Reconfigurable Intelligent Surface (RIS)-based direct modulation communication systems have garnered significant attention due to their low cost, low power consumption, and baseband-less characteristics. However, these systems face challenges such as the random time-varying coding state of the RIS and the difficulty in implementing beamforming in direct modulation. In this paper, we propose a simple and effective joint space-time coding approach for RIS that enables simultaneous realization of both direct modulation communication and beamforming. By modeling the transmitted signals of the RIS using space-time coding, we show that the time coding determines the direct modulation functionality, while the space coding governs the beamforming. Consequently, we introduce a joint time-space coding technique by performing exclusive-or (XOR) operations on the time and space coding sequences, enabling both functionalities to be achieved concurrently. Numerical simulations demonstrate the effectiveness of the proposed method. Furthermore, we design and fabricate a transmissive 1-bit phase reconfigurable RIS operating in the 3.4-3.79 GHz frequency band for the implementation of a direct modulation communication system. Experimental results reveal that the bit error rate (BER) is significantly reduced when joint space-time coding is used, compared to using time coding alone. Additionally, the root-mean-square error vector magnitude (rmsEVM) of the constellation diagram is reduced by 55%. This technique is promising for applications in the Internet of Things (IoT), contributing to the development of intelligent networks for electronic devices.

With the progress of computer vision and machine learning, the research of object detection and pedestrian recognition has demonstrated significant performance. However, the identification studies in domestic animals, especially in the same species of domestic animals, remains a significant challenge. His study focuses on distinguishing cashmere and dairy goats, which share similar traits. Our contributions are: (1) Proposing a dynamic quadruple optimization algorithm to optimize goat images from local and global dimensions, enhancing network representation with a multi-branch structure; (2) Introducing a novel transfer learning algorithm based on goat granularity to preview dataset knowledge; (3) Validating our approach on our goat dataset and a public bird dataset. We achieved recognition accuracies of 95% for cashmere goats, 94.04% for dairy goats, and 82.48% on the public dataset, demonstrating the effectiveness of our methods for animal biometric identification.

With the rapid growth of Low Earth Orbit (LEO) satellite networks, satellite-IoT systems using the LoRa technique have been increasingly deployed to provide widespread Internet services to low-power and low-cost ground devices. However, the long transmission distance and adverse environments from IoT satellites to ground devices pose a huge challenge to link reliability, as evidenced by the measurement results based on our real-world setup. In this paper, we propose a blind coherent combining design named B2LoRa to boost LoRa transmission performance. The intuition behind B2LoRa is to leverage the repeated broadcasting mechanism inherent in satellite-IoT systems to achieve coherent combining under the low-power and low-cost constraints, where each re-transmission at different times is regarded as the same packet transmitted from different antenna elements within an antenna array. Then, the problem is translated into aligning these packets at a fine granularity despite the time, frequency, and phase offsets between packets in the case of frequent packet loss. To overcome this challenge, we present three designs — joint packet sniffing, frequency shift alignment, and phase drift mitigation to deal with ultra-low SNRs and Doppler shifts featured in satellite-IoT systems, respectively. Finally, experiment results based on our real-world deployments demonstrate the high efficiency of B2LoRa.

Unmanned aerial vehicles (UAVs), serving as small mobile platforms, are well-suited for emitter detection and localization tasks in dynamic environments. However, implementing large antenna array apertures on the compact platform of UAVs is impractical. This article presents a small-aperture phase mode antenna (PMA) direction-finding (DF) system for UAV applications. Initially, a small-aperture, high port-isolation dual-port PMA is introduced. Through miniaturization and decoupling, a conventional slot antenna pair is transformed into the final dual-port PMA. The dual-port PMA features a compact aperture of$0.4~\lambda \times 0.47~\lambda $(where$\lambda $is the wavelength at 2 GHz), with port isolation reaching up to 40 dB. Next, we investigate the integration of the dual-port PMA with a novel amplitude-only DF (AODF) method, termed the single PMA multiple patterns amplitude comparison (SPMAMP-AC) method. This method utilizes multiple patterns to construct a DF function cluster, providing higher DF accuracy over a broader Field of View (FoV) compared to the traditional AODF method. The measured results indicate maximum DF errors of 2.4°, 4.3°, 4.9°, and 7.9° for FoVs of$60{^{\circ }}~(\theta \in $[−30°, 30°]),$140{^{\circ }}~(\theta \in $[−70°, 70°]),$160{^{\circ }}~(\theta \in $[−80°, 80°]), and$180{^{\circ }}~(\theta \in $[−90°, 90°]) in the yoz plane, respectively.

Abstract In computer vision, the task of 3D reconstruction from monocular sparse images poses significant challenges, particularly in the field of animal modelling. The diverse morphology of animals, their varied postures, and the variable conditions of image acquisition significantly complicate the task of accurately reconstructing their 3D shape and pose from a monocular image. To address these complexities, we propose S‐LASSIE, a novel technique for 3D reconstruction of quadrupeds from monocular sparse images. It requires only 10–30 images of similar breeds for training. To effectively mitigate depth ambiguities inherent in monocular reconstructions, S‐LASSIE employs a multi‐angle projection loss function. In addition, our approach, which involves fusion and smoothing of bone structures, resolves issues related to disjointed topological structures and uneven connections at junctions, resulting in 3D models with comprehensive topologies and improved visual fidelity. Our extensive experiments on the Pascal‐Part and LASSIE datasets demonstrate significant improvements in keypoint transfer, overall 2D IOU and visual quality, with an average keypoint transfer and overall 2D IOU of 59.6% and 86.3%, respectively, which are superior to existing techniques in the field.

The security of cloud-based databases is a crucial topic in current research. The primary solutions are focused on access control and data encryption. A central challenge these solutions face is navigating the balance between system security and operational efficiency. However, individualized data privacy requires fine-grained data protection at columns/rows or individual elements. In existing fine-grained resource or policy protection methods, resource access predominantly uses a traversal method, leading to a linear growth in time overhead. In response to these issues, this paper presents a fine-grained access control mechanism based on search trees. This mechanism provides element-level resource protection and accelerates policy search using an index tree, thereby reducing the time overhead of the fine-grained access control system. Experimental results demonstrate that this mechanism achieves element-level resource protection while maintaining low-performance overhead.

In this paper, we investigate the fundamental multiple access (MA) scheme in an active intelligent reflecting surface (IRS) aided energy-constrained Internet-of-Things (IoT) system, where an active IRS is deployed to assist the uplink transmission from multiple IoT devices to an access point (AP). Our goal is to maximize the sum throughput by optimizing the IRS beamforming vectors across time and resource allocation. To this end, we first study two typical active IRS aided MA schemes, namely time division multiple access (TDMA) and non-orthogonal multiple access (NOMA), by analytically comparing their achievable sum throughput and proposing corresponding algorithms. Interestingly, we prove that given only one available IRS beamforming vector, the NOMA-based scheme generally achieves a larger throughput than the TDMA-based scheme, whereas the latter can potentially outperform the former if multiple IRS beamforming vectors are available to harness the favorable time selectivity of the IRS. To strike a flexible balance between the system performance and the associated signaling overhead incurred by more IRS beamforming vectors, we then propose a general hybrid TDMA-NOMA scheme with device grouping, where the devices in the same group transmit simultaneously via NOMA while devices in different groups occupy orthogonal time slots. By controlling the number of groups, the hybrid TDMA-NOMA scheme is applicable for any given number of IRS beamforming vectors available. Despite of the non-convexity of the considered optimization problem, we propose an efficient algorithm based on alternating optimization, where each subproblem is solved optimally. Simulation results illustrate the practical superiorities of the active IRS over the passive IRS in terms of the coverage extension and supporting multiple energy-limited devices, and demonstrate the effectiveness of our proposed hybrid MA scheme for flexibly balancing the performance-cost tradeoff.

In this paper, we study an intelligent reflecting surface (IRS)-aided radar-communication (Radcom) system, where the IRS is leveraged to help Radcom base station (BS) transmit the joint of communication signals and radar signals for serving communication users and tracking targets simultaneously. The objective of this paper is to minimize the total transmit power at the Radcom BS by jointly optimizing the active beamformers, including communication beamformers and radar beamformers, at the Radcom BS and the phase shifts at the IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR) required by communication users, the minimum SINR required by the radar, and the cross-correlation pattern design. In particular, we consider two cases, namely, case I and case II, based on the presence or absence of the radar cross-correlation design and the interference introduced by the IRS on the Radcom BS. For case I where the cross-correlation design and the interference are not considered, we prove that the dedicated radar signals are not needed, which significantly reduces implementation complexity and simplifies algorithm design. Then, a penalty-based algorithm is proposed to solve the resulting non-convex optimization problem. Whereas for case II considering the cross-correlation design and the interference, we unveil that the dedicated radar signals are needed in general to enhance the system performance. Since the resulting optimization problem is more challenging to solve as compared with the case I, the semidefinite relaxation (SDR) based alternating optimization (AO) algorithm is proposed. Particularly, instead of relying on the Gaussian randomization technique to obtain an approximate solution by reconstructing rank-one solution, the tightness is achieved by our proposed reconstruction strategy. Simulation results demonstrate the effectiveness of proposed algorithms and also show the superiority of the proposed scheme over various benchmark schemes.

With the rapid development of electronic and information technologies, the Internet of Everything (IoE) has become one of the trendiest topics in both academia and industry. Therein, many types of space/air/ground platforms need to be connected to networks for breaking down the isolation of information islands and providing various services. Space/air/ground platforms, such as satellites, unmanned aerial vehicles (UAVs), airships, balloons, terrestrial vehicles, and high-speed trains (HSTs) have emerged for accomplishing various complex tasks. Wireless communication is one of the most important technologies to support the real-time delivery of control commands and mission-related data. On the other hand, the space-air-ground integrated network has become a promising paradigm for the six-generation (6G) mobile communication network, where the aerospace and terrestrial vehicles may need to connect to existing mobile cellular networks or act as base stations (BSs) or relays to assist terrestrial wireless communications. To meet the ever-increasing demands of high capacity, wide coverage, low latency, and strong robustness for communications, it is promising to adopt large-scale antenna arrays at the transceivers to obtain considerable array gains and improve the channel quality. Antenna array-enabled beamforming technologies can facilitate spectrum reuse, interference mitigation, coverage enhancement, and physical-layer security. Antenna arrays can also be used to promote the sensing capability of space/air/ground networks, where the sensing information may be carefully processed to assist communications. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive, and tricky challenges in antenna array design, physical layer, multiple access control layer, and network layer. As a result, numerous new research issues require to be addressed, which cover a wide range of disciplines including communication theory, network theory, antenna theory, signal processing, protocol design, resource allocation, optimization, hardware implementation, and experimentation.

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking.

Coronary calcification is a strong indicator of coronary artery disease and a key determinant of the outcome of percutaneous coronary intervention. We propose a fully automated method to segment and quantify coronary calcification in intravascular OCT (IVOCT) images based on convolutional neural networks (CNN). All possible calcified plaques were segmented from IVOCT pullbacks using a spatial-temporal encoder-decoder network by exploiting the 3D continuity information of the plaques, which were then screened and classified by a DenseNet network to reduce false positives. A novel data augmentation method based on the IVOCT image acquisition pattern was also proposed to improve the performance and robustness of the segmentation. Clinically relevant metrics including calcification area, depth, angle, thickness, volume, and stent-deployment calcification score, were automatically computed. 13844 IVOCT images with 2627 calcification slices from 45 clinical OCT pullbacks were collected and used to train and test the model. The proposed method performed significantly better than existing state-of-the-art 2D and 3D CNN methods. The data augmentation method improved the Dice similarity coefficient for calcification segmentation from 0.615±0.332 to 0.756±0.222, reaching human-level inter-observer agreement. Our proposed region-based classifier improved image-level calcification classification precision and F1-score from 0.725±0.071 and 0.791±0.041 to 0.964±0.002 and 0.883±0.008, respectively. Bland-Altman analysis showed close agreement between manual and automatic calcification measurements. Our proposed method is valuable for automated assessment of coronary calcification lesions and in-procedure planning of stent deployment.

MOTIVATION: Several methods have been proposed for the paired RNA-seq analysis. However, many of them do not consider the heterogeneity in treatment effect among pairs that can naturally arise in real data. In addition, it has been reported in literature that the false discovery rate (FDR) control of some popular methods has been problematic. In this paper, we present a full hierarchical Bayesian model for the paired RNA-seq count data that accounts for variation of treatment effects among pairs and controls the FDR through the posterior expected FDR. RESULTS: Our simulation studies show that most competing methods can have highly inflated FDR for small to moderate sample sizes while PairedFB is able to control FDR close to the nominal levels. Furthermore, PairedFB has overall better performance in ranking true differentially expressed genes (DEGs) on the top than others, especially when the sample size gets bigger or when the heterogeneity level of treatment effects is high. In addition, PairedFB can be applied to identify the biologically significant DEGs with controlled FDR. The real data analysis also indicates PairedFB tends to find more biologically relevant genes even when the sample size is small. PairedFB is also shown to be robust with respect to the model misspecification in terms of its relative performance compared to others. AVAILABILITY AND IMPLEMENTATION: Software to implement this method (PairedFB) can be downloaded at: https://sites.google.com/a/udel.edu/qiujing/publication. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A novel radar is proposed and verified based on a two-element time-modulated array. The distance of the object is estimated from the time delay between the source signal and the return one, while the direction is calculated by analyzing the spectrum of the return signal after the time modulation. The proposed radar needs no rotary machinery or electronic scanning system to detect the direction of the object, and it can deal with multiple objects from different directions simultaneously, if their distance differences are larger than the range resolution. Numeric simulations are provided to verify the proposed method.

The completeness of gene expression data is essential to many gene expression data analysis issues. In this paper, inspired by the idea of semi-supervised learning with tri-training, a hybrid iterative imputation method called tri-imputation is proposed to estimate the missing values in gene expression data. In detail, in each round of tri-imputation, any two imputation methods are collaborating with each other to firstly estimate an initial imputation value, and then to be applied to the rest imputation method for providing different available information. Finally, all these three results are combined with their respective pre-trained confidence values. Experimental results on real microarray matrices indicate that tri-imputation achieves more accurate estimation for missing values in terms of the lowest normalized root-mean-square error.

With people's increasing concern about health, judging people's health through medical record is becoming a potential demand. Most of preview disease analysis researches were conducted on structured dataset, which usually ignored the relationship between different symptoms, and the dataset was expensive to get. In this paper, we proposed a novel model based on Word2vec and Terms Frequency-Inverse Document Frequency (TF-IDF), which could be used to detect damp-heat syndrome on unstructured records directly. Firstly, we adopt ICTCLAS system combined with corpus collected in the field of Traditional Chinese Medicine (TCM) to segment the clinical records into words. Secondly, Word2vec tool was used to train word vector. Then, we constructed the record representation vector according to word vector and TF-IDF. The record representation method was named Word2vec+TF-IDF. In order to verify the effectiveness of the proposed method, we compared our record representation method with other text representation methods under four different classifiers. The experiment was conducted on the dataset collected from over 10 Chinese Medicine hospitals. And the experimental results show that our model perform better than the state-of-the-art methods such as LSA and Doc2vec.

BACKGROUD: DNA methylation is an epigenetic modification that plays important roles on gene regulation. Study of whole-genome bisulfite sequencing and reduced representation bisulfite sequencing brings the availability of DNA methylation at single CpG resolution. The main interest of study on DNA methylation data is to test the methylation difference under two conditions of biological samples. However, the high cost and complexity of this sequencing experiment limits the number of biological replicates, which brings challenges to the development of statistical methods. RESULTS: Bayesian modeling is well known to be able to borrow strength across the genome, and hence is a powerful tool for high-dimensional-low-sample-size data. In order to provide accurate identification of methylation loci, especially for low coverage data, we propose a full Bayesian partition model to detect differentially methylated loci under two conditions of scientific study. Since hypo-methylation and hyper-methylation have distinct biological implication, it is desirable to differentiate these two types of differential methylation. The advantage of our Bayesian model is that it can produce one-step output of each locus being either equal-, hypo- or hyper-methylated locus without further post-hoc analysis. An R package named as MethyBayes implementing the proposed full Bayesian partition model will be submitted to the bioconductor website upon publication of the manuscript. CONCLUSIONS: The proposed full Bayesian partition model outperforms existing methods in terms of power while maintaining a low false discovery rate based on simulation studies and real data analysis including bioinformatics analysis.

Data obtained from gene expression microarray experiments always suffer from missing values due to various reasons. However, complete gene expression data are of great importance to many gene expression data analysis issues. Therefore, imputation methods with high estimation precision are critical to further data analysis. In this paper, inspired by the idea of semi-supervised learning with tri-training, we propose a novel imputation method called TRIIM (TRIple IMputation). TRIIM estimates missing values using triple imputation strategies based on Bayesian principal component analysis (BPCA), local least squares (LLS) and expectation maximization (EM). The data properties of global correlation information, local structure and data distribution are all considered properly. It is implemented by sharing the estimated values of any two algorithms' cooperation to the rest at each step, and assembling combinations of all imputation results finally. Experimental results on four real microarray matrices demonstrate that TRIIM achieves better performance than the comparative algorithms in terms of normalized root mean square error (NRMSE), even in the case of microarray dataset with large missing rates and few complete genes.

Traditional Chinese medicine (TCM) has accumulated amount of literature with a total of 1,059 volumes, more than 190,000 chapters, and more than 120,000,000 words during the last 2000 years. In the previous works, researchers annotated the phrases one by one with their own hands. Here we propose semantic annotation techniques based on Semantic units division and annotation are realized through constructing a corpus and professional semantic unit dictionary. Based on the technology, a semantic annotation method is implemented using hidden markov models, which achieves 92.2% in terms of micro-average F1 measure and 87.6% in terms of macro-average F1 measure on the case of spleen putty genre.