Alex X. Liu’s research while affiliated with Midea Group and other places

The widespread use of modern network communications necessitates effective resource control and management in TCP/IP networks. However, most existing network traffic classification methods are limited to labeled known classes and struggle to handle open-set scenarios, where known classes coexist with significant volumes of unknown classes of traffic. To solve this problem more accurately and reliably, we propose RoNeTC. This method achieves high-precision classification by enhancing feature extraction and quantifying the reliability of classification decisions through uncertainty estimation. For feature extraction, we divide each packet of a flow into three views for parallel training, integrating both local and global feature representations across multiple packets to enhance accuracy. We devise a second-order classification probability to quantify the reliability of the classifier’s results and to visualize the reliability of open-set flow classification in terms of uncertainty. Additionally, we dynamically fuse classification decisions from multiple views, evaluating decision uncertainty to classify known and unknown flows and ensure robust, reliable results. We compare RoNeTC with four state-of-the-art (SOTA) methods in six open-set scenarios. RoNeTC outperforms the other methods by an average of 25.94% in F1 across all open-set scenarios, indicating its superior performance in open-set network traffic classification.

This study advances the precision and efficiency of precipitation nowcasting, particularly under extreme weather conditions. Traditional forecasting methods struggle with precision, spatial feature generalization, and recognizing long-range spatial correlations, challenges that intensify during extreme weather events. The Enhanced Temporal Correlation Jump Prediction Network (ETCJ-PredNet) introduces a novel attention mechanism that optimally leverages spatiotemporal data correlations. This model scrutinizes and encodes information from previous frames, enhancing predictions of high-intensity radar echoes. Additionally, ETCJ-PredNet addresses the issue of gradient vanishing through an innovative jump connection strategy. Comparative experiments on the Moving Modified National Institute of Standards and Technology (Moving-MNIST) and Hong Kong Observatory Dataset Number 7 (HKO-7) validate that ETCJ-PredNet outperforms existing models, particularly under extreme precipitation conditions. Detailed evaluations using Critical Success Index (CSI), Heidke Skill Score (HSS), Probability of Detection (POD), and False Alarm Ratio (FAR) across various rainfall intensities further underscore its superior predictive capabilities, especially as rainfall intensity exceeds 30 dbz,40 dbz, and 50 dbz. These results confirm ETCJ-PredNet’s robustness and utility in real-time extreme weather forecasting.

Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional nature of the language. With the development of quantum technology, quantum machine learning models leverage the superposition property of qubits to enhance model expressiveness and achieve faster computation compared to classical systems. To promote the development of quantum machine learning in low-resource language domains, we propose a quantum-classical hybrid architecture. This architecture utilizes a pre-trained multilingual BERT model to obtain vector representations of words and combines the proposed Batched Upload Quantum Recurrent Neural Network (BUQRNN) and Parameter Non-shared Batched Upload Quantum Recurrent Neural Network (PN-BUQRNN) as feature extraction models for sentiment analysis in Bengali. Our numerical results demonstrate that the proposed BUQRNN structure achieves a maximum accuracy improvement of 0.993% in Bengali text classification tasks while reducing average model complexity by 12%. The PN-BUQRNN structure surpasses the BUQRNN structure once again and outperforms classical architectures in certain tasks.

For the creation of wireless network applications, node locations are frequently necessary. However, communication effectiveness, measurement accuracy, and localization stability will be low in irregular multi-hop networks when locating nodes using conventional algorithms. To this end, a novel cooperative localization algorithm using expected minimum segments (LEMS, for short) is proposed in this paper. LEMS begins by measuring the distance between paired nodes, which is completed along with network initialization. Then, each unlocated node constructs its own sub-network, including it, based on the error characteristics among anchor nodes. Finally, each unlocated node searches for its estimated location in its sub-region based on the objective function generated by the chaotic mapping. Simulation results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art regarding efficiency, accuracy, and stability for various irregular networks. Specifically, our proposed algorithm achieves a median improvement in localization accuracy of 0.62 to 29.57 times and a reduction in the range of localization errors of 0.06 to 16.8 times.

Mobile radio frequency identification (RFID)…..

Anomaly detection in multivariate time series is an important problem with applications in several domains. However, the key limitation of the approaches that have been proposed so far lies in the lack of a highly parallel model that can fuse temporal and spatial features. In this paper, we propose TDRT, a three-dimensional ResNet and transformer-based anomaly detection method. TDRT can automatically learn the multi-dimensional features of temporal–spatial data to improve the accuracy of anomaly detection. Using the TDRT method, we were able to obtain temporal–spatial correlations from multi-dimensional industrial control temporal–spatial data and quickly mine long-term dependencies. We compared the performance of five state-of-the-art algorithms on three datasets (SWaT, WADI, and BATADAL). TDRT achieves an average anomaly detection F1 score higher than 0.98 and a recall of 0.98, significantly outperforming five state-of-the-art anomaly detection methods.

Mobile radio frequency identification (RFID) systems typically experience the continual movement of many tags rapidly going in and out of the interrogating range of readers. Readers that are deployed to maintain a current, real-time list of tags, which are present in the interrogating zone at any moment, must repeatedly execute a series of reading cycles. Each of these reading cycles provides the readers very limited time to identify unknown tags (those newly entering into the reader’s range), and, at the same time, to detect missing tags (those just leaving the reader’s range). In this paper, we study the continuous tag recognition problem, which is critical for mobile RFID systems. First, we obtain a lower bound on communication time for solving this problem. We then design a near-OPTimal protocoL, called OPT-L, and prove that its communication time is approximately equal to the lower bound. Finally, we present extensive simulation and experimental results that demonstrate OPT-L’s superior performance over other existing protocols.

Physical-Layer Authentication (PLA) attracts a lot of research interests because of its significant advantages over upper-layer authentication mechanisms: high security and low complexity. The PLA schemes can be categorized into passive and active schemes. In this paper, we extensively leverage the advantages of both the active and passive schemes as a reference scheme, named as the Direct Hybrid (DH) scheme. Although the DH scheme improves the authentication performance of the prior PLA schemes, it has limitations, e.g., high communication overhead. Then, we further propose two hybrid PLA schemes to overcome the limitations of the DH scheme. The first proposed scheme further uses the advantage of the Challenge-Response Authentication Mechanism (CRAM) scheme, named as the CR-based Hybrid (CRH) scheme. Although both DH and CRH schemes significantly improve the authentication performance of the prior PLA schemes, they do not address one significant limitation of the active scheme, i.e., to set the power allocation of a tag empirically. Thus, based on the CRH scheme, we further propose the Adaptive CR-Based Hybrid (ACRH) scheme to adaptively set the parameter instead of the empirical setting. Moreover, we provide the theoretical analysis of the proposed schemes over wireless fading channels and derive their closed-form expressions in terms of the Probability of Detection (PD), Probability of False Alarm (PFA), and optimal threshold, respectively. At last, we discuss the advantages and disadvantages of the proposed schemes and give some useful suggestions for seeking a better tradeoff. Our experimental results show that, in comparison with the active scheme, the DH scheme has better robustness, and the CRH scheme has better both robustness and compatibility but it sacrifices the security. The ARCH scheme achieves a better tradeoff than the remaining schemes.

Wireless Power Transfer (WPT) has received more and more attention for its convenience and reliability. In this paper, we first propose the notion of omnidirectional charging. First, we consider the problem of detecting whether the target area achieves omnidirectional charging given a deterministic deployment of chargers. We use piecewise constant approximation and area discretization techniques to partition the target area and approximate charging power as constants. Next, we propose the Minimum Coverage Set extraction technique to design a fast detection algorithm. Second, we design a charger deployment scheme that satisfies omnidirectional charging. By placing the chargers at the triangle lattice points, we estimate the length of triangle lattice side length that satisfies omnidirectional charging, and derive the error bound with the optimal length. Third, we determine the probability that the target area achieves omnidirectional charging given a random deployment of chargers. We devise both analytical and numerical solutions for the problem with good accuracy. Finally, we conduct simulation and field experiments, and the results show that the running speed of our omnidirectional charging detection algorithm is at least $1\times$ faster than comparison algorithms, and the consistency degree of our theoretical results and field experimental results is larger than $93.6 \%$ .