Low-light (LOL) conditions constantly restrict the performance of Internet of Things (IoT) image sensors, thereby impacting image quality and the precision of visual data analysis. The emerging edge intelligence is crucial for LOL image enhancement in improving image quality and data support reliability for IoT systems, which in turn fosters the intelligence and automation progress of the IoT. The enhancement of LOL images necessitates the restoration of both contextual information and spatial details, maintaining the semantic content of the original image and the point-to-point correspondence between inputs and outputs. However, existing methods predominantly concentrate on one aspect, either contextual information or spatial details, making it difficult to simultaneously balance both. To overcome this challenge, we introduce a novel two-branch network, the context-space balance network (CSBNet), and tailored for LOL image enhancement. It comprises a contextual information recovery network (CIRNet), which adeptly extracts contextual information from multiscale LOL images, and a spatial information recovery network (SIRNet), which is designed to preserve spatial details at the original resolution. We also implement a context-space feature fusion (CSFF) module to seamlessly integrate contextual information with spatial details. Qualitative and quantitative experimental results demonstrate that our CSBNet can better handle various kinds of degradations in lowlight images compared with state-of-the-art solutions on the benchmark LOL dataset. The source code of CSBNet is available athttps://github.com/Loong161/CSBNet.

This paper presents a hybrid data-driven method, termed moving average-Hankel-dynamic mode decomposition (MAHankDMD), for joint direction of arrival (DOA) and frequency estimation in environments affected by both radio frequency interference (RFI) and Gaussian white noise. The proposed approach integrates two key components: (1) a moving average-DMD filter that effectively mitigates Gaussian white noise and separates RFI from the source signal, and (2) a Hankel-DMD method that accurately estimates the DOA of the filtered signal and associates it with the corresponding frequency. The moving average-DMD stage first enhances the signal-to-noise ratio and improves the robustness of the estimation process through noise and inference mitigation, while the subsequent Hankel-DMD stage enables reliable parameter extraction even for overlapping sginals or strong interference conditions. Numerical simulations demonstrate the robustness of MAHankDMD, showing its ability to precisely estimate both DOA and frequency under challenging conditions involving RFI and Gaussian white noise interference. The proposed algorithm thus provides an effective solution for channel parameter estimation in complex noisy environments.

In wireless channel sounding, accurately estimating multiple parameters within a multipath signal, such as azimuth, elevation, Doppler shift, and delay, necessitates addressing the challenges posed by the multidimensional harmonic retrieval (MHR) problem. To overcome these complexities, we propose a framework based on high-order dynamic mode decomposition (HODMD) that designed for robustly estimating frequencies of interest from high-dimensional sinusoidal signals, particularly in additive white Gaussian noise conditions. The HODMD approach, a hybrid algorithm amalgamating high-order singular value decomposition (HOSVD) and dynamic mode decomposition (DMD), operates by initially decomposing observed tensorial data into a core tensor and R mode matrices through HOSVD. Subsequently, DMD is applied to analyze each mode matrix individually, decomposing it into dynamic modes and DMD eigenvalues. The imaginary component of the DMD eigenvalues yields frequencies along the rth dimension. By uniformly applying this analysis to all mode matrices, multiple frequencies of interest are efficiently obtained. Furthermore, the integration of HOSVD, DMD, and moving average techniques in the proposed method is designed to mitigate noise interference during the MHR process. We conduct several numerical experiments and present a real-life example, i.e., the double-direction multiple-input and multiple-output (MIMO) channel sounding, to validate the effectiveness of the proposed HODMD approach. Results demonstrate that HODMD outperforms comparable approaches, particularly in scenarios characterized by high-signal-to-noise ratios. Notably, the proposed method exhibits the capability to estimate the number of tones in undamped cases during the decomposition process. Hence, our work contributes a practical and effective tensor-based solution to the MHR problem, particularly in the context of channel parameter estimation for MIMO systems.

This article introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a 2-D matrix format along the rth dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the rth dimension, respectively. Furthermore, recursive DMD is employed to scrutinize each mode independently for parameter retrieval across the remaining dimensions, enabling parallel analysis. Ultimately, this high-dimensional correlated decomposition scheme delivers paired damping factors and frequencies for all tones. Notably, the proposed approach can ascertain the number of tones in undamped sinusoidal signals, making it particularly suitable for MHR even without prior knowledge of the source count. Numerical experiments demonstrate the accuracy and robustness of the RPDMD scheme, with comparative analysis indicating that RPDMD outperforms similar methods, achieving optimal results with minimal mean square error in high signal-to-noise ratio scenarios. This work presents an effective data-driven solution for the MHR problem in MIMO wireless channel sounding.

Nucleosomes represent hubs in chromatin organization and gene regulation and interact with a plethora of chromatin factors through different modes. In addition, alterations in histone proteins such as cancer mutations and post-translational modifications have profound effects on histone/nucleosome interactions. To elucidate the principles of histone interactions and the effects of those alterations, we developed histone interactomes for comprehensive mapping of histone-histone interactions (HHIs), histone-DNA interactions (HDIs), histone-partner interactions (HPIs) and DNA-partner interactions (DPIs) of 37 organisms, which contains a total of 3808 HPIs from 2544 binding proteins and 339 HHIs, 100 HDIs and 142 DPIs across 110 histone variants. With the developed networks, we explored histone interactions at different levels of granularities (protein-, domain- and residue-level) and performed systematic analysis on histone interactions at a large scale. Our analyses have characterized the preferred binding hotspots on both nucleosomal/linker DNA and histone octamer and unraveled diverse binding modes between nucleosome and different classes of binding partners. Last, to understand the impact of histone cancer-associated mutations on histone/nucleosome interactions, we complied one comprehensive cancer mutation dataset including 7940 cancer-associated histone mutations and further mapped those mutations onto 419,125 histone interactions at the residue level. Our quantitative analyses point to histone cancer-associated mutations' strongly disruptive effects on HHIs, HDIs and HPIs. We have further predicted 57 recurrent histone cancer mutations that have large effects on histone/nucleosome interactions and may have driver status in oncogenesis.

The sea clutter, referred to as the time-varying radar backscatter from the ocean surface, plays a significant role in marine radar development. The ocean’s complex hydrodynamics cause it to exhibit non-Gaussian and nonstationary characteristics, which brings challenges in the sea clutter modeling, especially for establishing its spatial–temporal correlated and coherent model. In this article, a data-driven method based on the Koopman mode decomposition (KMD) is proposed for modeling spatial–temporal correlated complex sea clutter. The method decomposes the coherent sea clutter dynamic behavior in terms of Koopman modes and corresponding temporal patterns. Then, these spatiotemporal patterns are used to construct the sea clutter state over time according to the approximate solution. Furthermore, this proposed state-of-the-art data-driven approach is benchmarked by the measured sea clutter data from intelligent PIXel processing radar (IPIX). It is demonstrated that the proposed approach accurately models the complex sea clutter with actual statistic characteristics, phase information, and spatial–temporal correlations. The mean absolute error (MAE) and root mean square error (RMSE) between the obtained and actual sea clutter are only 0.1817 and 0.2349, respectively. This work offers a practical approach for modeling sea clutter, especially when the spatial–temporal correlation and coherence information is needed.

Deep learning is applied to assist the joint inversion for audio-magnetotelluric and seismic travel time data. More specifically, deep residual convolutional neural networks (DRCNNs) are designed to learn both structural similarity and resistivity-velocity relationships according to prior knowledge. During the inversion, the unknown resistivity and velocity are updated alternatingly with the Gauss-Newton method, based on the reference model generated by the trained DRCNNs. The workflow of this joint inversion scheme and the design of the DRCNNs are explained in detail. Compared with describing the resistivity-velocity relationship using empirical equations, this method can avoid the necessity in modeling the correlations in rigorous mathematical forms and extract more hidden prior information embedded in the training set, meanwhile preserving the structural similarity between different inverted models. Numerical tests show that the inverted resistivity and velocity have similar profiles, and their relationship can be kept consistent with the prior joint distribution. Furthermore, the convergence is faster, and final data misfits can be lower than separate inversion.

Facial expression recognition has been widely used to solve the problems such as lie detection and human-machine interaction. However, due to the difficulties to control the application environments, current methods have the lower recognition accuracy in practice. This paper proposes a new method for facial expression recognition by considering several aspects. First, human beings are easy to recognize some expressions, while difficult to recognize others. Inspired by this intuition, a new loss function is proposed to enlarge the distances between samples from easily confused categories. Second, human learning is divided into many stages, and the learning objective of each stage is different. Thus, dynamic objectives learning is proposed, where each objective at different stage is defined by the corresponding loss function. In order to better realize the above ideas, a new deep neural network for facial expression recognition is proposed, which integrates the covariance pooling layer and residual network units into the deep convolution neural network so as to better perform dynamic objectives learning. The experimental results on the standard databases verify the effectiveness and the superior performance of our methods.

The memorability of multiple passwords is an important topic for user authentication systems. With the advent of Android unlock pattern mechanism, research studies started investigating its usability and security features. This paper presents a study of recalling multiple passwords between text passwords and touch screen unlock patterns, as well as exploring whether users have difficulty in remembering those patterns after a period of time. In our study, participants create unlock patterns for various account scenarios. Our results reveal that participants in the unlock pattern condition with three accounts can outperform those in the text password condition (i.e., achieve higher success rates), not only in a one-hour session (short-term), but also after two weeks (long-term). However, there was no statistically significant difference between participants in the text password and unlock pattern condition in the long-term, when dealing with six accounts.

Volterra series is a powerful tool for black-box macro-modeling of nonlinear devices. However, the exponential complexity growth in storing and evaluating higher order Volterra kernels has limited so far its employment on complex practical applications. On the other hand, tensors are a higher order generalization of matrices that can naturally and efficiently capture multi-dimensional data. Significant computational savings can often be achieved when the appropriate low-rank tensor decomposition is available. In this paper we exploit a strong link between tensors and frequency-domain Volterra kernels in modeling nonlinear systems. Based on such link we have developed a technique called speedy tensor-aided Volterra-based electronic simulator (STAVES) utilizing high-order Volterra transfer functions for highly accurate time-domain simulation of nonlinear systems. The main computational tools in our approach are the canonical tensor decomposition and the inverse discrete Fourier transform. Examples demonstrate the efficiency of the proposed method in simulating some practical nonlinear circuit structures.

This is a brief review of the development of computational electromagnetics (CEM) to partially summarize its achievements, issues, and possibilities.

The papers in this special issue are devoted to the topic of large-scale electromagnetic computation methods for modeling and applicatoins.

A challenging and interesting issue for the solution of large electromagnetic problems is the efficient, sufficiently accurate modeling of the broadband skin-effect loss for conducting planes and 3-D shapes. The inclusion of such models in an electromagnetic (EM) solver can be very costly in compute time and memory requirements. These issues are particularly important for the class of signal, power, and noise integrity (NI) problems. In this paper, we concentrate on partial element equivalent circuit (PEEC)-type methods which are suitable for the solution of this class of problems. Progress has been made recently in the design of skin-effect models. The difficult issues are broadband frequency-domain or time-domain problems. These models are considered in this paper. We present several solution methods, and we compare results obtained with these approaches.

We report recent progress in developing a numerically efficient formulation for electromagnetic-technology computer-aided design cosimulation for fast-transient computations. The difficulties underlying the currently existing transient formulation stemming from the vector potential-scalar potential (A-V) framework are analyzed. A time-domain electric field-scalar potential (E-V) framework is then developed via equation and variable transformations. This results in better-conditioned systems that are friendly to iterative solutions at fast switching times. Numerical examples show that the proposed E-V solver renders a useful tool for addressing multidomain simulation.

In this paper, we present a variational electromagnetic-semiconductor coupled solver to assess the impacts of process variations on the 3D integrated circuit (3D IC) on-chip structures. The solver employs the finite volume method (FVM) to handle a system of equation considering both the full-wave electromagnetic effects and semiconductor effects. With a smart geometrical variation model for the FVM discretization, the solver is able to handle both small-size or large-size variations. Moreover, a weighted principle factor analysis (wPFA) technique is presented to reduce the random variables in both electromagnetic and semiconductor regions, and the spectral stochastic collocation method (SSCM) is used to generate the quadratic statistical model. Numerical results validate the accuracy and efficiency of this solver in dealing with process variations in hybrid material through-silicon via (TSV) structures.

We present a fast time-domain multiphysics simulation framework that combines full-wave electromagnetism (EM) and carrier transport in semiconductor devices (TCAD). The proposed framework features a division of linear and nonlinear components in the EM-TCAD coupled system. The former is extracted and handled independently with high efficiency by a matrix exponential approach assisted with Krylov subspace method. The latter is treated by ordinary Newton's method yet with a much sparser Jacobian matrix that leads to substantial speedup in solving the linear system of equations. More convenient error management and adaptive control are also available through the linear and nonlinear decoupling.

We develop a new method based on the high-frequency electromagnetic (EM)-semiconductor coupled simulation to analyze the impact of multi-type process variations happen around semiconductor-metal structure. It is competent to simultaneously handle geometrical variations like surface roughness and material variations like semi-conductor doping profile, which are difficult for traditional "stand alone" simulation methods. A sparse grid based stochastic spectral collocation method (SSCM) combined with principle factor analysis (PFA) is implemented to accelerate the stochastic simulation. Numerical results confirm the validity and significance of our variational coupled simulation framework.

Computer generation of cell images can provide annotated data to simulate various imaging conditions with controllable parameters. Synthesized images based on simple models cannot reflect the complicated parameter constraints in simulating real objects in terms of their deformation with appropriate probabilities. Learning-based techniques can provide insight to these properties and impose constraints on deformation selections. In this work, we discuss the simulation of gray level images of healthy red blood cell populations. Different from existing techniques, we learn the unbiased average shape and deformation models of the cells. Both models are used to guide the selection of possible deformations. We also learn cell color models to govern the texture generation of simulated cells. We apply this technique to simulate cell populations and validate the results using cell segmentation and counting algorithms. The proposed learning and simulation technique is generic and can be applied to other types of cells as well.

Accurate and fast estimation of VLSI interconnect thermal profiles has become critically important to estimate their impact on circuit/system performance and reliability, which is necessary for reducing product development time and achieving first-pass silicon success. Present commercial thermal analysis tools are incapable of simulating complex structures, particularly in the 3-D domain and are also difficult to integrate with existing design tools. Existing analytical thermal models are not perfect either: they are either not accurate enough or oversimplified. This paper uses a methodology, which exploits existing electrical resistance solvers for thermal simulation, to allow fast acquisition of thermal profiles of complex interconnect structures with good accuracy and reasonable computation cost. Moreover, for the first time, an accurate closed-form thermal model is developed. The model allows for an equivalent medium with effective thermal conductivity (isotropic or anisotropic) to replace the detailed material information in non-critical regions so that complex interconnect structures can be simulated. Using these techniques, this paper demonstrates the simulation of a very complex interconnect structure (~9000 objects or 15 million meshed unknowns after first order isotropic equivalent medium replacement), which is a first time achievement in the area of interconnect thermal analysis. On the other hand, it is shown that an anisotropic equivalent medium is a much better approximation of real interconnect structures from the point of view of accuracy and computation.

Locally linear embedding heavily depends on whether the neighborhood graph represents the underlying geometry structure of the data manifolds. Inspired from the cognitive law, the relative transformation(RT) and kernel relative transformation (KRT) are proposed. They can improve the distinction between data points and inhibit the impact of noise and sparsity of data, which can be then applied to construct the neighborhood graph so as to reduce the short circuit edges, while the embedding is still performed in the original space. Subsequently, another enhanced Hessian Locally Linear Embedding approach is developed with significantly increased performance. The conducted experiments on challenging benchmark data sets validate the proposed approaches.

Text categorization has become the key technology in organizing and processing the large amount of text information. It normally involves an extremely high dimensional space, which makes most existing approaches generate highly biased estimates so as to reduce the classification accuracy. These approaches do not consider that the text documents may be intrinsically located on the low-dimensional manifold. This paper presents an approach that performs text categorization on texts manifold with respect to the intrinsic global manifold structure, such as by geodesic distance to measure the distance between two texts. This approach has been applied to improve the KNN for text categorization. This is empirically validated by the conducted experiments.

Hessian locally linear embedding (HLLE) has good representational capacity and high computational efficiency, but it still fails to nicely deal with the sparsely sampled or noise contaminated datasets, where the local neighborhood structure is critically distorted. To solve this problem, this paper proposes a new approach that takes the general conceptual framework of HLLE so as to guarantee its correctness in the setting of local isometry, and then employs the geodesic distance instead of Euclidean distance to determine the local neighborhood so as to give the global representation to the local data. This approach can be regarded as the integration of both local approaches and global approaches, so that it have the better performance and stability. The conducted experiments on both synthetic and real datasets have validated the proposed approach.

Locally linear embedding approach (LLE) is one of most efficient nonlinear dimensionality reduction approaches with good representational capacity for a broader range of manifolds and high computational efficiency. However, LLE and its variants fail to nicely deal with sparsely sampled or noise contaminated datasets,where the local neighborhood structure is critically distorted. To solve this problem, this paper utilizes the clustering approaches to partition the input data into clusters and then rescale the distance between any points based on the clustering structure so as to make data points from different clusters separated more easily. This rescaled distance matrix is then provided to improve LLE so as to achieve the better performance. Unlike the supervised approaches, this approach does not take the labelled dataset as prerequisite, so that it is unsupervised. This makes it applicable to broader range of domains. The conducted experiments by classification on benchmark datasets have validated the proposed approach.

Color encoded phase-stepping light projection method is a new and promising technique for 3D shape modeling However, the 3D model acquired is often smeared by large error. The main cause of the error is color coupling amongst the three primary colors RGB. In this paper, we first analyzed the color-coupling problem. It is found that there is a strong coupling between G and R element. The coupling between R and G are proportional to the phase interval between them and the overall intensity of image. Second, we proposed an adaptive phase stepping method to alleviate the color coupling errors efficiently and improve accuracy effectively. An algorithm corresponding to a specific paradigm with R-G-B phase step set to 0-45-180 is given and is applied to measure different objects. Experimental results demonstrate the effectiveness of the method.

We introduce a PDA-based live video streaming system on GPRS network based on MPEG-4 video compression standard. Due to the limited computational resources of PDA, all the key modules of MPEG-4 codec are efficiently implemented and optimized such as multithreading, buffer design, wireless communication, encoder and decoder. Several novel techniques are developed in the coding, streaming as well as the post- processing stages of the system.

In this paper, a method for measuring the perceptual image quality of JPEG-2000 coded images has been proposed. The image quality is characterized by the average edge-spread in the image, or more specifically the average extent of the slope's spread of an edge in the opposing gradients' directions. The proposed method is, in effect, a way of measuring the amount of blurring in the image. The effectiveness of such method is validated using subjective tests and the experimental results show that the proposed method can provide results that correlate relatively well with human subjective ratings.