Conventional fast Fourier transform (FFT)-based frequency estimation in frequency-modulated continuous-wave (FMCW) radar altimetry suffers from limited resolution and noise sensitivity, which constrains ranging accuracy. To address this, this letter proposes a novel clustering-based frequency estimation algorithm leveraging multi-cycle triangular FMCW signals. The method increases signal redundancy without additional bandwidth by transmitting a multi-cycle triangular waveform. At the receiver, the beat signal undergoes envelope extraction, amplitude normalization, and a squaring operation to ensure phase coherence and enhance the fundamental component. A short-time Fourier transform (STFT) is then applied to obtain a time-frequency representation, followed by K-means clustering on the spectral maxima to extract the most stable and dominant frequency component. This process effectively suppresses spectral leakage, mitigates noise and outlier effects, and substantially improves estimation robustness. Simulation results demonstrate that the proposed algorithm achieves a relative altitude error below 1% at signal-to-noise ratios above 2 dB, significantly outperforming conventional FFT-based and STFT-averaging methods. Experimental validation using an FMCW radar confirms the superior accuracy and practical reliability of the approach, demonstrating its strong potential for high-precision radar altimetry in compact systems.

MOTIVATION: The drug-likeness has been widely used as a criterion to distinguish drug-like molecules from non-drugs. Developing reliable computational methods to predict the drug-likeness of compounds is crucial to triage unpromising molecules and accelerate the drug discovery process. RESULTS: In this study, a deep learning method was developed to predict the drug-likeness based on the graph convolutional attention network (D-GCAN) directly from molecular structures. Results showed that the D-GCAN model outperformed other state-of-the-art models for drug-likeness prediction. The combination of graph convolution and attention mechanism made an important contribution to the performance of the model. Specifically, the application of the attention mechanism improved accuracy by 4.0%. The utilization of graph convolution improved the accuracy by 6.1%. Results on the dataset beyond Lipinski's rule of five space and the non-US dataset showed that the model had good versatility. Then, the billion-scale GDB-13 database was used as a case study to screen SARS-CoV-2 3C-like protease inhibitors. Sixty-five drug candidates were screened out, most substructures of which are similar to these of existing oral drugs. Candidates screened from S-GDB13 have higher similarity to existing drugs and better molecular docking performance than those from the rest of GDB-13. The screening speed on S-GDB13 is significantly faster than screening directly on GDB-13. In general, D-GCAN is a promising tool to predict the drug-likeness for selecting potential candidates and accelerating drug discovery by excluding unpromising candidates and avoiding unnecessary biological and clinical testing. AVAILABILITY AND IMPLEMENTATION: The source code, model and tutorials are available at https://github.com/JinYSun/D-GCAN. The S-GDB13 database is available at https://doi.org/10.5281/zenodo.7054367. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Reinforcement learning method that learns while interacting with the environment, relies heavily on the concept of state as the input to the policy and value function. In the task, the view of agent contains a lot of information, and it is difficult for the agent to learn to ignore the irrelevant information and focus on the key information. Inspired by recent work in attention models for computer vision, we present a role‐based attention model for reinforcement learning. The proposed model uses convolutional neural networks to generate soft attention maps, adding crucial role information in the task, forcing the agent to focus on important features and distinguish task‐related information. To validate the performance in complex problems, the proposed approach is evaluated in a challenging scenario, Football Academy in Google Research Football Environment, a newly released reinforcement learning environment with physics‐based three‐dimensional simulator. The experimental results demonstrate that agents using role‐based attention mechanism can perform better in football games.

Abstract Cross‐modal retrieval aims to do flexible retrieval among different modals, whose main issue is how to measure the semantic similarities among multimodal data. Though many existing methods have been proposed to enable cross‐modal retrieval, they rarely consider the preservation of content information among multimodal data. In this paper, we present a three‐stage cross‐modal retrieval method, named MMCA‐CMR. To reduce the discrepancy among multimodal data, we first attempt to embed multimodal data into a common representation space. We then combine the feature vectors with the content information into the semantic‐aware feature vectors. We finally obtain the feature‐aware and content‐aware projections via multimodal semantic autoencoders. With semantic deep autoencoders, MMCA‐CMR promotes a more reliable cross‐modal retrieval by learning feature vectors from different modalities and content information simultaneously. Extensive experiments demonstrate that the proposed method is valid in cross‐modal retrieval, which significantly outperforms state‐of‐the‐art on four widely‐used benchmark datasets.

Abstract Previous works propose the distance‐based sampling for unlabeled datapoints to address the few‐shot person re‐identification task, however, many selected samples may be assigned with wrong labels due to poor feature quality in these works, which negatively affects the learning procedure. In this article, we propose a novel sampling strategy to improve the quality of assigned pseudo‐labels, thus promoting the final performance. To illustrate, we first propose the concept of variance confidence to measure the credibility of pseudo‐labels, then we apply a novel variance subsampling algorithm to improve the accuracy of the selected sample labels. Our method combines distance confidence and variance confidence as a two‐round sampling criterion. Meanwhile, a variation decay strategy is used in our sampling process in combination with the actual distribution of features. We evaluate our approach on two publicly available datasets, MARS and DukeMTMC‐VideoReID, and achieve state‐of‐the‐art one‐shot performance.

UNLABELLED: Family-based designs offer unique advantage for identifying rare risk variants in genetic association studies. There are existing tools for analyzing rare variants in families but lacking components to handle binary traits properly and survival traits. In this report, we introduce an R software package RVFam (Rare Variant association analysis with Family data) designed to analyze continuous, binary and survival traits against rare and common sequencing variants in genome-wide association studies (GWAS) involving family data. Single and multiple variant association tests were implemented while accounting for arbitrary family structures. Extensive simulation studies were performed to evaluate all the approaches implemented in RVFam. AVAILABILITY AND IMPLEMENTATION: http://cran.r-project.org/web/packages/RVFam/.

This study proposes an analytical model and an effective scheme for the periodic broadcast on the control channel in vehicular ad hoc networks (VANETs). An improved Markov model for analysing the performance of the periodic broadcast in VANETs is established. Compared with the traditional two‐dimensional Markov chain models, the improvement of our proposed model is achieved by the considerations of the unsaturated traffic conditions with the deterministic message generation at each node, modelled by a discrete‐time D/M/1 queue and the control mechanism of freezing the backoff‐time counter. In adapting to the change of the vehicle densities, the authors propose to use the dynamic contention window (DCW), instead of the fixed contention window (CW), for the broadcast in the IEEE 802.11p medium access control in VANETs. For a certain vehicle density, a best CW size is chosen to achieve a more effective broadcast. Simulation results show that the proposed DCW‐based broadcast performs better than the traditional fixed‐CW‐size broadcast in terms of the packet collision probability. The results also validate our proposed Markov model and its performance improvement than the scheme without the consideration of freezing the backoff‐time counter.

In some real-time networked control applications, information between sensors and controllers is exchanged over a shared wireless channel. One key issue is to manage multiple access to the shared medium to accomplish different control tasks. In the paper, a protocol sequence based media access control (MAC) design is presented for networked control systems (NCSs). It is challenging to design an optimal or suboptimal controller since the sensor packets and control packets could be lost in unreliable wireless networks. An ad hoc and efficient control policy is presented. Numerical results illustrate that the cost performance of the protocol sequence based NCS is much better than that of the π-persistent random access based NCS.

This paper proposes a Markov model for analyzing the performance of periodic broadcast in vehicular ad hoc networks (VANETs). Since there is no packet retransmission in broadcast, we only consider a one-dimensional Markov model. In the proposed model, we assume that the traffic condition is unsaturated and introduce a state to represent the situation when there is no packet waiting for transmission in the buffer of a node. Meanwhile, the state of a backoff counter changes only when the channel is sensed idle. When the channel is busy, the state of the backoff counter does not change. Moreover, we use a discrete time D/M/1 queue to model the periodic broadcast of each node. Based on this model, we also show numerical results to analyze the performance of the periodic broadcast in VANETs in terms of packet collision probability and average packet delay.

Corner feature point detection with both the high-speed and high-quality is still very demanding for many real-time computer vision applications. The Harris and Kanade-Lucas-Tomasi (KLT) are widely adopted good quality corner feature point detection algorithms due to their invariance to rotation, noise, illumination, and limited view point change. Although they are widely adopted corner feature point detectors, their applications are rather limited because of their inability to achieve real-time performance due to their high complexity. In this paper, we redesigned Harris and KLT algorithms to reduce their complexity in each stage of the algorithm: Gaussian derivative, cornerness response, and non-maximum suppression (NMS). The complexity of the Gaussian derivative and cornerness stage is reduced by using an integral image. In NMS stage, we replaced a highly complex sorting and NMS by the efficient NMS followed by sorting the result. The detected feature points are further interpolated for sub-pixel accuracy of the feature point location. Our experimental results on publicly available evaluation data-sets for the feature point detectors show that our low complexity corner detector is both very fast and similar in feature point detection quality compared to the original algorithm. We achieve a complexity reduction by a factor of 9.8 and attain 50 f/s processing speed for images of size 640$\,\times\,$480 on a commodity central processing unit with 2.53 GHz and 3 GB random access memory.

This paper proposes a real-time design for accurate stereo matching on compute unified device architecture (CUDA). We present a leading local algorithm and then accelerate it by parallel computing. High matching accuracy is achieved by cost aggregation over shape-adaptive support regions and disparity refinement using reliable initial estimates. A novel sample-and-restore scheme is proposed to make the algorithm scalable, capable of attaining several times speedup at the expense of minor accuracy degradation. The refinement and the restoration are jointly realized by a local voting method. To accelerate the voting on CUDA, a graphics processing unit (GPU)-oriented bitwise fast voting method is proposed, faster than the traditional histogram-based approach with two orders of magnitude. The whole algorithm is parallelized on CUDA at a fine granularity, efficiently exploiting the computing resources of GPUs. Our design is among the fastest stereo matching methods on GPUs. Evaluated in the Middlebury stereo benchmark, the proposed design produces the most accurate results among the real-time methods. The advantages of speed, accuracy, and desirable scalability advocate our design for practical applications such as robotics systems and multiview teleconferencing.

The high-speed feature detection is still very demanding for many computer vision applications. In this paper, the Harris and KLT corner detectors are redesigned to reduce the complexity of the algorithm. The complexity of Harris and KLT corner detectors are reduced by using the box kernel, the integral image and efficient non-maximum suppression, achieving complexity reduction by a factor of 8. For the image of size 1000×700, our method costs only 74ms on a commodity CPU with 2GHz and 1GB RAM.

In this paper, we present the Kanade-Lucas-Tomasi (KLT) tracking algorithm coupled with a varying integration window, which tracks a small subset of feature points to initialize the approximate motion model between images. For the remaining larger subset of the feature points initial tracking location is predicted by using this motion model, thus improving the tracking result. For an image of size 1000×700, the computational cost is reduced by a factor of 9.5 and tracking 500 features with our method runs in only 64 ms on a commodity 2GHz CPU with 1GB RAM.

Abstract Summary: GWAF, Genome-Wide Association analyses with Family, is an R package designed for GWAF. It implements association tests between a batch of genotyped or imputed single nucleotide polymorphisms (SNPs) and a binary or continuous trait with user specified genetic model, and generates informative results from the analyses. In addition, GWAF provides functions to visualize results. We evaluated GWAF using a simulated continuous trait and a binary trait dichotomized from the simulated continuous trait with real genotype data from the Framingham Heart Study's SNP Health Association Resource project. Availability: http://cran.r-project.org/web/packages/GWAF/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

To properly benchmark and stimulate current stereo algorithms specifically in the application context of view interpolation, a robust quantitative evaluation approach is important. As a prevailing quality assessment method, interpolation error has been widely used. It measures the distortions between an interpolated image and a real camera image for a desired virtual viewpoint. However, is it a robust quality metric, especially when state-of-the-art stereo technology is developing so fast? This paper hence focuses on revealing several rarely attended weaknesses that make the interpolation error evaluation paradigm vulnerable. In addition, we propose an alternative evaluation method as an early attempt at addressing these challenges, from a perspective of communication system. Evaluation of representative stereo methods from the Middlebury Web site shows that the new approach yields consistent quality assessment outcomes.

In this paper, we address the issue of transducing the object cutout model from an example image to novel image instances. We observe that although object and background are very likely to contain similar colors in natural images, it is much less probable that they share similar color configurations. Motivated by this observation, we propose a local color pattern model to characterize the color configuration in a robust way. Additionally, we propose an edge profile model to modulate the contrast of the image, which enhances edges along object boundaries and attenuates edges inside object or background. The local color pattern model and edge model are integrated in a graph-cut framework. Higher accuracy and improved robustness of the proposed method are demonstrated through experimental comparison with state-of-the-art algorithms.

In this paper, we propose Sparse Non-negative Pattern Learning (SNPL) based on self-taught learning framework. In the algorithm, visual patterns are first learned from unlabeled data by non-negative matrix approximation with sparseness constraints, and then features are extracted by the second part of the algorithm, a conjugate family based non-negative sparse feature extraction method. By combining sparse and non-negative constraints of patterns together, SNPL model gives a better representation for images than state-of-art methods. Beyond that, we give an analytical solution for feature extraction although it is approximate, and thereby we extract the features for self-taught learning framework in a faster and more stable way. We apply the new model to various areas, including pattern coding, feature extraction, and recognition. Experimental results show the advantages of SNPL model.

Most techniques for image analysis consider the image labels fixed and without uncertainty. In this paper, we address the problem of ordinal/rank label prediction based on training samples with uncertain labels. First, the core ranking model is designed as the bilinear fusing of multiple candidate kernels. Then, the parameters for feature selection and kernel selection are learned by maximum a posteriori for given samples and uncertain labels. The convergency provable Expectation-Maximization (EM) method is used for inferring these parameters. The effectiveness of the proposed algorithm is finally validated by the extensive experiments on age ranking task. The FG-NET and Yamaha aging database are used for the experiments, and our algorithm significantly outperforms those state-of-the-art algorithms ever reported in literature.

A novel patch-based correspondence model is presented in this paper. Many segment-based correspondence approaches have been proposed in recent years. Untextured pixels and boundaries of discontinuities are imposed with hard constraints by the discontinuity assumption that large disparity variation only happens at the boundaries of segments in the above approaches. Significant improvements on performance of untextured and discontinuity area have been reported. But, the performance near occlusion is not satisfactory because a segmented region in one image may be only partially visible in the other one. To solve this problem, we utilize the observation that the shared edge of a visible area and an occluded area corresponds to the discontinuity in the other image. So, the proposed model conducts color segmentation on both images first and then a segment in one image is further cut into smaller patches corresponding to the boundaries of segments in the other when it is assigned with a disparity. Different visibility of patches in one segment is allowed. The uniqueness constraint in a segment level is used to compute the occlusions. An energy minimization framework using graph-cuts is proposed to find a global optimal configuration including both disparities and occlusions. Besides, some measurements are taken to make our segment-based algorithm suffer less from violation of the discontinuity assumption. Experimental results have shown superior performance of the proposed approach, especially on occlusions, untextured areas, and near discontinuities.

A novel method is presented to detect roads in synthetic aperture radar (SAR) images. A multi-segmented poly-line model is introduced to provide a more accurate description of the road as well as to ensure the road curve's smoothness in the model level. We then solve the road detection problem using the Bayesian tracking theory, where the particle filtering algorithm is adopted to provide a simple and consistent framework. The effectiveness and robustness of the proposed method is demonstrated by experimental results.

Gradient conveys important salient features in images. Traditional fusion methods based on gradient generally treat gradients from multichannels as a multi-valued vector, and compute its global statistics under the assumption of identical distribution. However, different source channels may reflect different important salient features, and their gradients are basically non-identically distributed. This prevents existing methods from successful salience preservation. In this paper, we propose to fuse the gradients from multi-channels in the concept of saliency. We first measure the salience map of each channel's gradient, and then use their saliency to weight their contribution in computing the global statistics. Gradients with high saliency are properly highlighted in the target gradient, and thereby salient features in the sources are well preserved. Furthermore, we handle the dynamic range problem by applying range compression on the target gradient, and thereby halo effect is effectively reduced.

The lacking of automatic screen systems that can deal with large volume of time-lapse optical microscopy imaging is a bottleneck of modern bio-imaging research. In this paper, we propose an effective automated analytic system that can be used to acquire, track and analyze cell-cycle behaviors of a large population of cells. We use traditional watershed algorithm for cell nuclei segmentation and then a novel hybrid merging method is proposed for fragments merging. After a distance and size based tracking procedure, the performance of fragments merging is improved again by the sequence context information. At last, the cell nuclei can be classified into different phases accurately in a continuous hidden Markov model (HMM). Experimental results show the proposed system is very effective for cell sequence segmentation, tracking and cell phase identification.

Recently, interactive image cutout technique becomes prevalent for image segmentation problem due to its easy-to-use nature. However, most existing stroke-based interactive object cutout system did not consider the user intention inherent in the user interaction process. Strokes in sequential steps are treated as a collection rather than a process, and only the color information of the additional stroke is used to update the color model in the graph cut framework. Accordingly, unexpected fluctuation effect may occur during the process of interactive object cutout. In fact, each step of user interaction reflects the user's evaluation of previous result and his/her intention. By analyzing the user's intention behind the interaction, we propose a progressive cut algorithm, which explicitly models the user's intention into a graph cut framework for the object cutout task. Three aspects of user intention are utilized: 1) the color of the stroke indicates the kind of change s/he expects, 2) the location of the stroke indicates the region of interest, 3) the relative position between the stroke and the previous result indicates the segmentation error. By incorporating such information into the cutout system, the new algorithm removes the unexpected fluctuation effect of existing stroke-based graph-cut methods, and thus provides the user a more controllable result with fewer strokes and faster visual feedback. Experiments and user study show the strength of progressive cut in accuracy, speed, controllability, and user experience.

Occlusion is one of the challenging problems in stereo. In this paper, we solve the problem in a segment-based style. Both images are segmented, and we propose a novel patch-based stereo algorithm that cuts the segments of one image using the segments of the other, and handles occlusion areas in a proper way. A symmetric graph-cuts optimization framework is used to find correspondence and occlusions simultaneously. The experimental results show superior performance of the proposed algorithm, especially on occlusions, untextured areas and discontinuities

Robustness and discriminability are two key issues in face recognition. In this paper, we propose a new algorithm which extracts micro-structural Gabor feature to achieve good robustness and discriminability simultaneously. We first design a family of directional block partitions to compute the block-level directional projections of the classical Gabor feature. Then we use two statistical kernels, i.e, the mean kernel and the variance kernel, to extract the micro-structural statistics. Analysis of both robustness and discriminability is conducted to show that the new feature is not only more robust to misalignment, but also more discriminative than the classical down-sampling Gabor feature, which is further demonstrated by three groups of experiments on the BANCA dataset.

In the traditional DKL algorithm, PCA is performed before LDA to achieve stable numerical computation and good generalization in small-sample-size problems. However, PCA is based on the global information, ignoring the significant local characteristics. This paper will propose a novel algorithm called discriminant local feature analysis based on a broader understanding of LFA features. In the algorithm, LFA instead of PCA is applied before LDA. On the one hand, LFA captures local characteristics with little loss of global information. On the other hand, it presents an effective low-dimensional representation of signals, and thus reduces the dimensionality for LDA. By combining LFA and LDA, the DLFA algorithm outperforms DKL, which is showed by experiments on open-set face verification.

Facial symmetry is a useful natural characteristic of facial images, which can help in the development of face-oriented recognition technology and algorithms. The paper applies it to face recognition after introducing mirror images. By combining PCA with the even-odd decomposition principle, a new algorithm called symmetrical principal component analysis is proposed, in which different energy ratios of even/odd symmetrical principal components and their different sensitivities to pattern variations are employed for feature selection. This algorithm has two outstanding advantages. Firstly, it effectively improves the stability of features and remarkably raises the recognition rate. Secondly, it greatly saves computational cost as well as storage space.