In next generation mobile multimedia communications, different wireless access networks are expected to cooperate. However, it is a challenging task to choose an optimal transmission path in this scenario. This paper focuses on the problem of selecting the optimal access network for multicast services in the cooperative mobile and broadcasting networks. An algorithm is proposed, which considers multiple decision factors and multiple optimization objectives. An analytic hierarchy process (AHP) method is applied to schedule the service queue and an artificial neural network (ANN) is used to improve the flexibility of the algorithm. Simulation results show that by applying the AHP method, a group of weight ratios can be obtained to improve the performance of multiple objectives. And ANN method is effective to adaptively adjust weight ratios when users' new waiting threshold is generated.

Presented is a new measuring and reconstruction framework of Compressed Sensing (CS), aiming at reducing the measurements required to ensure faithful reconstruction. A sparse vector is segmented into sparser vectors. These new ones are then randomly sensed. For recovery, we reconstruct these vectors individually and assemble them to obtain the original signal. We show that the proposed scheme, referred to as SegOMP, yields higher probability of exact recovery in theory. It is finished with much smaller number of measurements to achieve a same reconstruction quality when compared to the canonical greedy algorithms. Extensive experiments verify the validity of the SegOMP and demonstrate its potentials.

An ECL circuit with an active pull-down device, operated from a CMOS supply voltage, is described as a high-speed digital circuit for a 0.25-µm BiCMOS technology. A pair of ECL/CMOS level converters with build-in logic capability is presented for effective intermixing of ECL with CMOS circuits. Using a 2.5-V supply and a reduced-swing BiNMOS buffer, the ECL circuit has reduced power dissipation, while still providing good speed. A design example shows the implementation of complex logic by emitter and collector dottings and the selective use of ECL circuits to achieve high performance.

This study proposes a novel machine learning architecture and various learning algorithms to build-in anti-phishing services for avoiding cyber-phishing attack. For the rapid develop of information technology, hackers engage in cyber-phishing attack to steal important personal information, which draws information security concerns. The prevention of phishing website involves in various aspect, for example, user training, public awareness, fraudulent phishing, etc. However, recent phishing research has mainly focused on preventing fraudulent phishing and relied on manual identification that is inefficient for real-time detection systems. In this study, we used methods such as ANOVA, X2, and information gain to evaluate features. Then, we filtered out the unrelated features and obtained the top 28 most related features as the features to use for the training and evaluation of traditional machine learning algorithms, such as Support Vector Machine (SVM) with linear or rbf kernels, Logistic Regression (LR), Decision tree, and K-Nearest Neighbor (KNN). This research also evaluated the above algorithms with the ensemble learning concept by combining multiple classifiers, such as Adaboost, bagging, and voting. Finally, the eXtreme Gradient Boosting (XGBoost) model exhibited the best performance of 99.2%, among the algorithms considered in this study.

In this letter, we propose a novel robust transferable subspace learning (RTSL) method for cross-corpus facial expression recognition. In this method, on one hand, we present a novel distance metric algorithm, which jointly considers the local and global distance distribution measure, to reduce the cross-corpus mismatch. On the other hand, we design a label guidance strategy to improve the discriminate ability of subspace. Thus, the RTSL is much more robust to the cross-corpus recognition problem than traditional transfer learning methods. We conduct extensive experiments on several facial expression corpora to evaluate the recognition performance of RTSL. The results demonstrate the superiority of the proposed method over some state-of-the-art methods.

To provide cellular data services with differentiated QoS, a shared resource scheme, based on the optimization theory and LaGrange λ-calculus was developed. This scheme can generate a fair schedule for a diverse mix of traffic with diverse QoS requirements in a limited radio spectrum. We define the acceptance indication, AI, as the QoS measurement for the shared resource scheme. The experimental results show that this approach outperforms other existing schemes.

Touch screen has become the mainstream manipulation technique on handheld devices. However, its innate limitations, e.g. the occlusion problem and fat finger problem, lower user experience in many use scenarios on handheld displays. Back-of-device interaction, which makes use of input units on the rear of a device for interaction, is one of the most promising approaches to address the above problems. In this paper, we present the findings of a user study in which we explored users' pointing performances in using two types of touch input on handheld devices. The results indicate that front-of-device touch input is averagely about two times as fast as back-of-device touch input but with higher error rates especially in acquiring the narrower targets. Based on the results of our study, we argue that in the premise of keeping the functionalities and layouts of current mainstream user interfaces back-of-device touch input should be treated as a supplement to front-of-device touch input rather than a replacement.

An improved multivariate wavelet denoising algorithm combined with subspace and principal component analysis is presented in this paper. The key element is deriving an optimal orthogonal matrix that can project the multivariate observation signal to a signal subspace from observation space. Univariate wavelet shrinkage operator is then applied to the projected signals channel-wise resulting in the improvement of the output SNR. Finally, principal component analysis is performed on the denoised signal in the observation space to further improve the denoising performance. Experimental results based on synthesized and real world ECG data verify the effectiveness of the proposed algorithm.

A novel method is proposed to track the position of MS in the mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions in cellular network. A first-order markov model is employed to describe the dynamic transition of LOS/NLOS conditions, which is hidden in the measurement data. This method firstly uses modified EKF banks to jointly estimate both mobile state (position and velocity) and the hidden sight state based on the the data collected by a single BS. A Bayesian data fusion algorithm is then applied to achieve a high estimation accuracy. Simulation results show that the location errors of the proposed method are all significantly smaller than that of the FCC requirement in different LOS/NLOS conditions. In addition, the method is robust in the parameter mismodeling test. Complexity experiments suggest that it supports real-time application. Moreover, this algorithm is flexible enough to support different types of measurement methods and asynchronous or synchronous observations data, which is especially suitable for the future cooperative location systems.

Communication-free Petri nets provide a net semantics for Basic Parallel Processes, which form a subclass of Milner's Calculus of Communicating Systems (CCS) a process calculus for the description and algebraic manipulation of concurrent communicating systems. It is known that the reachability problem for communication-free Petri nets is NP-complete. Lacking the synchronization mechanism, the expressive power of communication-free Petri nets is somewhat limited. It is therefore importance to see whether the power of communication-free Petri nets can be enhanced without sacrificing their analytical capabilities. As a first step towards this line of research, in this paper our main concern is to investigate, from the decidability/complexity viewpoint, the reachability problem for a number of variants of communication-free Petri nets, including communication-free Petri nets augmented with 'static priorities,' 'dynamic priorities,' 'states,' 'inhibitor arcs,' and 'timing constraints.'

Currently, the automatic image inpainting methods emphasize the inpainting techniques either globally or locally. They didn't consider the merits of global and local techniques to compensate each other. On the contrary, the artists fixed an image in global view firstly, and then focus on the local features of it, when they repaired it. This paper proposes a progressive processing of image inpainting method based on multi-resolution analysis. In damaged and defective area, we imitate the artistic techniques to approach the effectiveness of image inpainting in human vision. First, we use the multi-resolution characteristics of wavelet transform, from the lowest spatial-frequency layer to the higher one, to analyze the image from global-area to local-area progressively. Then, we utilize the variance of the energy of wavelet coefficients within each image block, to decide the priority of inpainting blocks. Finally, we extract the multi-resolution features of each block. We take account of the correlation among horizontal, vertical and diagonal directions, to determine the inpainting strategy for filling image pixels and approximate a high-quality image inpainting to human vision. In our experiments, the performance of the proposed method is superior to the existing methods.

Aiming at the contradiction between exploration and exploitation in deep reinforcement learning, this paper proposes “reward-based exploration strategy combined with Softmax action selection” (RBE-Softmax) as a dynamic exploration strategy to guide the agent to learn. The superiority of the proposed method is that the characteristic of agent's learning process is utilized to adapt exploration parameters online, and the agent is able to select potential optimal action more effectively. The proposed method is evaluated in discrete and continuous control tasks on OpenAI Gym, and the empirical evaluation results show that RBE-Softmax method leads to statistically-significant improvement in the performance of deep reinforcement learning algorithms.

As an important method to solve sequential decision-making problems, reinforcement learning learns the policy of tasks through the interaction with environment. But it has difficulties scaling to large-scale problems. One of the reasons is the exploration and exploitation dilemma which may lead to inefficient learning. We present an approach that addresses this shortcoming by introducing qualitative knowledge into reinforcement learning using cloud control systems to represent ‘if-then’ rules. We use it as the heuristics exploration strategy to guide the action selection in deep reinforcement learning. Empirical evaluation results show that our approach can make significant improvement in the learning process.

Nowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on cold-start users.

Operations, such as text entry and zooming, are simple and frequently used on mobile touch devices. However, these operations are far from being perfectly supported. In this paper, we present our prototype, BackAssist, which takes advantage of back-of-device input to augment front-of-device touch interaction. Furthermore, we present the results of a user study to evaluate whether users can master the back-of-device control of BackAssist or not. The results show that the back-of-device control can be easily grasped and used by ordinary smart phone users. Finally, we present two BackAssist supported applications - a virtual keyboard application and a map application. Users who tried out the two applications give positive feedback to the BackAssist supported augmentation.

This CMOS transceiver IC exploits the superheterodyne architecture to implement a low-cost RF front-end with only 6.25 mm2 die area for IEEE 802.11b standard. The transceiver is implemented in 0.25 µm CMOS process with 2.7 V supply voltage, and achieves a -86 dBm 11 Mb/s receive sensitivity and a 2 dBm transmit output power.

This paper proposes an effective continuous super-resolution (CSR) algorithm for the multipath channel estimation. By designing a preamble including up-chirp and down-chirp symbols, the Doppler shift and multipath delay are estimated jointly by using convex programming. Simulation results show that the proposed CSR can achieve better detection probability of the number of multipaths than the eigenvalue based methods. Moreover, compared with conventional super-resolution techniques, such as MUSIC and ESPRIT methods, the proposed CSR algorithm demonstrates its advantage in root mean square error of the Doppler shift and multipath delay, especially for the closely located paths within low SNR.

This paper proposes a novel image inpainting method based on bandelet transform. This technique is based on a multi-resolution layer to perform image restoration, and mainly utilizes the geometrical flow of the neighboring texture of the damaged regions as the basis of restoration. By performing the warp transform with geometrical flows, it transforms the textural variation into the nearing domain axis utilizing the bandelet decomposition method to decompose the non-relative textures into different bands, and then combines them with the affine search method to perform image restoration. The experimental results show that the proposed method can simplify the complexity of the repair decision method and improve the quality of HVS, and thus, repaired results to contain the image of contour of high change, and in addition, offer a texture image of high-frequency variation. These repair results can lead to state-of-the-art results.

The commonly used Deep Q Networks is known to overestimate action values under certain conditions. It's also proved that overestimations do harm to performance, which might cause instability and divergence of learning. In this paper, we present the Deep Sarsa and Q Networks (DSQN) algorithm, which can considered as an enhancement to the Deep Q Networks algorithm. First, DSQN algorithm takes advantage of the experience replay and target network techniques in Deep Q Networks to improve the stability of neural networks. Second, double estimator is utilized for Q-learning to reduce overestimations. Especially, we introduce Sarsa learning to Deep Q Networks for removing overestimations further. Finally, DSQN algorithm is evaluated on cart-pole balancing, mountain car and lunarlander control task from the OpenAI Gym. The empirical evaluation results show that the proposed method leads to reduced overestimations, more stable learning process and improved performance.

Input devices based on direct touch have replaced traditional ones and become the mainstream interactive technology for handheld devices. Although direct touch interaction proves to be easy to use, its problems, e.g. the occlusion problem and the fat finger problem, lower user experience. Camera-based mobile interaction is one of the solutions to overcome the problems. There are two typical interaction styles to generate camera-based pointing interaction for handheld devices: move the device or move an object before the camera. In the first interaction style, there are two approaches to move a cursor's position across the handheld display: move it towards the same direction or the opposite direction which the device moves to. In this paper, the results of a comparison research, which compared the pointing performances of three camera-based pointing techniques, are presented. All pointing techniques utilized input from the rear-facing camera. The results indicate that the interaction style of moving a finger before the camera outperforms the other one in efficiency, accuracy, and throughput. The results also indicate that within the interaction style of moving the device, the cursor positioning style of moving the cursor to the opposite direction is slightly better than the other one in efficiency and throughput. Based on the findings, we suggest giving priority to the interaction style of moving a finger when deploying camera-based pointing techniques on handheld devices. Given that the interaction style of moving the device supports one-handed manipulation, it also worth deploying when one-handed interaction is needed. According to the results, the cursor positioning style of moving the cursor towards the opposite direction which the device moves to may be a better choice.