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In the paper, a digital clock stopping technique for gain and offset correction in time-mode analog-to-digital converters (ADCs) has been proposed. The technique is dedicated to imagers with massively parallel image acquisition working in the time mode where compensation of dark signal non-uniformity (DSNU) as well as photo-response non-uniformity (PRNU) is critical. Fixed pattern noise (FPN) reduction has been experimentally validated using 128-pixel CMOS imager. The reduction of the PRNU to about 0.5 LSB has been achieved. Linearity improvement technique has also been proposed, which allows for integral nonlinearity (INL) reduction to about 0.5 LSB. Measurements confirm the proposed approach.

While radiographic imaging is the gold standard for assessing spinopelvic alignment, it may not fully reflect symptom severity in patients with lumbar spinal stenosis (LSS) as patients employ dynamic compensatory strategies. This study aimed to develop a method to align static spinopelvic alignment parameters derived from motion capture with radiographic definitions. 27 patients underwent EOS radiography and motion capture analysis in a standardized posture. Radiopaque and retroreflective markers were placed on the same anatomical landmarks before EOS radiography and motion capture analysis, respectively. Offset angles were calculated to align motion capture-derived with radiographic parameters. Postural agreement between the two modalities was assessed using Bland-Altman analysis of the vertical distances between the posterior and anterior superior iliac spine markers (ASIS-PSIS) and the horizontal distances between the C7 and sacrum markers (SACR-C7). The influence of postural variation between modalities on alignment parameters was estimated using trigonometric analysis. Radiographic parameters differed notably from motion-capture derived parameters, particularly sacral slope, with an average offset of 31.1° (range: –0.4°–46.4°). The mean vertical ASIS-PSIS distance was −3.3 mm (LoA (limits of agreement): [−21.4; 14.8] mm) and the mean horizontal SACR-C7 distance was +4.9 mm (LoA: [−16.3; 26.1] mm), corresponding to maximum angular deviations of 5.9° for sacral slope and 3.7° for spine inclination. In conclusion, the large offset ranges underscore the need for radiography and individual offset corrections to approximate spinopelvic alignment parameters using motion capture. However, the close replication of the EOS posture highlights this method’s potential for assessing spinopelvic alignment in dynamic conditions.

Optical tracking of unknown space objects requires both spatial accuracy and temporal stability to enable high-resolution identification through narrow field-of-view sensors. Traditional performance indices such as RMS error and persistence time (PT) have been used for controller tuning, but they each capture only a subset of the requirements for successful optical identification. This paper proposes a new composite metric, the Persistence-Weighted Tracking Index (PWTI), which combines spatial precision and segment-level continuity into a single measure. The metric assigns a frame-level score based on positional error and accumulates weighted scores over the longest continuous in-threshold segment. Using PWTI as the optimization objective, a genetic algorithm (GA) is employed to tune the PID gains of a frame-by-frame offset correction controller. Comparative simulations under various observation scenarios demonstrate that the PWTI-based approach outperforms RMS- and PT-based tuning methods in both alignment accuracy and consistency. The results validate the proposed metric as a more suitable performance indicator for optical identification tasks involving unknown or uncataloged targets.

Satellite videos typically employ image registration techniques for video stabilization in order to achieve persistent observation. However, existing methods largely neglect the residual stabilization offsets, particularly when exceeding the physical dimensions of target vehicles, which inevitably causes performance degradation. Furthermore, the detection pipeline struggles with hard-to-discriminate samples that exhibit low contrast, motion blur, or occlusion, while conventional sample assignment strategies fail to address the inherent annotation ambiguity for extremely small objects. We propose an end-to-end method called ViTrans for detecting moving vehicles in satellite video under stabilization offsets. ViTrans consists of three core modules: (1) a feature-aligned stabilization offset correction module (SCM) that mitigates feature misalignment by aligning features between the reference frame and the current frame; (2) a feature adaptive aggregation enhancement module (AAEM) based on vehicle trajectory consistency, which leverages the motion characteristics of objects across consecutive frames to eliminate dynamic clutter and false-alarm artifacts; and (3) a Gaussian distribution-based metric that dynamically adapts to bounding box dimensions, thereby providing more accurate positive sample feedback during model training. Extensive experiments on the VISO and SDM-Car datasets under simulated stabilization offsets demonstrate that ViTrans achieves state-of-the-art performance, improving F1-score by 14.4% on VISO and 6.9% on SDM-Car over existing methods.

The Gravity Recovery and Climate Experiment Follow-On (GRFO) mission precisely measures the inter-satellite range between the centers of mass of its twin satellites to map the earth’s gravity field. The baseline ranging measurement is achieved using the K-band ranging (KBR) system, which is sensitive to satellite attitude variations caused by the offset between the satellite center of mass and the KBR antenna phase center. Accurate decoupling of the KBR range from attitude variations requires precise determination of the KBR’s antenna offset vectors (AOVs). To address this, GRFO conducted eight KBR calibration maneuvers on 17 and 28 September 2020. However, these maneuvers exaggerated the impact of microwave multipath noise, complicating AOV estimation. Existing studies have not fully mitigated this noise. This study introduces a new frequency-domain method to estimate AOVs by leveraging double-difference signals and analyzing their spectral characteristics, along with those of the KBR range during calibration maneuvers, to suppress multipath noise. Our recalibrated AOVs achieve good alignment between the KBR and laser ranging interferometer (LRI) ranging signals. We validate our recalibrated AOVs by comparing the residuals between the LRI and KBR ranging signals corrected using both recalibrated AOVs and documented AOVs. The results show that, for the majority (58.4%) of the analyzed period (from January 2020 to June 2023), the residuals corrected by the recalibrated AOVs are closer to the LRI ranging signal. These findings demonstrate the effectiveness of the proposed method in addressing multipath noise and improving the accuracy of KBR range measurements. This work provides a framework for future gravity missions requiring precise calibration of multipath effects in inter-satellite ranging systems.

Thermal images are widely used for various healthcare applications and advanced research. However, thermal images captured by smartphone thermal cameras are not accurate for monitoring human body temperature due to the small body that is vulnerable to temperature change. In this paper, we propose ThermalWrist, a dynamic offset correction method for thermal images captured by smartphone thermal cameras. We fully utilize the characteristic that is specific to thermal cameras: the relative temperatures in a single thermal image are highly reliable, although the absolute temperatures fluctuate frequently. To correct the offset error, ThermalWrist combines thermal images with a reliable absolute temperature obtained by a wristband sensor based on the above characteristic. The evaluation results in an indoor air-conditioned environment shows that the mean absolute error and the standard deviation of face temperature measurement error decrease by 49.4% and 64.9%, respectively. In addition, Pearson’s correlation coefficient increases by 112%, highlighting the effectiveness of ThermalWrist. We also investigate the limitation with respect to the ambient temperature where ThermalWrist works effectively. The result shows ThermalWrist works well in the normal office environment, which is 22.91 °C and above.

Background. Phenological correction of pre- and post-fire imagery is used to improve remotely sensed burn severity evaluations. Unburned offset values standardize greenness between image pairs, however, efficacy across diverse scenarios remains underexplored. Aims. We evaluated the impact of phenological offset correction methods to support analyst decision-making across fire-prone environments. Methods. We generated burn severity spectral index values for a dataset of Composite Burn Index (CBI) field plots across the conterminous U.S. The effectiveness of offset corrections was tested across image selection techniques, spectral indices, offset generation methods, and burn perimeter sources. We assessed the influence of offset corrections on the modeled relationship with CBI, agreement between burn severity thresholds, and potential bias. Key Results. Applying offset corrections consistently improved the modeled relationship with CBI by addressing extreme outlier severity values. However, automated offset corrections had the potential to introduce bias, systematically lowering severity values and reducing correspondence to observed burn severity categories. Conclusions. Offset corrections offer benefits but also pose tradeoffs to accurately representing remotely sensed burn severity. Implications. The utility of offset corrections depends on the environment, methods, and scale of analysis. We propose a decision-tree framework for analysts to consider when employing offset corrections given their study scope.

Global economic growth is now increasingly conflicting with the sustainable development strategy. In the context of ecological environmental preservation and air pollution prevention and control, this paper probes into the regional differentiation in the influencing factors of clean renewable energy consumption. First and foremost, a brief analysis of the status quo of clean renewable consumption in China was outputted, grounded on data on the input and output of 30 provinces and cities nationwide from 2010 to 2020. Then, national and regional models are built respectively in virtue of differential GMM, systematic GMM, and bias-corrected LSDV methods. Furthermore, efforts were invested in dissecting the working mechanism of the influencing factors and verifying the previous prediction resulting in applying the Tobit regression method. For every 1% increase in the green finance index, the clean renewable energy consumption rises by 0.882 accordingly, said the regression analysis results. Last but not least, it was concluded that the development level of green finance, internet advance, and technological progress significantly positively affected clean renewable energy consumption. While the industrial structure, the degree of openness, and the level of urbanization represented by the proportion of the secondary industry play hardly-seen impact.

The use of ion-selective field-effect transistors (ISFETs) facilitates real-time nutrient analysis in agricultural applications, including soil analysis and hydroponics. The rapid digital availability of analysis results allows for the implementation of ion-specific fertilisation control. The success, accuracy, and robustness of measurements using ISFET technology strongly depend on the handling of the process. This article presents a detailed overview of the sub-process steps required for the implementation of a stable automated application-specific ISFET-based measurement. This article provides experience-based recommendations for handling the conditioning, full calibration, and single-point calibration of the ISFET sensors. The hypotheses were empirically tested under authentic conditions and subsequently integrated into an overall process optimisation strategy. A comprehensive investigation has been conducted with the objective of gaining a deeper understanding of the ISFET baseline drift and implementing corrective measures. The results show that the baseline drift can be quantified and taken into account in the evaluation of the ISFET measurements. The efficacy of these measures was validated using standard laboratory analyses.

Thermal images are widely used for various healthcare applications and advanced research. However, thermal images captured by smartphone thermal cameras are not accurate for monitoring human body temperature due to the small body that is vulnerable to temperature change. In this paper, we propose ThermalWrist, a dynamic offset correction method for thermal images captured by smartphone thermal cameras. We fully utilize the characteristic that is specific to thermal cameras: the relative temperatures in a single thermal image are highly reliable, although the absolute temperatures fluctuate frequently. To correct the offset error, ThermalWrist combines thermal images with a reliable absolute temperature obtained by a wristband sensor based on the above characteristic. The evaluation results in an indoor air-conditioned environment shows that the mean absolute error and the standard deviation of face temperature measurement error decrease by 49.4% and 64.9%, respectively. In addition, Pearson's correlation coefficient increases by 112%, highlighting the effectiveness of ThermalWrist. We also investigate the limitation with respect to the ambient temperature where ThermalWrist works effectively. The result shows ThermalWrist works well in the normal office environment, which is 22.91 °C and above.