In the literature, it has been shown that the secrecy capacity of the additive white Gaussian noise (AWGN) wiretap channel with noise-free feedback equals the capacity of the same model without secrecy constraint, and the classical Schalkwijk-Kailath (SK) scheme achieves the secrecy capacity. In this paper, we show that in finite blocklength regime, the SK scheme is not optimal, and propose a modified SK scheme which may perform better than the classical one. Besides this, this paper establishes a finite blocklength converse for the AWGN wiretap channel with feedback, which can also be viewed as a converse for the same model without secrecy constraint. To the best of the authors' knowledge, this is the first paper to address such a problem, and the results of this paper are further explained via numerical examples.

Fault detection in users’ reactive power compensators (URPCs) remains a critical challenge, particularly for general commercial and industrial consumers lacking technical expertise. Undetected URPC malfunctions not only increase electricity costs for users but also aggravate utility power losses. To address this issue, we propose a novel remote fault detection framework that exploits the joint distribution of active load levels and power factors derived from metering data. A vision transformer with a large margin-aware focal model is then employed to effectively classify the operational states of URPCs, using joint frequency distribution matrices as characteristic representations. Unlike conventional approaches, the proposed method relies exclusively on metering data, thereby simplifying deployment and enhancing accessibility for nonspecialist users. This enables timely operation and maintenance of URPCs, reducing electricity costs and improving overall power system efficiency. The effectiveness of the proposed approach is validated through extensive simulations.

In this paper, coding for the covert computation over Gaussian multiple-access channel (GMAC) with feedback is investigated, where a receiver wishes to decode a function of the sources transmitted over the GMAC while ensuring a low probability of detection by a warden. Traditionally, in covert communication, the feedback from the receiver to the transmitters helps to share a secret key which is used to confuse the warden. This paper shows that a slight modification of the existing Schalkwijk-Kailath (SK) type feedback scheme for computation over GMAC satisfies covert constraint by itself, which indicates that the SK-type feedback coding schemes in the literature can also be viewed as covert computation/communication schemes for channels with feedback.

ABSTRACT To address the challenge of low positioning accuracy caused by sensor uncertainties in mobile robot dead reckoning systems, this study proposes Trans‐GCN, a novel position prediction model that integrates Graph Convolutional Networks (GCN) with a Transformer architecture. The model leverages data‐driven AI principles and sensor‐specific characteristics to uncover hidden dependencies between wheel speed and inertial data, thereby enhancing navigation accuracy. Initially, the sensor data is segmented using a sliding window approach and represented as multiple graph structures. GCN is employed to capture spatial dependencies by learning the complex topological structures inherent in the data. Subsequently, positional encoding of graph feature signals is embedded into the Transformer, enabling more efficient extraction of global node features. An adaptive learning rate is introduced to enhance flexibility and efficiency in information propagation. The integrated model performs multi‐sensor data modeling and feature fusion to predict the two‐dimensional displacement increments of the mobile robot at each sampling interval, ultimately reconstructing the navigation trajectory. The model is trained under GNSS availability and used to predict robot positions during GNSS signal degradation or outages. Six sets of experiments were conducted on the publicly available NCLT dataset and a self‐collected dataset. Results demonstrate that the proposed model achieves a trajectory fitting accuracy of 89.2%–97.7% in scenarios with partial or complete GNSS failures. The proposed model also improves training and inference speeds by 19.6% and 26.0%, respectively, compared to state‐of‐the‐art methods, validating its superior performance in dead reckoning.

Electricity theft users with zero electricity usage (UZEU) should be specifically concerned in electricity theft detection (ETD) research. The challenges are: they provide no effective information on electricity usage behaviors, and they are easily confused with vacant house users. This has caused the majority of the existing detection methods relying on single electricity usage to fail to identify UZEU accurately. Hence, this article first analyzes the underlying correlation between water and electricity (W&E) usage collected by the smart meter. This analysis then lends the theoretical basis to propose a new ETD method by comprehensively using the multisource information. More precisely, the proposed method utilizes the mutual information coefficient (MIC) to construct a correlation model between W&E usage and in turn the wavelet clustering algorithm to cluster the MIC of the power distribution users. Thereafter, the resulting weak correlations indicate the suspected users as the electricity theft UZEU in case of zero electricity usage. Finally, the proposed method is validated by numerical experiments in the real world and illustrated to be more accurate than existing methods in detecting UZEU.

The detection and tracking of dynamic traffic participants (e.g., pedestrians, cars, and bicyclists) plays an important role in reliable decision-making and intelligent navigation for autonomous vehicles. However, due to the rapid movement of the target, most current vision-based tracking methods, which perform tracking in the image domain or invoke 3D information in parts of their pipeline, have real-life limitations such as lack of the ability to recover tracking after the target is lost. In this work, we overcome such limitations and propose a complete system for dynamic object tracking in 3D space that combines: (1) a 3D position tracking algorithm based on monocular camera and LIDAR for the dynamic object; (2) a re-tracking mechanism (RTM) that restore tracking when the target reappears in camera's field of view. Compared with the existing methods, each sensor in our method is capable of performing its role to preserve reliability, and further extending its functions through a novel multimodality fusion module. We perform experiments in the real-world self-driving environment and achieve a desired 10Hz update rate for real-time performance. Our quantitative and qualitative analysis shows that this system is reliable for dynamic object tracking purposes of self-driving cars.

Abstract Wireless image sensor network generates a large number of images from the distributed camera sensors. The image data need to be delivered securely and efficiently to the sink in many circumstances. The current node‐disjoint multipath and dispersive routings cannot provide enough security and efficiency for the image data collection and transportation. In this paper, we propose an ellipse batch dispersive routing (EBDR) algorithm to address the secure and efficient data collection issue in wireless image sensor network. Images are broken into many shares using (K,N) threshold secret sharing. A multi‐hop path is built for each share. All hop nodes are constrained in an ellipse. The routing chooses a relay node for each share of an image in each hop selection step. Relay nodes are dispersed in the whole ellipse area. We analyze the interception probability of shares and delivery delay of EBDR compared with multicast tree‐assisted random propagation. Simulation experiments show that EBDR can obtain better security and efficiency for the routing of image data than multicast tree‐assisted random propagation routing protocol. Copyright © 2013 John Wiley & Sons, Ltd.