Dual-frequency continuous wave radar, as a promising Internet of Things, has been used for indoor human tracking, activity detection, and smart homes. Previous indoor tracking primarily used short-time Fourier transform (STFT) for instantaneous frequencies extraction. However, STFT has low resolution and suffers from Heisenberg’s uncertainty principle, which limits the positioning accuracy. Therefore, this article introduces an improved time-frequency analysis (TFA) algorithm called UTFA-Net, capable of significantly enhancing time-frequency resolution, potentially outperforming traditional principles, and thus boosting the precision of through-wall radar target tracking. The proposed framework is founded on an end-to-end self-supervised neural network architecture, integrating novel Rényi and consistency loss mechanisms. Meanwhile, we propose a multidimensional spectrogram generation module, short time fusion attention, and a nonlocal module to boost the model’s spectrogram generation capabilities. In order to validate proposed UTFA-Net, a comprehensive set of experiments is undertaken, including simulation tests, ablation studies, and indoor tracking experiments. The results indicate that UTFA-Net surpasses current advanced algorithms in four performance metrics, showcasing the highest tracking accuracy.

Doppler radar is a cost-effective Internet of Things (IoT) device widely utilized in smart homes, urban management, and health monitoring. Conventional Doppler radars, which detect targets from a single perspective, can only extract the radial information from the radar echoes and struggle to detect stationary targets or targets moving tangentially to the radar. Furthermore, the receivers commonly encounter the issue of ambiguous frequency (AF) simultaneously, making it difficult for conventional Doppler radar to track multiple targets accurately. To address these limitations, this article enhances the target detection capabilities of Doppler radars through the design of both radar hardware structure and Doppler frequency (DF) estimation algorithms. First, a multiperspective radar system is proposed to provide richer target information and substantially minimize the AF area. Second, a novel DF estimation algorithm, based on reservoir computing (RC) theory, is proposed to estimate the DFs of targets in these reduced ambiguous intervals. Lastly, an error compensation process, adapted to the characteristics of the echoes, is designed to reduce the accumulation of estimation errors. Compared to conventional Doppler radar systems, this approach reveals more precise target information and suppresses AF interference, a critical advantage in multitarget tracking environments.

The range-Doppler (RD) spectrum of frequency-modulated continuous-wave (FMCW) radar is widely used in various fields, making high-resolution RD spectrum estimation a critical research focus. This letter proposes HRRD-Net, an end-to-end high-resolution RD spectrum estimation algorithm for FMCW radar. HRRD-Net uses 2-D complex signals as input and achieves strong anti-noise performance with a matched filtering module and denoising loss. A coarse RD spectrum is generated using a discrete Fourier transform (DFT)-initialized module, and its resolution is enhanced by a super-resolution module. Four experiments—perception, frequency accuracy, frequency resolution, and real super-resolution RD—demonstrate HRRD-Net’s ability to generate high-resolution RD spectra, even under noisy conditions, outperforming advanced RD estimation algorithms, and its frequency accuracy has been improved by approximately 37.78% compared to the Periodogram.

Deep reinforcement learning (DRL) has superior autonomous decision-making capabilities, combining deep learning and reinforcement learning (RL). Unlike DRL employs deep neural networks (DNNs), broad RL (BRL) adopts the broad learning system (BLS) that is established with flat networks to generate the strategy. This article proposes the multiagent adaptive broad-DRL (ABDRL) approach for traffic light control (TLC), which combines the broad network with the deep network structure. Specifically, the structure of ABDRL first expands in the form of flatted broad networks. Then, the feature representation module that contains DNNs is employed to extract the critical traffic information. In addition, experiences sampled randomly by the experience replay mechanism cannot reflect the current training status of the agent effectively. In order to alleviate the impacts caused by random sampling, the forgetful experience mechanism (FEM) is incorporated into ABDRL. The FEM enables the agent to discriminate the importance of experiences stored in the experience reply buffer to improve robustness and adaptability. We validate the effectiveness of ABDRL in TLC, and the results illustrate the optimality and robustness of ABDRL over the state-of-the-art multiagent DRL (MADRL) algorithms.

Efficient traffic light control is a critical part of realizing smart transportation. In particular, deep reinforcement learning (DRL) algorithms that use deep neural networks (DNNs) have superior autonomous decision-making ability. Most existing work has applied DRL to control traffic lights intelligently. In this article, we propose a novel context-aware multiagent broad reinforcement learning (CAMABRL) approach based on broad reinforcement learning (BRL) for mixed pedestrian-vehicle adaptive traffic light control (ATLC). CAMABRL exploits the broad learning system (BLS) established in a flat network structure to make decisions instead of a deep network structure. Unlike previous works that consider the attributes of vehicles, CAMABRL also takes the states of pedestrians waiting at the intersection into consideration. Combining with the context-aware mechanism that utilizes the states of adjacent agents and potential state information captured by the long short-term memory (LSTM) network, agents can make farsighted decisions to alleviate traffic congestion. The experimental results show that CAMABRL is superior to several state-of-the-art multiagent reinforcement learning (MARL) methods.

Compared to sequential learning models, graph-based neural networks exhibit some excellent properties, such as ability capturing global information. In this paper, we investigate graph-based neural networks for text classification problem. A new framework TensorGCN (tensor graph convolutional networks), is presented for this task. A text graph tensor is firstly constructed to describe semantic, syntactic, and sequential contextual information. Then, two kinds of propagation learning perform on the text graph tensor. The first is intra-graph propagation used for aggregating information from neighborhood nodes in a single graph. The second is inter-graph propagation used for harmonizing heterogeneous information between graphs. Extensive experiments are conducted on benchmark datasets, and the results illustrate the effectiveness of our proposed framework. Our proposed TensorGCN presents an effective way to harmonize and integrate heterogeneous information from different kinds of graphs.

Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.

Due to technology scaling, lifetime reliability is becoming one of the major design constraints in the performance optimization of future many-core systems. Given a lifetime reliability constraint, the existing lifetime-constrained runtime mapping schemes often lead to low throughput because of the requirement to map all applications to compact regions. In this paper, we propose a runtime application mapping scheme that exploits a borrowing strategy to improve the throughput of many-core systems given a lifetime constraint. First, we propose using different strategies for mapping communication-intensive applications and computation-intensive applications. The lifetime reliability constraint can be relaxed in the local time scale when the communication requirement is high. The throughput is improved because the communication distance of communication-intensive applications is optimized while the waiting time of computation-intensive application is reduced. Then, we propose a method to effectively classify applications depending on the communication-to-computation ratio. A dynamic threshold is determined according to the current locations of available cores. Finally, we propose an improved neighborhood allocation scheme to reduce the communication cost in the task mapping. The experimental results show that compared to the state-of-the-art lifetime-constrained mapping, the proposed mapping scheme improves the throughput of many-core systems by 26% on average for synthetic task graphs and by 20% on average for realistic task graphs while the lifetime reliability is maintained within a constraint.

In the current paper, we propose a probabilistic generative model, the label correlation mixture model (LCMM), to depict multi-labeled document data, which can be utilized for multi-label text classification. LCMM assumes two stochastic generative processes, which correspond to two submodels: 1) a label correlation model; and 2) a label mixture model. The former model formulates labels’ generative process, in which a label correlation network is created to depict the dependency between labels. Moreover, we present an efficient inference algorithm for calculating the generative probability of a multi-label class. Furthermore, in order to optimize the label correlation network, we propose a parameter-learning algorithm based on gradient descent. The second submodel in the LCMM depicts the generative process of words in a document with the given labels. Different traditional mixture models can be adopted in this generative process, such as the mixture of language models, or topic models. In the multi-label classification stage, we propose a two-step strategy to most efficiently utilize the LCMM based on the framework of Bayes decision theory. We conduct extensive multi-label classification experiments on three standard text data sets. The experimental results show significant performance improvements comparing to existing approaches. For example, the improvements on accuracy and macro F-score measures in the OHSUMED data set achieve 28.3% and 37.0%, respectively. These performance enhancements demonstrate the effectiveness of the proposed models and solutions.

We propose a new unsupervised learning model, hidden softmax sequence model (HSSM), based on Boltzmann machine for dialogue structure analysis.The model employs three types of units in the hidden layer to discovery dialogue latent structures: softmax units which represent latent states of utterances; binary units which represent latent topics specified by dialogues; and a binary unit that represents the global general topic shared across the whole dialogue corpus.In addition, the model contains extra connections between adjacent hidden softmax units to formulate the dependency between latent states.Two different kinds of real world dialogue corpora, Twitter-Post and AirTicketBooking, are utilized for extensive comparing experiments, and the results illustrate that the proposed model outperforms sate-ofthe-art popular approaches.

Multi-label text categorization is more difficult but practical than the conventional binary or multi-class text categorization. This paper propose a novel probabilistic generative model, label correlation mixture model (LCMM), to depict the multiple labeled documents, which can be used for multi-label text categorization. In LCMM, labels and topics have the one-to-one correspondences. LCMM consists of two parts: label correlation model and multi-label conditioned document model. The former one formulates the generating process of labels and the dependencies between the labels are taken into account. We also propose an efficient algorithm for calculating the probability of generating an arbitrary subset of labels. Multi-label conditioned document model can be regarded as a supervised label mixture model, in which the labels for a document are known. To evaluate LCMM, multi-label text categorization experiments on three standard text data sets are performed. The experimental results demonstrate the effectiveness of LCMM, comparing to other reported methods.

A common method to solve a multiclass classification problem is to reduce the problem to a serial binary classification problems and combine them via Error-Correcting Output Codes (ECOC). The ECOC contains three parts: coding design, decoding algorithm, and base dichotomizer. Recently, the Loss-Weighted (LW) decoding algorithm (Escalera et al., PAMI2010), which introduces a weight matrix to the Loss-Based (LB) decoding (Allwein et al., JMLR2001), achieves improved performance over traditional decoding methods. However, the weight matrix is assigned empirically. In this paper, we present a theoretical global optimization method for the weight matrix, so as to achieve the minimal training risk. Although the experimental results on real-world image, audio and text classification tasks show that the proposed decoding method only leads to slightly better performances than others in the case of discrete outputs of the dichotomizers, the proposed method provides a new screen on the decoding methods of the ECOC.