Search Results (17)

Industrial energy consumption in shift-based manufacturing exhibits pronounced temporal asymmetry—here defined as direction-dependent conditional dynamics in which the transition from production to shutdown states follows a systematically different temporal trajectory than the reverse transition. At the facility studied, this asymmetry also manifests in the marginal distribution of hourly consumption values: pooling all 4724 observations yields a bimodal, right-skewed histogram (skewness ≈ −0.4) comprising two sub-populations corresponding to production hours (14–19 kWh/h) and shutdown hours (0–2 kWh/h). Although individual hourly observations are serially dependent and therefore not i.i.d., the marginal distributional shape is consequential because ARIMA-class models assume approximately Gaussian innovations, and residuals from models fit to this bimodal series inherit its non-Gaussianity. More fundamentally, the conditional distribution P(E_t|E_{t − 1}, …) is direction-dependent: the production-to-shutdown transition is abrupt (1–2 h, 18:00–20:00), while the shutdown-to-production ramp is slower and more variable (2–4 h, 05:00–07:00). Symmetric ARMA models, applying identical autoregressive coefficients regardless of transition direction, cannot represent this directional asymmetry, rendering their assumptions and associated error metrics structurally unreliable for this class of data. This paper addresses this asymmetry directly by presenting and evaluating two hybrid forecasting architectures—Prophet+LSTM and SARIMA+LSTM—for 24 h-ahead energy prediction at an industrial bread factory in Kazakhstan, instrumented with 15 IoT energy meters. The two-stage design exploits the complementary asymmetry-handling properties of each component: the statistical model (Prophet or SARIMA) captures deterministic seasonal structure, while the LSTM corrects asymmetric residuals that the statistical model systematically misrepresents. In a rigorous 14-day holdout evaluation, Prophet+LSTM achieves an MAE of 3.39 kWh—outperforming the Seasonal Naïve baseline by 12.3% and reducing Prophet-alone error by 32.7%—with statistical significance at the 10% level confirmed via Diebold–Mariano testing (DM = +1.747, p = 0.081). The LSTM residual correction reduces Prophet’s systematic negative bias by 69% (from −3.60 to −1.13 kWh), as confirmed by ablation testing. In eight weeks of production operation with incremental retraining, MAE improved 35% (7.02 → 4.58 kWh). These results demonstrate that explicitly modelling temporal asymmetry through hybrid statistical-neural architectures substantially improves industrial energy forecasting accuracy. Full article

Background/Objectives: Red blood cell (RBC) transfusion is frequently used to treat anemia of prematurity, yet bedside metrics that capture its short-term impact on oxygenation stability are limited. We assessed whether pulse oximetry histogram-derived oxygen saturation (SpO₂) exposure changes after transfusion and whether responses differ across clinical subgroups. Methods: This prospective observational cohort included preterm infants born <32 weeks’ gestation who received a standardized RBC transfusion (15 mL/kg). Continuous SpO₂ histograms quantified the percentage of monitored time spent in hypoxemia (<85%), normoxemia (86–95%), and hyperoxemia (≥96%) during four intervals: 24 h pre-transfusion and 24, 48, and 72 h post-transfusion. Repeated-measures and subgroup analyses (BPD, sex, birth weight < 1000 g) were performed. Results: Thirty-three infants were analyzed (gestational age 29.4 ± 2.1 weeks; birth weight 1220.6 ± 316.9 g). Hemoglobin increased from 8.6 ± 1.1 to 11.7 ± 1.0 g/dL (p < 0.001). Cohort-level histogram shifts were modest: normoxemia increased from 68.4 ± 12.1% to 72.6 ± 11.4% at 24 h (p = 0.18), hypoxemia decreased from 10.3 ± 6.5% to 6.6 ± 4.8% (p = 0.09), and hyperoxemia remained stable (21.3 ± 9.2% to 20.8 ± 8.5%; p = 0.44). Infants with BPD and those <1000 g showed persistently higher hypoxemia and/or hyperoxemia at 72 h compared with counterparts. Exploratory ROC analyses showed modest discrimination of 24 h hypoxemia for ROP (AUC 0.71) and 72 h hyperoxemia for BPD (AUC 0.74). Conclusions: RBC transfusion corrected anemia but did not produce a consistent cohort-level improvement in SpO₂ histogram stability. Histogram metrics may help characterize heterogeneous oxygenation responses and support hypothesis generation for individualized monitoring strategies. Full article

In early 2025, the Jason-3 satellite’s orbit shifted from an “interleaved” to a tandem configuration with Sentinel-6A, and its Geophysical Data Records (GDR) were upgraded from Version F to G. This study evaluated GDR-G via eight processing approaches, using Jason-3’s last six GDR-F cycles (#394–#399) and first six GDR-G cycles (#501–#506), integrating histogram/geographical distribution analyses of Sea Surface Height Anomaly (SSHA), Significant Wave Height (SWH), Wind Speed (WS), and multi-method validation (e.g., self-cross-calibration). Key findings include the following: GDR-G had significantly lower SSHA noise than GDR-F, with up to ~4 cm SSHA bias from different retrackers/corrections; Adaptive retracker + 3D Sea State Bias (SSB) correction achieved optimal accuracy. Adaptive retracker’s SWH/WS anomalies linked to invalid MLE4 results and non-Brownian waveforms (coastal/sea ice). A detrending method was proposed, and the 41-point Lanczos window was optimal for smoothing. The results from the “detrending method” were consistent with the results based on the SSHA spectrum and classic self-cross-calibration methods. A ~5 mm drop was observed in Jason-3 GDR-G MLE4 baseline SSHA, probably caused by GDR upgrade or geographic sampling mismatch, while Sentinel-6A’s GDR-G upgrade might induce ~1 cm jump. The jumps along with GDR version upgrade highlighted the value of timely in situ absolute calibration. Full article

Single-photon avalanche detectors (SPADs) have significant applications in fields such as autonomous driving. However, processing massive amounts of background data requires substantial storage and computational resources. This paper designs a linear SPAD sensor capable of three detection modes: 2D intensity detection, 3D synchronous detection, and 3D asynchronous detection. A configurable coincidence circuit is used to effectively suppress background light. To overcome the significant resource demands for storage and computation, this paper designs a histogram circuit that simultaneously possesses data storage and shifting capabilities. This circuit can not only perform statistical counting on time data but also shift data to quickly complete computational analysis. The chip is fabricated using a 0.13 μm mixed-signal CMOS process, with a pixel scale of 64 elements, a time resolution of 132 ps, and a power consumption of 12.9 mW. Test results indicate that the chip has good detection capabilities and good background light suppression. When the background light intensity is 6000 lux, the maximum background data are suppressed by 95.4%, and the average suppression rate increases to 86% as the coincidence threshold is raised from 0 to 1. Full article

JPEG Reversible Data Hiding (RDH) is a method designed to extract hidden data from a marked image and perfectly restore the image to its original JPEG form. However, while existing RDH methods adaptively manage the visual distortion caused by embedded data, they often neglect the concurrent increase in file size. In rectifying this oversight, we have designed a new JPEG RDH scheme that addresses all influential metrics during the embedding phase and a dynamic frequency selection strategy with recoverable frequency order after data embedding. The process initiates with a pre-processing phase of blocks and the subsequent selection of frequencies. Utilizing a two-dimensional (2D) mapping strategy, we then compute the visual distortion and file size increment (FSI) for each image block by examining non-zero alternating current (AC) coefficient pairs (NZACPs) and their corresponding run lengths. Finally, we select appropriate block groups based on the influential metrics of each block group and proceed with data embedding by 2D histogram shifting (HS). Extensive experimentation demonstrates how our method’s efficiently and consistently outperformed existing techniques with a superior peak signal-to-noise Ratio (PSNR) and optimized FSI. Full article

The range accuracy of single-photon-array three-dimensional (3D) imaging systems is limited by the time resolution of the array detectors. We introduce a method for achieving super-resolution in 3D imaging through sub-bin delayed scanning acquisition and fusion. Its central concept involves the generation of multiple sub-bin difference histograms through sub-bin shifting. Then, these coarse time-resolution histograms are fused with multiplied averages to produce finely time-resolved detailed histograms. Finally, the arrival times of the reflected photons with sub-bin resolution are extracted from the resulting fused high-time-resolution count distribution. Compared with the sub-delayed with the fusion method added, the proposed method performs better in reducing the broadening error caused by coarsened discrete sampling and background noise error. The effectiveness of the proposed method is examined at different target distances, pulse widths, and sub-bin scales. The simulation analytical results indicate that small-scale sub-bin delays contribute to superior reconstruction outcomes for the proposed method. Specifically, implementing a sub-bin temporal resolution delay of a factor of 0.1 for a 100 ps echo pulse width substantially reduces the system ranging error by three orders of magnitude. Furthermore, Monte Carlo simulations allow to describe a low signal-to-background noise ratio (0.05) characterised by sparsely reflected photons. The proposed method demonstrates a commendable capability to simultaneously achieve wide-ranging super-resolution and denoising. This is evidenced by the detailed depth distribution information and substantial reduction of 95.60% in the mean absolute error of the reconstruction results, confirming the effectiveness of the proposed method in noisy scenarios. Full article

Reversible data hiding (RDH) techniques recover the original cover image after data extraction. Thus, they have gained popularity in e-healthcare, law forensics, and military applications. However, histogram shifting using a reversible data embedding technique suffers from low embedding capacity and high variability. This work proposes a technique in which the distribution obtained from the cover image determines the pixels that attain a peak or zero distribution. Afterward, adjacent histogram bins of the peak point are shifted, and data embedding is performed using the least significant bit (LSB) technique in the peak pixels. Furthermore, the robustness and embedding capacity are improved using the proposed dynamic block-wise reversible embedding strategy. Besides, the secret data are encrypted before embedding to further strengthen security. The experimental evaluation suggests that the proposed work attains superior stego images with a peak signal-to-noise ratio (PSNR) of more than 58 dB for 0.9 bits per pixel (BPP). Additionally, the results of the two-sample t-test and the Kolmogorov–Smirnov test reveal that the proposed work is resistant to attacks. Full article

To better cope with the significant nonlinear radiation distortions (NRD) and severe rotational distortions in multi-modal remote sensing image matching, this paper introduces a rotationally robust feature-matching method based on the maximum index map (MIM) and 2D matrix, which is called the rotation-invariant local phase orientation histogram (RI-LPOH). First, feature detection is performed based on the weighted moment equation. Then, a 2D feature matrix based on MIM and a modified gradient location orientation histogram (GLOH) is constructed and rotational invariance is achieved by cyclic shifting in both the column and row directions without estimating the principal orientation separately. Each part of the sensed image’s 2D feature matrix is additionally flipped up and down to obtain another 2D matrix to avoid intensity inversion, and all the 2D matrices are concatenated by rows to form the final 1D feature vector. Finally, the RFM-LC algorithm is introduced to screen the obtained initial matches to reduce the negative effect caused by the high proportion of outliers. On this basis, the remaining outliers are removed by the fast sample consensus (FSC) method to obtain optimal transformation parameters. We validate the RI-LPOH method on six different types of multi-modal image datasets and compare it with four state-of-the-art methods: PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH. The experimental results show that our proposed method has obvious advantages in the success rate (SR) and the number of correct matches (NCM). Compared with PSO-SIFT, MS-HLMO, CoFSM, and RI-ALGH, the mean SR of RI-LPOH is 170.3%, 279.8%, 81.6%, and 25.4% higher, respectively, and the mean NCM is 13.27, 20.14, 1.39, and 2.42 times that of the aforementioned four methods. Full article

Reversible data hiding is a technique for embedding secret data into a cover media. Such technique has the ability to recover marked cover media to its original version after extracting the secret data. In this paper, a new reversible data hiding algorithm for videos H.264/AVC is proposed to improve the embedding capacity while the distortion drift is prevented. To embed the secret data into videos H.264/AVC without any intra-frame distortion drift, in our proposed scheme, the relationship of QDCT coefficients is explored. Then, a coefficient pair mapping mechanism in 2D histogram is introduced for embedding data. The experimental results demonstrated that the proposed scheme obtains reversibility. The proposed scheme prevents intra-frame distortion drift on the marked videos. In addition, the embedding capacity of the proposed scheme is superior to that of existing schemes while guaranteeing the high visual quality of marked videos. Full article

Because color patterns with multiple channels can carry more information than gray patterns with only one channel, color phase-shifting profilometry (CPSP) has been widely used for high-speed, three-dimensional (3D) shape measurement. However, the accuracy of CPSP suffers from nonlinear errors caused by color crosstalk. This paper presents an effective nonlinear error correction method for CPSP based on histogram equalization. The two main steps of the proposed method are eliminating nonlinear errors with histogram equalization and optimizing the results using a spline fitting algorithm. Compared with other compensation methods, the proposed approach does not require any precalibration information or additional patterns, which are very time-consuming. The simulations and experiments indicate that the proposed method has a promising performance for nonlinear error elimination. Full article

In this paper, and for the first time in literature, optical performance monitoring (OPM) of super-channel optical networks is considered. In particular, we propose a novel machine learning OPM technique based on the use of transformed in-phase quadrature histogram (IQH) features and support vector regressor (SVR) to estimate different optical parameters such as optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). Two transformation methods, the two-dimensional (2D) discrete Fourier transform (DFT) and 2D discrete cosine transform (DCT), are applied to the IQH to extract features with a considerably reduced dimensionality. For the purpose of simulation, the OPM of a 7 × 20 Gbaud dual-polarization–quadrature phase shift keying (DP-QPSK) is considered. Simulations reveal that it can accurately estimate the various optical parameters (i.e., OSNR and CD) with a coefficient of determination value greater than 0.98. In addition, the effectiveness of proposed OPM scheme is examined under different values of polarization mode dispersion and frequency offset, as well as the utilization of different higher order modulation formats. Moreover, proof-of-concept experiments are performed for validation. The results show an excellent matching between the simulation and experimental findings. Full article

Light detection and ranging (LiDAR) data systems mounted on a moving or stationary platform provide 3D point cloud data for various purposes. In applications where the interested area or object needs to be measured twice or more with a shift, precise registration of the obtained point clouds is crucial for generating a healthy model with the combination of the overlapped point clouds. Automatic registration of the point clouds in the common coordinate system using the iterative closest point (ICP) algorithm or its variants is one of the frequently applied methods in the literature, and a number of studies focus on improving the registration process algorithms for achieving better results. This study proposed and tested a different approach for automatic keypoint detecting and matching in coarse registration of the point clouds before fine registration using the ICP algorithm. In the suggested algorithm, the keypoints were matched considering their geometrical relations expressed by means of the angles and distances among them. Hence, contributing the quality improvement of the 3D model obtained through the fine registration process, which is carried out using the ICP method, was our aim. The performance of the new algorithm was assessed using the root mean square error (RMSE) of the 3D transformation in the rough alignment stage as well as a-prior and a-posterior RMSE values of the ICP algorithm. The new algorithm was also compared with the point feature histogram (PFH) descriptor and matching algorithm, accompanying two commonly used detectors. In result of the comparisons, the superiorities and disadvantages of the suggested algorithm were discussed. The measurements for the datasets employed in the experiments were carried out using scanned data of a 6 cm × 6 cm × 10 cm Aristotle sculpture in the laboratory environment, and a building facade in the outdoor as well as using the publically available Stanford bunny sculpture data. In each case study, the proposed algorithm provided satisfying performance with superior accuracy and less iteration number in the ICP process compared to the other coarse registration methods. From the point clouds where coarse registration has been made with the proposed method, the fine registration accuracies in terms of RMSE values with ICP iterations are calculated as ~0.29 cm for Aristotle and Stanford bunny sculptures, ~2.0 cm for the building facade, respectively. Full article

Histogram shifting (HS) has been proved to be a great success in reversible data hiding (RDH). To reduce the quality loss of marked media and the increase in file size, several two-dimensional (2D) HS schemes based on the characteristics of cover media have been proposed recently. However, our analysis shows that the embedding strategies used in these methods can be further optimized. In this paper, two new 2D HS schemes for RDH in H.264/AVC video are developed, one of which uses the DCT coefficient pairs with both values 0 and the other does not. The embedding efficiency of a DCT coefficient pair in different embedding modes is firstly calculated. Then, based on the obtained embedding efficiency along with the statistical distribution of DCT coefficient pairs, two better embedding strategies are proposed. The secret data is finally embedded into the pairs of DCT coefficients of the middle and high frequencies using our proposed strategies. The comparison experiment results demonstrate that our schemes can achieve enhanced visual quality in terms of PSNR, SSIM, and entropy in most cases, and the increase in file size is smaller. Full article

Robust reversible watermarking in an encrypted domain is a technique that preserves privacy and protects copyright for multimedia transmission in the cloud. In general, most models of buildings and medical organs are constructed by three-dimensional (3D) models. A 3D model shared through the internet can be easily modified by an unauthorized user, and in order to protect the security of 3D models, a robust reversible 3D models watermarking method based on homomorphic encryption is necessary. In the proposed method, a 3D model is divided into non-overlapping patches, and the vertex in each patch is encrypted by using the Paillier cryptosystem. On the cloud side, in order to utilize addition and multiplication homomorphism of the Paillier cryptosystem, three direction values of each patch are computed for constructing the corresponding histogram, which is shifted to embed watermark. For obtaining watermarking robustness, the robust interval is designed in the process of histogram shifting. The watermark can be extracted from the symmetrical direction histogram, and the original encrypted model can be restored by histogram shifting. Moreover, the process of watermark embedding and extraction are symmetric. Experimental results show that compared with the existing watermarking methods in encrypted 3D models, the quality of the decrypted model is improved. Moreover, the proposed method is robust to common attacks, such as translation, scaling, and Gaussian noise. Full article

The emergence of super-resolution imaging makes it possible to display the microvasculatures clearly using ultrasound imaging, which is of great importance in the early diagnosis of cancer. At present, the super-resolution performance can only be achieved when the sampling signal is long enough (usually more than 10,000 frames). Thus, the imaging time resolution is not suitable for clinical use. In this paper, we proposed a novel super-resolution reconstruction method, which is proved to have a satisfactory resolution using shorter sampling signal sequences. In the microbubble localization step, the integrated form of the 2D Gaussian function is innovatively adopted for image deconvolution in our method, which enhances the accuracy of microbubble positioning. In the trajectory tracking step, for the first time the averaged shifted histogram technique is presented for the visualization, which greatly improves the precision of reconstruction. In vivo experiments on rabbits were conducted to verify the effectiveness of the proposed method. Compared to the conventional reconstruction method, our method significantly reduces the Full-Width-at-Half-Maximum (FWHM) by 50% using only 400-frame signals. Besides, there is no significant increase in the running time using the proposed method. Considering its imaging performance and used frame number, the conclusion can be drawn that the proposed method advances the application of super-resolution imaging to the clinical use with a much higher time resolution. Full article