the favourable-tail observations that drive SA’s basin escape, and we delineate the operating regime in which the calibrated-uncertainty contribution of KA is worth its optimisation cost. Full article

Background: False-positive variant calls generated within low-complexity regions (LCRs) remain a persistent bottleneck in clinical genomics, complicating downstream analysis. This study evaluates a targeted spatial masking strategy designed to suppress deterministic artifacts in short-read sequencing data, while preserving clinically actionable variants residing outside LCRs. We implemented a selective masking protocol prior to variant calling across analytical reference standards (EQA, NA12878) and two independent breast cancer whole-exome sequencing cohorts (n = 25). Methods: Callsets were evaluated for diagnostic sensitivity, precision gains, mutational signatures, VAF behavior, pseudo-multiallelic noise and ClinVar/dbSNP annotation. Results: The protocol removed thousands of sequencing and alignment artifacts while maintaining the retained biological callset, with negligible disease-associated diagnostic variants detected in the excluded artifact fraction. LCR masking preserved physiological Ti/Tv and Ins/Del profiles in retained calls, resolved pseudo-multiallelic noise, and distinguished excluded artifact calls by distorted mutational and VAF signatures. dbSNP profiling showed cohort-dependent behavior: TCGA-BRCA reproduced an intriguing phenomenon, with excluded calls showing higher dbSNP annotation than retained calls, whereas AURORA showed the opposite direction. Conclusions: These findings demonstrate the potential vulnerability of one-dimensional database annotation for variant authentication and highlight targeted spatial filtration as a critical, early pipeline intervention for high-fidelity clinical genomics of non-LCR-associated germline variants using short reads. Full article

Low-power CMOS image sensors increasingly rely on approximate analog front-end designs, including reduced ADC precision, relaxed voltage swing, and noise-tolerant readout circuits, to reduce energy consumption in always-on edge vision systems. However, the acceptable degradation boundary of such analog front-ends remains unclear when sensor outputs are consumed by downstream spatial perception workloads rather than conventional image-quality metrics. This paper presents a task-aware system-level evaluation framework for approximate CMOS image-sensor analog front-ends. We parameterize key circuit-level non-idealities, including ADC bit-depth reduction, temporal read noise, gain and offset variation, fixed-pattern noise, and dynamic-range clipping, and we evaluate how these impairments propagate through semantic, geometric, mapping, and spatial decision workloads. Across 10,500 end-to-end evaluations and 1996 geometric mapping trials, we identify a strong non-linear error cascade: semantic free-space extraction remains tolerant to aggressive quantization, whereas monocular depth and visual odometry impose much stricter analog front-end requirements. The results show that read noise and offset errors are the dominant failure sources for geometric perception, while controlled voltage swing clipping at 0.8 V can reduce front-end energy without degrading, and in some cases slightly improving, downstream reliability by suppressing high-intensity outliers. The analysis provides quantitative design boundaries for low-power CMOS image-sensor front-ends, including task-specific ADC precision, read-noise tolerance, voltage swing, PGA bypass, and offset calibration requirements. Full article

Reliable uncertainty quantification matters for high-stakes tabular classification, yet the comparative influence of architecture and post-hoc calibration on uncertainty quality remains underexplored, particularly outside in-distribution conditions. We present a matched-protocol benchmark on 36 OpenML-CC18 datasets comparing three GBDTs, a single MLP, MC-Dropout, and deep ensembles of three, five, and ten members under five post-hoc calibration methods, with additional evaluation under Gaussian feature shift, symmetric label noise, split-conformal coverage, and a sub-comparison against TabPFN v2. All paired comparisons use Wilcoxon signed-rank tests with Holm corrections within pre-specified research questions at $\alpha =0.05$. Using this benchmark, calibrator choice had a larger practical influence on uncertainty metrics than the difference between calibrated GBDTs and five-member neural ensembles. We observe a clear architecture-by-calibrator interaction: Dirichlet calibration was generally strongest on the evaluated GBDTs under low-to-moderate class imbalance, whereas temperature scaling was generally strongest on the evaluated neural models. Under controlled covariate shift, the in-distribution ordering reversed, with multinomial logistic recalibration showing the strongest performance among the tested calibrators on the evaluated neural models under heavy perturbation, and under high class imbalance, the preference for Dirichlet calibration on the evaluated GBDTs weakened. GBDTs were run at recommended defaults rather than tuned per dataset, and the covariate-shift protocol uses synthetic Gaussian noise rather than naturalistic out-of-distribution data; the shift-related findings should be read as directional indicators within this protocol. Calibration and architecture should therefore be selected jointly. The preferred calibrator depends on the model family in-distribution and changes again under perturbation. Full article

Reliable altitude and vertical speed estimation are fundamental for unmanned aerial vehicle (UAV) autonomous flight, especially during low-altitude operations such as takeoff and landing. Barometric altimeters are widely used due to their low cost, high availability, and good long-term stability, providing smooth altitude trends over a wide operating range. However, barometric measurements are indirectly inferred from static pressure and are therefore sensitive to local airflow disturbances. In particular, rotor downwash and ground effect-induced pressure perturbations near the surface can introduce significant biases and short-term fluctuations in barometric altitude, which propagate into erroneous vertical speed estimates during critical flight phases. Time-of-flight (TOF) altimeters, such as radar or laser sensors, provide direct above-ground-level (AGL) measurements and are largely insensitive to ground effect-related pressure disturbances. Within their limited operational range, TOF altimeters typically offer higher accuracy and lower short-term noise compared with barometric altitude. Nevertheless, TOF sensors are characterized by a restricted valid measurement range and frequently exhibit non-ideal behaviors in real-world UAV operations, including out-of-range outputs, frozen measurements, and in-range biased readings. These anomalies violate the nominal sensor assumptions used in conventional Kalman filter-based fusion and can significantly degrade estimation performance if not properly handled. This paper proposes a hybrid Kalman–rule-based altitude estimation framework that fuses barometric and TOF altitude measurements to exploit their complementary characteristics while mitigating their respective limitations. A vertical dynamic state-space model is formulated to jointly estimate altitude, vertical velocity, accelerometer bias, and ground height offset. A rule-based anomaly detection and classification module is developed to identify multiple TOF altimeter failure modes observed in operational UAV flights. The detected anomaly states are incorporated into the Kalman filter to adaptively weight, accept, or reject TOF measurements, thereby improving robustness against sensor non-idealities. The proposed approach is validated using 39 real UAV flight logs covering diverse flight regimes, including low-altitude maneuvers, cruise, and autonomous landing. Experimental results show that the proposed framework provides more stable and robust altitude and vertical speed estimation under practical sensor anomaly conditions compared with conventional barometer-only and standard Kalman fusion configurations. These results demonstrate the practical effectiveness of the proposed method for fault-aware altitude estimation in UAV autonomous flight. Full article

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article

Dark current represents a fundamental limiting factor in photodiode performance, establishing the noise floor and constraining detectivity in low-light applications. This comprehensive literature review examines publications covering the physical mechanisms underlying dark current generation and diverse techniques employed for its reduction. Covered mechanisms include diffusion current, Shockley–Read–Hall (SRH) generation–recombination, trap-assisted tunneling, band-to-band tunneling, and surface leakage, each examined with respect to its physical origin and characteristic signatures. Reduction strategies are categorized into thermal management approaches, surface passivation techniques including atomic-layer-deposited aluminum oxide (ALD Al₂O₃), guard ring architectures (attached, floating, and combined configurations), gettering and defect engineering methods, doping profile optimization, bias voltage management, and advanced device architectures such as pinned photodiodes and black silicon structures. A classification table organizes all the reviewed literature by material system, reduction technique, and key findings. Special emphasis is placed on silicon, germanium, III–V compounds, and emerging material photodiodes relevant to near-infrared detection, CMOS imaging, single-photon avalanche diodes (SPADs), and Time-of-Flight (ToF) applications. Full article

The automatic reading recognition of digital instruments is crucial for achieving metering automation and intelligent inspection. However, in non-standardized industrial environments, the masking effect caused by the coupling of non-uniform low-light conditions and the reflective surfaces of instrument panels severely degrades the displayed information, significantly limiting the recognition performance. Conventional image processing methods, while aiming to restore the imaging quality of instrument panels through low-light enhancement, inevitably introduce overexposure and indiscriminately amplify background noise during this process. To address the two key challenges of illumination recovery and noise suppression in the process of restoring panel image quality under non-uniform low-light conditions, this paper proposes a coarse-to-fine cascaded perception framework (CFCP). First, a lightweight YOLOv10 detector is employed to coarsely localize the meter reading region under non-uniform illumination conditions. Second, an Adaptive Illumination Correction Module (AICM) is designed to decouple and correct the illumination component at the pixel level, effectively restoring details in dark areas. Then, an Illumination-invariant Feature Perception Module (IFPM) is embedded at the feature level to dynamically perceive illumination-invariant features and filter out noise interference. Finally, the refined detection results are fed into a lightweight sequence recognition network to obtain the final meter readings. Experiments on a self-built industrial digital instrument dataset show that the proposed method achieves 93.2% recognition accuracy, with 17.1 ms latency and only 7.9 M parameters. Full article

Space gravitational wave detection is performed via a laser interferometry system across hundreds of thousands to millions of kilometers for picometer-level displacement measurement, using phasemeters to read gravitational wave-induced displacement changes. A critical yet unresolved challenge is the coupling of Doppler frequency shift—resulting from relative satellite motion—into the phase measurements, as well as its consequent impact. To address this, we analyzed the Doppler effect principle, built a laser interferometry signal model, and obtained signal frequency ranges via orbit simulation. We then conducted time- and frequency-domain analyses of the phasemeter, theoretically deriving steady-state phase errors to clarify how Doppler shift affects phasemeter noise. A hardware system was constructed for verification, showing that phase noise curves rise significantly at a 100 Hz/s Doppler shift rate, and increasing phasemeter bandwidth increases low-frequency phase noise. This study provides a theoretical and experimental basis for phasemeter parameter optimization and ground experiments of phasemeters in space gravitational wave detection. Full article

For sensing applications, a complementary metal oxide semiconductor (CMOS) image sensor (CIS) with a lateral overflow integration capacitor (LOFIC) is in high demand. The LOFIC CIS can achieve high-dynamic-range (HDR) imaging by combining a low-conversion-gain (LCG) signal for large maximum signal electrons and a high-conversion-gain (HCG) signal for a low electron-referred noise floor. However, the LOFIC CIS faces challenges regarding the power consumption and circuit area when reading both HCG and LCG signals. To address these issues, this study proposes a readout circuit composed of area-efficient MOS capacitors using a folding DC operating point technique and an in-column signal selector for an on-chip HDR merger of HCG and LCG signals. A 10-bit test chip was fabricated with a $0.18$ µm CMOS process with MOS capacitors. The fabricated chip maintains high linearity, achieving an integral nonlinearity (INL) of +7.17/−6.93 LSB for the HCG signal and +7.95/−7.41 LSB for the LCG signal. Furthermore, the proposed design achieves a $14.92\%$ reduction in the average power consumption of the total readout circuit and a $36.5\%$ reduction in the readout circuit area. Full article