Physical-layer secret key generation (PKG) is an emerging technique for secret key sharing. However, researches on it rarely consider the issue of energy efficiency, which results in a limited performance gain at the expense of a large amount of consumed energy. In this paper, we define the secret key energy efficiency (KEE) as the ratio of the generated secret key bits to the total energy consumption in the resource constrained PKG system. An optimization problem with quality of service (QoS) requirement and power consumption constraints is formulated and a multi-layer iterative algorithm to maximize the KEE is proposed. To cope with the difficulty of the non-convex problem, we transform and iterate it until it is equivalent to the primal problem by applying the Dinkelbach algorithm. In each iteration, the penalty algorithm and difference-of-convex-functions (DC) programming algorithm are used to tackle the non-convex constraints and objectives, respectively. Simulation results demonstrate that the KEE which is maximized can be 84% higher than that of the secret key rate (SKR) maximization only at the cost of a 5% decrease in SKR.

Deep learning has made data-driven methods effective in underwater acoustic target recognition (UATR) using passive sonar signals. However, a major current challenge is the limited availability of underwater acoustic data, leading to suboptimal performance without sufficient data. Self-supervised learning (SSL) can help address this problem by learning intrinsic patterns within acoustic data. Nonetheless, applying SSL in UATR systems requires efficient learning of meaningful representations that can provide quick prediction speed for real-time recognition systems. To this end, we propose the masking hierarchical tokens (MHT) method to learn meaningful representations via efficient self-supervised learning for our previously proposed UATR-Transformer, giving rise to the MHT-UATR-Transformer. In particular, the MHT-UATR-Transformer first exploits a new designed token-convolution-based hierarchical tokenization to efficiently obtain rich time–frequency information from the input Mel-spectrogram. Then, most of these tokens are masked with a high masking ratio and subsequently reconstructed by an integrated Encoder–Decoder structure. In this way, the MHT-UATR-Transformer can learn intrinsic representations of underwater acoustic signals to achieve better recognition performance with fewer labeled data, which is expected to alleviate the dependency on expensive acoustic data. Experimental results on two widely studied underwater databases show that our proposed method achieves better performance than supervised learning and state-of-the-art SSL method in both accuracy and speed, especially in few-shot and noisy scenarios, thus enhancing its practicality in real marine applications.

String prediction (SP) is a very efficient screen content coding (SCC) tool which has been adopted in the third generation of Audio Video Standard (AVS3). It is observed that two special types of strings occur frequently. To further improve the coding efficiency for SCC on top of the original SP, a new variation of SP named Equal-value-string and Copy-above-string based SP (ECSP) is proposed. An ECSP coding unit uses only three types of strings: Equal-value-string, Copy-above-string, and Unpredictable-pixel-string. Compared with the AVS3 reference software HPM9.0 with ECSP disabled, using AVS3 SCC Common Test Condition and YUV 4:2:0 test sequences, the proposed technique achieves an average Y BD-rate reduction of5.54and3.01%for All Intra and Low Delay configurations, respectively, with low additional encoding and decoding complexity. The proposed ECSP has been adopted in the AVS3 standard.

Underwater acoustic target recognition (UATR) is usually difficult due to the complex and multi-path underwater environment. Currently, deep learning-based UATR methods have proven its effectiveness, and have outperformed the traditional methods by using powerful convolution neural networks (CNNs) to extract discriminative features on acoustic spectrograms. However, CNNs always fail to capture the global information implicated in spectrogram due to the use of small kernel, and thus encounter the performance bottleneck. To this end, we propose the UATR-Transformer based on a convolution-free architecture, referred to as the Transformer, which can perceive both the global and local information from acoustic spectrograms, and thus improve the accuracy. Experiments on two real world data demonstrate that our proposed model has achieved comparative results to the state of art CNNs, and thus can be applied to some certain cases in UATR.

We consider visual tracking in numerous applications of computer vision and seek to achieve optimal tracking accuracy and robustness based on various evaluation criteria for applications in intelligent monitoring during disaster recovery activities. We propose a novel framework to integrate a Kalman filter (KF) with spatial-temporal regularized correlation filters (STRCF) for visual tracking to overcome the instability problem due to large-scale application variation. To solve the problem of target loss caused by sudden acceleration and steering, we present a stride length control method to limit the maximum amplitude of the output state of the framework, which provides a reasonable constraint based on the laws of motion of objects in real-world scenarios. Moreover, we analyze the attributes influencing the performance of the proposed framework in large-scale experiments. The experimental results illustrate that the proposed framework outperforms STRCF on OTB-2013, OTB-2015 and Temple-Color datasets for some specific attributes and achieves optimal visual tracking for computer vision. Compared with STRCF, our framework achieves AUC gains of 2.8%, 2%, 1.8%, 1.3%, and 2.4% for the background clutter, illumination variation, occlusion, out-of-plane rotation, and out-of-view attributes on the OTB-2015 datasets, respectively. For sporting events, our framework presents much better performance and greater robustness than its competitors.

In this paper, we propose a Space-Code Index Modulation (SCIM) scheme, which combines Space Shift Keying (SSK) and M-ary Spread Spectrum (M-ary SS) modulations for wireless communications. The information bits are respectively mapped into the pseudo noise (PN) code index and the antenna index after series-to-parallel conversion at the transmitter. The combination of the SSK and the M-ary SS reduces the number of transmitting antennas greatly while preserving the original anti-jamming performance of the spread spectrum communication. The analysis and simulation results show that SCIM saves a lot of index resources compared to SSK for the same transmission rate, and the performance advantage of 2 dB when bit error rate is 10-5.

With the rapid growth of web 2.0, online product reviews generated by users are becoming increasingly useful for customers to make purchase decisions. In this paper, we focus on the problem of classifying user reviews as recommended the product or not. The proposed method first mines the product features and relevant opinions, and then determines the overall sentiment orientation of the review based on the polarity and strength of these opinions. The evaluation results show the effectiveness of our proposed method in product feature mining and review classification.

In this letter, a practical mean reflectivity model of radar land clutter (LC) for the complex system design of ground-based radars involved in low-angle targets on some typical terrains is studied using the inductive reasoning method. The functional relationships between the radar parameters and radar surface clutter backscattering are analyzed. Following the recent research work in the area of reflectivity modeling of LC in the literature, the least squares method is employed to estimate the model parameters. The model is validated using reliable practical data and shown to outperform other models in accuracy.

Built-in self-test (BIST) technique is now widely applied. How to estimate its testing capabilities is an important problem. BIST detectability is defined as the probability of that a fault set of the circuit-under-test is detected. It depends on the properties of the test at, circuit-under-test, as well as the signature analyser as a data compressor. The detectability of a signature analyzer is evaluated. The random and pseudorandom testing techniques are examined for their BIST detectability and several results are derived.>