The minimalist and ultra-low-power natures of ambient backscatter communications (AmBC) make it a compelling wireless paradigm for Internet of Things (IoT), which in turn puts AmBC at great risk of being monitored. Recently, covert backscatter systems have been explored to secure transmission without detection, but they mainly introduce artificial noise and beamforming, consuming additional energy. In this paper, we propose a spatial modulation (SM)-enabled multi-antenna covert AmBC system that guarantees covertness and raises the covert capacity without requiring additional energy consumption. Specifically, the multiple-antenna backscatter tag transmits covert information by adopting SM, while its remaining antennas superimpose overt messages to shelter the covert transmission. We derive the closed-form expression of the minimum detection error probability (DEP) to capture the covertness, that represents the worst-case for the tag. Furthermore, we formulate a covert capacity maximization problem by optimizing the power reflection coefficient under the constraints of the covertness requirement. By exploiting the objective function's monotonic properties and the constraints, we derive the optimal power reflection coefficient and the corresponding covert capacity. Due to the target expression is very complex and lacks a closed-form solution, we introduce a Taylor expansion to simplify it. Finally, numerical results demonstrate the performance and effectiveness of the proposed multi-antenna covert backscatter communication system. As a result, it shown that the covert capacity can be significantly improved compared with the benchmark scheme.

The quality of visual media is critically impacted by low illumination and the presence of airborne particulates, leading to challenges in brightness balance, color saturation, and texture clarity which are detrimental to various applications in image processing and computer vision. Addressing these challenges, this study introduces a novel image enhancement algorithm that significantly improves the quality of degraded images. Our proposed method, the multi-channel phase activation and multi-constraint dark channel prior (MMDCP), leverages an innovative approach by integrating the phase-adjusted Gaussian kernel function for brightness channel optimization in the Fourier transform frequency domain. This optimization is enhanced through the application of a saturated dark channel prior, achieving simultaneous brightness enhancement and color fidelity. Furthermore, we refine the dark channel prior deblurring algorithm by incorporating intensity, brightness, and color constraints to correct overexposure issues and color offsets in the reconstructed images. The efficacy of the MMDCP algorithm is demonstrated through extensive experimentation, comparing it against six contemporary image enhancement algorithms using two types of objective indicators and subjective assessments across four public datasets. The MMDCP algorithm consistently outperforms the existing methods, with a notable average improvement of 20% in PSNR and 19.6% in SSIM metrics, substantiating its superiority in enhancing brightness, detail, and color accuracy. This study’s results underline the MMDCP algorithm’s robustness and versatility in improving image quality across various conditions, including daytime, nighttime, indoor, and outdoor settings.

Video anomaly detection has always been a challenging task in computer vision due to data imbalance and susceptibility to scene variations such as lighting and occlusions. In response to this challenge, this paper proposes an unsupervised video anomaly detection method based on an attention-enhanced memory network. The method utilizes a dual-stream network structure of autoencoders, enhancing the model’s learning ability for important features in appearance and motion by introducing coordinate attention mechanisms and variance attention mechanisms, emphasizing significant characteristics of static objects and rapidly moving regions. By adding memory modules to both the appearance and motion branches, the network structure’s memory information is reinforced, enabling it to capture long-term spatiotemporal dependencies in videos and thereby improving the accuracy of anomaly detection. Furthermore, by optimizing the network structure’s activation functions to handle negative inputs, it enhances its nonlinear modeling capabilities, enabling better adaptation to complex environments, including variations in lighting and occlusions, further improving the effectiveness of anomaly detection. The paper conducts comparative experiments and ablation studies using three public available datasets and various models. The results demonstrate that compared to baseline models, the AUC performance is improved by 3.9%, 4.7%, and 1.7% on UCSD Ped2, CHUK Avenue, and ShanghaiTech datasets, respectively. When compared with the other models, the average AUC performance is improved by 4.3%, 5.4%, and 6.2%, with an average improvement of 8.75% in the ERR metric, validating the effectiveness and adaptability of the proposed method. The code can be obtained at the following URL: https://github.com/AcademicWhite/AEMNet .

Image matching can be transformed into the problem of feature point detection and matching of images. The current neural network methods have a weak detection effect on feature points and cannot extract enough sparse and uniform feature points. In order to improve the detection and description ability of feature points, this paper proposes a self‐supervised feature point detection and description network based on asymmetric convolution: ACPoint. Specifically, first, feature point pseudolabels are learned from an unlabeled dataset, and pseudolabels are used for supervised learning; then, the learned model is used to update pseudolabels. Through multiple iterations of model training and label updating, high‐quality labels and high‐accuracy models are obtained adaptively. The asymmetric convolution feature point (ACPoint) network adopts an asymmetric convolution module to simultaneously train three convolution branches to learn more feature information, which uses two one‐dimensional convolutions to enhance the backbone of square convolution from both horizontal and vertical directions and improve the representation of local features during inference. Based on the ACPoint network, a cross‐resolution image‐matching method is proposed. Experiments show that our proposed network model has higher localization accuracy and homography estimation ability on the HPatches dataset.

Cross-media hashing, which encodes data points from different modalities into a common Hamming space, has been successfully applied to solve large-scale multimedia retrieval issue due to storage efficiency and search effectiveness. Recently, matrix factorization based hashing methods have drawn considerable attention for their promising search accuracy. However, pioneer methods mainly focus on learning consensus hash codes for different modalities, but neglect the potential inconsistency among different modalities, \emph{e.g.,} the diversities of different modalities and noises, which may undermine the retrieval accuracy. To address this problem, we propose a novel unsupervised hashing model, namely, Discrete Robust Matrix Factorization Hashing (DRMFH), which simultaneously formulates the consistency and inconsistency across different modalities into a matrix factorization based model. Specifically, a homogenous space composed of a consistent Hamming space and an inconsistent diversity part, are generated by matrix factorization for each modality. Therefore, the consensus information across different modalities can be well captured in the learnt hash codes, leading to improved retrieval performance. Moreover, we design an effective optimization algorithm which is able to obtain an approximate discrete code matrix with linear time complexity. Comprehensive experimental results on three public multimedia retrieval datasets show that the proposed DRMFH outperforms several state-of-the-art methods.

Recently, the coherent optic wireless communication (OWC) has received extensive attention due to its superiority over traditional intensity modulated direct detection (IMDD) systems. Yet, the Doppler effect could be a showstopper for coherent OWC which is sensitive to the frequency offset and spread. Thus we introduce a new modulation scheme, which called orthogonal time frequency space (OTFS) modulation, into the coherent OWC to solve the Doppler problem. OTFS transforms traditional time-varying channel into delay-Doppler (DD) domain, which ensures all transmit symbols experience an almost identical and slowly varying sparse channel. Thus full channel diversity in time and frequency can be obtained when a suitable receiver is used. In addition, most of the research on Doppler effect only consider the random Doppler spread and average frequency shift to the signal, while ignoring the spectral spread caused by the frequency-dependent Doppler shift of a wideband signal such as radar signals and terahertz signals. To accurately model the Doppler effect, it is necessary to calculate the Doppler frequency shift according to the frequency bin of each sub-carrier, thereby the overall frequency offset and spectral spread are included. This manuscript proposes a Doppler channel model based on frequency-domain subband partition (FDSP), and for the first time, this wideband Doppler channel model is applied into wideband OTFS and orthogonal frequency division multiplexing (OFDM) systems. We carried out simulation for OTFS and OFDM under different velocities. Simulation results show that the OTFS can resist high Doppler frequency shift in high mobility scenarios, and in the case of large subcarrier spacing, OTFS is less affected by the Doppler frequency shift of each subcarrier than OFDM.

Abstract Online social networks (OSNs) have been an integral aspect of daily life. Most people use OSNs such as Facebook and Twitter to communicate and exchange information. Numerous analytical methods have been applied to OSNs by active exploration of their graph structure, like clustering analyses for automatic online group detection and node impact analyses of awareness of influential nodes within social networks. On OSN, a large‐scale attack was launched by creating a bogus profile and using it to propagate spam, malware, and phishing attacks. To overcome the above issues, we designed a trust management and data protection model (TM‐A‐DPM), in which the trust management identifies the trust factor accuracy to achieve confidentiality, integrity, and privacy. Experimental results determine the performance and computational efficiency as well as resolve the privacy threats such as data leakage and data forgery.

The increasing popularity of social media facilitates the propagation of fake news, posing a major threat to the government and journalism, and thereby making how to detect fake news from social media an urgent requirement. In general, multimodal-based methods can achieve better performance because of the complementation among different modalities. However, the majority of them simply concatenate features from different modalities, failing to well preserve the mutual information in common features. To address this issue, a novel framework named semantic-enhanced multimodal fusion network is proposed for fake news detection, which can better capture mutual features among events and thus benefit the detection of fake news. This model consists of three subnetworks, namely multimodal fusion and event domain adaptation networks as well as the fake news detector. Specifically, the multimodal fusion network aims to extract deep features from texts and images and fuse them into a common semantic feature known as a snapshot. Then, the fake news detector can learn the representation of posts. Finally, the event domain adaptation network can single out and remove the peculiar features of each event, and keep shared features among events. The experimental results show that the proposed model outperforms some state-of-the-art approaches on two real-world multimedia data sets.

As Internet of Things (IoT) technology is growing fast, it is clearly forseen that the number of IoT devices and the scale of connections will be further expanded. IoT is capable of taking advantage of the existing network infrastructure effectively, so as to achieve data sharing between devices. However, the large scale and complexity of the network structure will bring potential security risks to IoT system. The traditional access control model is more complex and centralized. This paper aims to build a trust-driven distributed self-collaborative security architecture of IoT based on blockchain and smart contracts, which could overcome the single-point failure problem of centralized entities. Implementation of the security architecture shows that it still possesses characteristics including scalability, lightweight, and fine granularity, while the secure access control preserves the privacy of IoT devices.

The resolution of the integer phase ambiguity among a group of spatially separated sensors is considered, where the smallest inter-sensor spacing exceeds half wavelength (λ/2) of a single-frequency signal. The sufficient condition that the integer phase ambiguities can be unambiguously determined is derived and verified numerically. The traditional receiver of a triplet is identified as a particular example of this general framework. An efficient strategy for averaging redundant virtual observations is outlined. The performance in terms of the success rate of ambiguity resolution is significantly improved due to the averaging process.

In order to dynamically assign the optical/electrical routing path and optimize the optical/electrical resource allocation, a software-defined hybrid optical/electrical data center architecture (SD-HDC for short) is proposed in this work. A control mechanism that consists of two parallel steps including the new request handle step and the hybrid resource optimize step is introduced in SD-HDC. The new request handle step is used to quickly process the new incoming request while the hybrid resource optimize step periodically optimizes the hybrid optical/electrical network resource. To validate the SD-HDC architecture, a hybrid optical/electical routing algorithm used for handling elephant/mice flows is also proposed in this paper. Experimental results show that the proposed SD- HDC architecture can effectively reduce the blocking probability and path provisioning latency.

Localization with low cost and high accuracy is of utmost importance for most applications in wireless sensor networks (WSNs). Most existing range-free schemes have limitations in one or more areas, such as low accuracy, low scalability and threat from the malicious nodes. Secure sensor localization in WSNs has received considerable attention with the rise of Internet of things. In this paper, we propose an efficient and secure range-free localization scheme based on the outlier elimination and the vector refinement process. An outlier elimination method is introduced to the localization process by filtering inaccurate beacon nodes in the presence of the malicious beacon nodes, which can prevent the location attacks and improve the localization accuracy. A vector refinement feedback process is introduced to further improve the localization accuracy. Parameters that affect the localization accuracy are discussed. Simulation and experiment are carried out to evaluate the performance of the proposed outlier elimination vector refinement DV-hop with the presence of the malicious beacon nodes. All the results show that the localization accuracy is improved significantly compared to the DV-hop algorithm and the vector refinement DV-hop algorithm, for both of the cases of with and without the malicious beacon nodes.