Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%, and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.

Despite Siamese trackers attracting much attention due to their scalability and efficiency in recent years, researchers have ignored the background appearance, which leads to their inapplicability in recognizing arbitrary target objects with various variations, especially in complex scenarios with background clutter and distractors. In this paper, we present a simple yet effective Siamese tracker, where the shifted windows multi-head self-attention is produced to learn the characteristics of a specific given target object for visual tracking. To validate the effectiveness of our proposed tracker, we use the Swin Transformer as the backbone network and introduced an auxiliary feature enhancement network. Extensive experimental results on two evaluation datasets demonstrate that the proposed tracker outperforms other baselines.

Based on imperfect channel state information (CSI), the energy efficiency (EE) of downlink distributed antenna systems (DASs) with multiple receive antennas is investigated assuming composite Rayleigh fading channels. A new EE is introduced which is defined as the ratio of the average transmission rate to the total consumed power. According to this definition, an optimal power allocation (PA) scheme is developed for maximizing EE in a DAS subject to the maximum transmit power constraint. It is shown that a PA solution for the constrained EE optimization does exist and is unique. A Newton method based practical iterative algorithm is presented to solve PA. To avoid the iterative calculation, a suboptimal PA scheme is derived by means of the Lambert function, which yields a closed-form PA. The developed schemes include the ones under perfect CSI as special cases, and only need the statistical CSI. Thus, they have low overhead and good robustness. Moreover, the theoretical EE under imperfect CSI is derived for performance evaluation, and the resulting closed-form EE expression is obtained. Simulation results indicate that the theoretical EE can match the corresponding simulated value well, and the developed suboptimal scheme has performance close to optimal one, but with lower complexity.

With maturation of 5G technology in recent years, multimedia services such as live video streaming and online games on the Internet have flourished. These multimedia services frequently require low latency, which pose a significant challenge to compute the high latency requirements multimedia tasks. Mobile edge computing (MEC), is considered a key technology solution to address the above challenges. It offloads computation-intensive tasks to edge servers by sinking mobile nodes, which reduces task execution latency and relieves computing pressure on multimedia devices. In order to use MEC paradigm reasonably and efficiently, resource allocation has become a new challenge. In this paper, we focus on the multimedia tasks which need to be uploaded and processed in the network. We set the optimization problem with the goal of minimizing the latency and energy consumption required to perform tasks in multimedia devices. To solve the complex and non-convex problem, we formulate the optimization problem as a distributed deep reinforcement learning (DRL) problem and propose a federated Dueling deep Q-network (DDQN) based multimedia task offloading and resource allocation algorithm (FDRL-DDQN). In the algorithm, DRL is trained on the local device, while federated learning (FL) is responsible for aggregating and updating the parameters from the trained local models. Further, in order to solve the not identically and independently distributed (non-IID) data problem of multimedia devices, we develop a method for selecting participating federated devices. The simulation results show that the FDRL-DDQN algorithm can reduce the total cost by 31.3% compared to the DQN algorithm when the task data is 1000 kbit, and the maximum reduction can be 35.3% compared to the traditional baseline algorithm.

Most state-of-the-art discriminative tracking approaches are based on either template appearance models or statistical appearance models. Despite template appearance models have shown excellent performance, they perform poorly when the target appearance changes rapidly. In contrast, statistic appearance models are insensitive to fast target state changes, but they yield inferior tracking results in challenging scenarios such as illumination variations and background clutters. In this paper, we propose an adaptive object tracking approach with complementary models based on template and statistical appearance models. Both of these models are unified via our novel combination strategy. In addition, we introduce an efficient update scheme to improve the performance of our approach. Experimental results demonstrate that our approach achieves superior performance at speeds that far exceed the frame-rate requirement on recent tracking benchmarks.

By tracking the changing rate of hard decisions during every two consecutive iterations of the alternating direction method of multipliers (ADMM) penalized decoding, an efficient early termination (ET) criterion is proposed to improve the convergence rate of ADMM penalized decoder for low-density parity-check (LDPC) codes. Compared to the existing ET criterion for ADMM penalized decoding, the proposed method can reduce the average number of iterations significantly at low signal-to-noise ratios with negligible performance degradation.

Multi-pattern matching is a key technique for implementing network security applications such as Network Intrusion Detection/Protection Systems (NIDS/NIPSes) where every packet is inspected against tens of thousands of predefined attack signatures written in regular expressions (regexes). To this end, Deterministic Finite Automaton (DFA) is widely used for multi-regex matching, but existing DFA-based researches have claimed high throughput at an expense of extremely high memory cost, so fail to be employed in devices such as high-speed routers and embedded systems where the available memory is quite limited. In this paper, we propose a parallel architecture of DFA called Parallel DFA (PDFA) taking advantage of the large amount of concurrent flows to increase the throughput with nearly no extra memory cost. The basic idea is to selectively store the underlying DFA in memory modules that can be accessed in parallel. To explore its potential parallelism we intensively study DFA-split schemes from both state and transition points in this paper. The performance of our approach in both the average cases and the worst cases is analyzed, optimized and evaluated by numerical results. The evaluation shows that we obtain an average speedup of 100 times compared with traditional DFA-based matching approach.

A facial components based facial expression recognition algorithm with sparse representation classifier is proposed. Sparse representation classifier is based on sparse representation and computed by L1-norm minimization problem on facial components. The features of “important” training samples are selected to represent test sample. Furthermore, fuzzy integral is utilized to fuse individual classifiers for facial components. Experiments for frontal views and partially occluded facial images show that this method is efficient and robust to partial occlusion on facial images.

A novel compress-and-forward (CF) system based on multi-relay network is proposed. In this system, two networks are linked, wherein one is a sensor network connecting the analog source and the relays, and the other is a communication network between the relays and the destination. At several parallel relay nodes, the analog signals are transformed into digital signals after quantization and encoding and then the digital signals are transmitted to the destination. Based on the Chief Executive Officer (CEO) theory, we calculate the minimum transmission rate of every source-relay link and we propose a system model by combining sensor network with communication network according to Shannon channel capacity theory. Furthermore, we obtain the best possible system performance under system power constraint, which is measured by signal-to-noise ratio (SNR) rather than bit error rate (BER). Numerical simulation results show that the proposed CF outperforms the traditional amplify-and-forward (AF) system in the performance versus SNR.

In this paper, we study the power allocation (PA) scheme design for energy efficiency (EE) maximization with discrete-rate adaptive modulation (AM) in the downlink distributed antenna system (DAS). By means of the Karush-Kuhn-Tucker (KKT) conditions, an optimal PA scheme with closed-form expression is derived for maximizing the EE subject to maximum transmit power and target bit error rate (BER) constraints, where the number of active transmit antennas is also derived for attaining PA coefficients. Considering that the optimal scheme needs to calculate the PA of all transmit antennas for each modulation mode, its complexity is extremely high. For this reason, a low-complexity suboptimal PA is also presented based on the antenna selection method. By choosing one or two remote antennas, the suboptimal scheme offers lower complexity than the optimal one, and has almost the same EE performance as the latter. Besides, the outage probability is derived in a performance evaluation. Computer simulation shows that the developed optimal scheme can achieve the same EE as the exhaustive search based approach, which has much higher complexity, and the suboptimal scheme almost matches the EE of the optimal one as well. The suboptimal scheme with two-antenna selection is particularly effective in terms of balancing performance and complexity. Moreover, the derived outage probability is in good agreement with the corresponding simulation.

Recent research on the assessment of low probability of interception (LPI) radar waveforms is mainly based on limiting spectral properties of Wigner matrices. As the dimension of actual operating data is constrained by the sampling frequency, it is very urgent and necessary to research the finite theory of Wigner matrices. This paper derives a closed-form expression of the spectral cumulative distribution function (CDF) for Wigner matrices of finite sizes. The expression does not involve any derivatives and integrals, and therefore can be easily computed. Then we apply it to quantifying the LPI performance of radar waveforms, and the Kullback-Leibler divergence (KLD) is also used in the process of quantification. Simulation results show that the proposed LPI metric which considers the finite sample size and signal-to-noise ratio is more effective and practical.

This paper proposes a coarse-fine Time-to-Digital Converter (TDC), based on a Ring-Tapped Delay Line (RTDL). The TDC achieves the picosecond's level timing resolution and microsecond's level dynamic range at low cost. The TDC is composed of two coarse time measurement blocks, a time residue generator, and a fine time measurement block. In the coarse blocks, RTDL is constructed by redesigning the conventional Tapped Delay Line (TDL) in a ring structure. A 12-bit counter is employed in one of the two coarse blocks to count the cycle times of the signal traveling in the RTDL. In this way, the input range is increased up to 20.3µs without use of an external reference clock. Besides, the setup time of soft-edged D-flip-flops (SDFFs) adopted in RTDL is set to zero. The adjustable time residue generator picks up the time residue of the coarse block and propagates the residue to the fine block. In the fine block, we use a Vernier Ring Oscillator (VRO) with MOS capacitors to achieve a scalable timing resolution of 11.8ps (1 LSB). Experimental results show that the measured characteristic curve has high-level linearity; the measured DNL and INL are within ± 0.6 LSB and ± 1.5 LSB, respectively. When stimulated by constant interval input, the standard deviation of the system is below 0.35 LSB. The dead time of the proposed TDC is less than 650ps. When operating at 5 MSPS at 3.3V power supply, the power consumption of the chip is 21.5mW. Owing to the use of RTDL and VRO structures, the chip core area is only 0.35mm × 0.28mm in a 0.35µm CMOS process.

When an image of a 3D object is transmitted or recorded, its range image as well its grey-scale image are required. In this paper, we propose a method of coding for efficient compression of a pair of a pair of range and grey-scale images of 3D objects. We use Lambertian reflection optical model to model the relationship between a 3D object shape and it's brightness. Good illuminant direction estimation leads to good grey-scale image generation and furthermore effects compression results. A method for estimating the illuminant derection and composite albedo from grey-scale image statistics is presented. We propose an approach for estimating the slant angle of illumination based on an optical model from a pair of range and grey-scale images. Estimation result shows that our approach is better. Using the estimated parameters of illuminant direction and composite albedo a synthetic grey-scale image is generated. For comparison, a comparison coding method is used, in which we assume that the range and grey-scale images are compressed separately. We propose an efficient compression coding method for a pair of range and grey-scale images in which we use the correlation between range and grey-scale images, and compress them together. We also evaluate the coding method on a workstation and show numerical results.