In order to apply the mobile multimedia communication to the road information system, a seamless handover scheme is needed. Therefore, we present a novel micro-mobility scheme based on explicit multicast (Xcast) in this article. In our proposal, the mobile traffic of its forward direction is sent to the potential access routers of a mobile node (MN) by including IP addresses of the access routers explicitly in the IP headers. As for 802.11 wireless LAN, because it is difficult to know the potential ARs of a mobile node, a new trigger, called as the Candidate AR Trigger (CAT), is introduced to get this information from the link layer. The handover mechanism of our proposed scheme is depicted in detail and the merits of this scheme are also discussed. Finally, by using a combination of performance evaluation and simulation, we argue that our architecture is capable of providing seamless handover while introducing limited network overhead.

In this paper, we investigate multi-service forwarding in selfish wireless networks (SeWN) with selfish relay nodes (RN). The RN's node-selfishness is characterized from the perspectives of its residual energy and the incentive paid by the source, by which the degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES) are defined. Meanwhile, a framework of the node-selfishness management is conceived to extract the RNs' node-selfishness information (NSI). Based on the RN's NSI, the expected energy cost and expected service profit are determined for analyzing the effect of the RN's node-selfishness on the multi-service forwarding. Moreover, the optimal incentive paid by the source is obtained for minimizing its cost and, at the same time, effectively stimulating the multi-service delivery. Simulation validate our analysis.

Numerous examples in image processing have demonstrated that human visual perception can be exploited to improve processing performance. This paper presents another showcase in which some visual information is employed to guide adaptive block-wise compressive sensing (ABCS) for image data, i.e., a varying CS-sampling rate is applied on different blocks according to the visual contents in each block. To this end, we propose a visual analysis based on the discrete cosine transform (DCT) coefficients of each block reconstructed at the decoder side. The analysis result is sent back to the CS encoder, stage-by-stage via a feedback channel, so that we can decide which blocks should be further CS-sampled and what is the extra sampling rate. In this way, we can perform multiple passes of reconstruction to improve the quality progressively. Simulation results show that our scheme leads to a significant improvement over the existing ones with a fixed sampling rate.

A translucent wavelength switched optical network (WSON) is a cost-efficient infrastructure between opaque networks and transparent optical networks, which aims at seeking a graceful balance between network cost and service provisioning performance. In this paper, we experimentally present a resilient translucent WSON with the control of an enhanced path computation element (PCE) and extended generalized multi-protocol label switching (GMPLS) controllers. An adaptive routing and wavelength assignment scheme with the consideration of accumulated physical impairments, wavelength availabilities and regenerator allocation is experimentally demonstrated and evaluated for dynamic provisioning of lightpaths. By using two different network scenarios, we experimentally verify the feasibility of the proposed solutions in support of translucent WSON, and quantitatively evaluate the path computation latency, network blocking probability and service disruption time during end-to-end lightpath restoration. We also deeply analyze the experimental results and discuss the synchronization between the PCE and the network status. To the best of our knowledge, the most significant progress and contribution of this paper is that, for the first time, all the proposed methodologies in support of PCE/GMPLS controlled translucent WSON, including protocol extensions and related algorithms, are implemented in a network testbed and experimentally evaluated in detail, which allows verifying their feasibility and effectiveness when being potentially deployed into real translucent WSON.

Optimizating the deployment of wireless sensor networks, which is one of the key issues in wireless sensor networks research, helps improve the coverage of the networks and the system reliability. In this paper, we propose an evolutionary algorithm based on modified t-distribution for the wireless sensor by introducing a deployment optimization operator and an intelligent allocation operator. A directed perturbation operator is applied to the algorithm to guide the evolution of the node deployment and to speed up the convergence. In addition, with a new geometric sensor detection model instead of the old probability model, the computing speed is increased by 20 times. The simulation results show that when this algorithm is utilized in the actual scene, it can get the minimum number of nodes and the optimal deployment quickly and effectively.Compared with the existing mainstream swarm intelligence algorithms, this method has satisfied the need for convergence speed and better coverage, which is closer to the theoretical coverage value.

In this paper we present a rigorous vector 3D lithography simulator METROPOLE-3D which is designed to run moderately fast on conventional engineering workstations. METROPOLE-3D solves Maxwell's equations rigorously in three dimensions to model how the non-vertically incident light is scattered and transmitted in non-planar structures. METROPOLE-3D consists of several simulation modules: photomask simulator, exposure simulator, post-exposure baking module and 3D development module. This simulator has been applied to a wide range of pressing engineering problems encountered in state-of-the-art VLSI fabrication processes, such as layout printability/manufacturability analysis including reflective notching problems and optimization of an anti-reflective coating (ARC) layer. Finally, a 3D contamination to defect transformation study was successfully performed using our rigorous simulator.

Software-defined networking (SDN) decouples the control and forwarding of network devices, providing benefits such as simplified control. However, due to cost constraints and other factors, SDN is difficult to fully deploy. It has been proposed that SDN devices can be incrementally deployed in a traditional IP network, i.e., hybrid SDN, to provide partial SDN benefits. Studies have shown that better traffic engineering performance can be achieved by modifying the coverage and placement of SDN devices in hybrid SDN, because they can influence the behavior of legacy switches through certain strategies. However, it is difficult to develop and execute a traffic engineering strategy in hybrid SDN. This article proposes a routing algorithm to achieve approximate load balancing, which minimizes the maximum link utilization by using the optimal solution of linear programming and merging the minimum split traffic flows. A multipath forwarding mechanism under the same problem is designed to optimize transmission time. Experiments show that our algorithm has certain advantages in link utilization and transmission time compared to traditional distributed routing algorithms like OSPF and some hybrid SDN routing mechanisms. Furthermore, our algorithm can approximate the control effect of full SDN when the deployment rate of SDN devices is 40%.

A fully-integrated double-channel 5-Gb/s/ch 2:1 serializer array is designed and fabricated in a standard 0.18-$mu $m CMOS technology, which can be easily expanded to any even-number-channel array, e.g. 12 channels, by means of arrangement in a parallel manner. Besides two conventional half-rate 2:1 serializers, both phase-locked loop and delay-locked loop techniques are employed locally to deal with the involved clocking-related issues, which make the serializer array self-contained, compact and automatic. The system architecture, circuit and layout designs are discussed and analyzed in detail. The chip occupies a die area of 673,$mu $m$, imes ,$667,$mu $m with a core width of only 450,$mu $m. Measurement results show that it works properly without a need for additional clock channels, reference clocks, off-chip tuning, external components, and so on. From a single supply of 1.8,V, a power of 200,mW is consumed and a single-ended swing of above 300,mV for each channel is achieved.

Constructing accurate similarity graph is an important process in graph-based clustering. However, traditional methods have three drawbacks, such as the inaccuracy of the similarity graph, the vulnerability to noise and outliers, and the need for additional discretization process. In order to eliminate these limitations, an entropy regularized unsupervised clustering based on maximum correntropy criterion and adaptive neighbors (ERMCC) is proposed. 1) Combining information entropy and adaptive neighbors to solve the trivial similarity distributions. And we introduce l0-norm and spectral embedding to construct similarity graph with sparsity and strong segmentation ability. 2) Reducing the negative impact of non-Gaussian noise by reconstructing the error using correntropy. 3) The prediction label vector is directly obtained by calculating the sparse strongly connected components of the similarity graph Z, which avoids additional discretization process. Experiments are conducted on six typical datasets and the results showed the effectiveness of the method.

As a hot topic of speech signal processing, speech emotion recognition methods have been developed rapidly in recent years. Some satisfactory results have been achieved. However, it should be noted that most of these methods are trained and evaluated on the same corpus. In reality, the training data and testing data are often collected from different corpora, and the feature distributions of different datasets often follow different distributions. These discrepancies will greatly affect the recognition performance. To tackle this problem, a novel corpus-invariant discriminant feature representation algorithm, called transfer discriminant analysis (TDA), is presented for speech emotion recognition. The basic idea of TDA is to integrate the kernel LDA algorithm and the similarity measurement of distributions into one objective function. Experimental results under the cross-corpus conditions show that our proposed method can significantly improve the recognition rates.

Quad-core cpus have been a common desktop configuration for today's office. The increasing number of processors on a single chip opens new opportunity for parallel computing. Our goal is to make use of the multi-core as well as multi-processor architectures to speed up large-scale data mining algorithms. In this paper, we present a general parallel learning framework, Cut-And-Stitch, for training hidden Markov chain models. Particularly, we propose two model-specific variants, CAS-LDS for learning linear dynamical systems (LDS) and CAS-HMM for learning hidden Markov models (HMM). Our main contribution is a novel method to handle the data dependencies due to the chain structure of hidden variables, so as to parallelize the EM-based parameter learning algorithm. We implement CAS-LDS and CAS-HMM using OpenMP on two supercomputers and a quad-core commercial desktop. The experimental results show that parallel algorithms using Cut-And-Stitch achieve comparable accuracy and almost linear speedups over the traditional serial version.

In practice, emotional speech utterances are often collected from different devices or conditions, which will lead to discrepancy between the training and testing data, resulting in sharp decrease of recognition rates. To solve this problem, in this letter, a novel transfer semi-supervised non-negative matrix factorization (TSNMF) method is presented. A semi-supervised negative matrix factorization algorithm, utilizing both labeled source and unlabeled target data, is adopted to learn common feature representations. Meanwhile, the maximum mean discrepancy (MMD) as a similarity measurement is employed to reduce the distance between the feature distributions of two databases. Finally, the TSNMF algorithm, which optimizes the SNMF and MMD functions together, is proposed to obtain robust feature representations across databases. Extensive experiments demonstrate that in comparison to the state-of-the-art approaches, our proposed method can significantly improve the cross-corpus recognition rates.

Let m ≥ 3 be an odd positive integer, n=2m and p be an odd prime. For the decimation factor $d=rac{(p^{m}+1)(p^{m}+p-1)}{p+1}$, the cross-correlation between the p-ary m-sequence {tr1n(αt)} and its all decimated sequences {tr1n(αdt+l)} is investigated, where 0 ≤ l < gcd(d,pn-1) and α is a primitive element of Fpn. It is shown that the cross-correlation function takes values in {-1,-1±ipm|i=1,2,…p}. The result presented in this paper settles a conjecture proposed by Kim et al. in the 2012 IEEE International Symposium on Information Theory Proceedings paper (pp.1014-1018), and also improves their result.

In this letter, an AGV based relay selection mechanism is developed to ensure relays reporting true information in wireless relay networks. The source selects relays based on the channel state information (CSI) of relay-destination links. Selfish relays may report fake CSI in order to obtain a better chance of being selected, whereas the source is not able to tell the reported in real or in false. In the proposed scheme, a relay node receives some payoffs from the destination with respect to the achievable data rate and also some compensations from the others in terms of the reported CSI of all relays. This mechanism not only enforces truth-telling upon relay nodes with maximum payoff but also ensures fairness among them. The equilibrium of payoff is attained when relay nodes report their true CSI. Simulation results demonstrate the theoretical solutions.

Aiming at the different abilities of the defogging algorithms in different fog concentrations, this paper proposes a fog image classification algorithm for a small and imbalanced sample dataset based on a convolution neural network, which can classify the fog images in advance, so as to improve the effect and adaptive ability of image defogging algorithm in fog and haze weather. In order to solve the problems of environmental interference, camera depth of field interference and uneven feature distribution in fog images, the CutBlur-Gauss data augmentation method and focal loss and label smoothing strategies are used to improve the accuracy of classification. It is compared with the machine learning algorithm SVM and classical convolution neural network classification algorithms alexnet, resnet34, resnet50 and resnet101. This algorithm achieves 94.5% classification accuracy on the dataset in this paper, which exceeds other excellent comparison algorithms at present, and achieves the best accuracy. It is proved that the improved algorithm has better classification accuracy.

One key to implementing the smart city is letting the smart space know where and how many people are. The visual method is a scheme to recognize people with high accuracy, but concerns arise regarding potential privacy leakage and user nonacceptance. Besides, being functional in a limited environment in an emergency should also be considered. We propose a real-time people counting and tracking system based on a millimeter wave radar (mmWave) as an alternative to the optical solutions in a restaurant. The proposed method consists of four main procedures. First, capture the point cloud of obstacles and generate them using a low-cost, commercial off-the-shelf (COTS) mmWave radar. Next, cluster the individual point with similar properties. Then the same people in sequential frames would be associated with the tracking algorithm. Finally, the estimated people would be counted, tracked, and shown in the next frame. The experiment results show that our proposed system provided a median position error of 0.17 m and counting accuracy of 83.5% for ten insiders in various scenarios in an actual restaurant environment. In addition, the real-time estimation and visualization of people's numbers and positions show a potential capability to help prevent crowds during the pandemic of Covid-19 and analyze customer visitation patterns for efficient management and target marketing.

With the emergence of a large quantity of data in science and industry, it is urgent to improve the prediction accuracy and reduce the high complexity of Gaussian process regression (GPR). However, the traditional global approximation and local approximation have corresponding shortcomings, such as global approximation tends to ignore local features, and local approximation has the problem of over-fitting. In order to solve these problems, a large-scale Gaussian process regression algorithm (RFFLT) combining random Fourier features (RFF) and local approximation is proposed. 1) In order to speed up the training time, we use the random Fourier feature map input data mapped to the random low-dimensional feature space for processing. The main innovation of the algorithm is to design features by using existing fast linear processing methods, so that the inner product of the transformed data is approximately equal to the inner product in the feature space of the shift invariant kernel specified by the user. 2) The generalized robust Bayesian committee machine (GRBCM) based on Tsallis mutual information method is used in local approximation, which enhances the flexibility of the model and generates a sparse representation of the expert weight distribution compared with previous work. The algorithm RFFLT was tested on six real data sets, which greatly shortened the time of regression prediction and improved the prediction accuracy.

Until today, digital image watermarking has not been large-scale used in the industry. The first reason is that the watermarking efficiency is low and the real-time performance cannot be satisfied. The second reason is that the watermarking scheme cannot cope with various attacks. To solve above problems, this paper presents a multi-domain based digital image watermarking scheme, where a fast DFT (Discrete Fourier Transform) based watermarking method is proposed for synchronization correction and an IWT-DCT (Integer Wavelet Transform-Discrete Cosine Transform) based watermarking method is proposed for information embedding. The proposed scheme has high efficiency during embedding and extraction. Compared with five existing schemes, the robustness of our scheme is very strong and our scheme can cope with many common attacks and compound attacks, and thus can be used in wide application scenarios.

Multi-agent based simulation has been widely used in behavior finance, and several single-processed simulation platforms with Agent-Based Modeling (ABM) have been proposed. However, traditional simulations of stock markets on single processed computers are limited by the computing capability since financial researchers need larger and larger number of agents and more and more rounds to evolve agents' intelligence and get more efficient data. This paper introduces a distributed multi-agent simulation platform, named PSSPAM, for stock market simulation focusing on large scale of parallel agents, communication system and simulation scheduling. A logical architecture for distributed artificial stock market simulation is proposed, containing four loosely coupled modules: agent module, market module, communication system and user interface. With the customizable trading strategies inside, agents are deployed to multiple computing nodes. Agents exchange messages with each other and with the market based on a customizable network topology through a uniform communication system. With a large number of agent threads, the round scheduling strategy is used during the simulation, and a worker pool is applied in the market module. Financial researchers can design their own financial models and run the simulation through the user interface, without caring about the complexity of parallelization and related problems. Two groups of experiments are conducted, one with internal communication between agents and the other without communication between agents, to verify PSSPAM to be compatible with the data from Euronext-NYSE. And the platform shows fair scalability and performance under different parallelism configurations.

Tor is the most popular and well-researched low-latency anonymous communication network provides sender privacy to Internet users. It also provides recipient privacy by making TCP services available through “hidden service”, which allowing users not only to access information anonymously but also to publish information anonymously. However, based on our analysis of the hidden service protocol, we found a special combination of cells, which is the basic transmission unit over Tor, transmitted during the circuit creation procedure that could be used to degrade the anonymity. In this paper, we investigate a novel protocol-feature based attack against Tor's hidden service. The main idea resides in fact that an attacker could monitor traffic and manipulate cells at the client side entry router, and an adversary at the hidden server side could cooperate to reveal the communication relationship. Compared with other existing attacks, our attack reveals the client of a hidden service and does not rely on traffic analysis or watermarking techniques. We manipulate Tor cells at the entry router to generate the protocol-feature. Once our controlled entry onion routers detect such a feature, we can confirm the IP address of the client. We implemented this attack against hidden service and conducted extensive theoretical analysis and experiments over Tor network. The experiment results validate that our attack can achieve high rate of detection rate with low false positive rate.