Facial Micro-expression Recognition (MER) distinguishes the underlying emotional states of spontaneous subtle facial expressions. Automatic MER is challenging because that the intensity of subtle facial muscle movement is extremely low and the duration of ME is transient.Recent works adopt motion magnification or temporal interpolation to resolve these issues. Nevertheless, existing works divide them into two separate modules due to their non-linearity. Though such operation eases the difficulty in implementation, it ignores their underlying connections and thus results in inevitable losses in both accuracy and speed. Instead, in this paper, we propose a consolidated Eulerian framework to reveal the subtle facial movements. It expands the temporal duration and amplifies the muscle movements in micro-expressions simultaneously. Compared to existing approaches, the proposed method can not only process ME clips more efficiently but also make subtle ME movements more distinguishable. Experiments on two public MER databases indicate that our model outperforms the state-of-the-art in both speed and accuracy.

Recent saliency models extensively explore to incorporate multi-scale contextual information from Convolutional Neural Networks (CNNs). Besides direct fusion strategies, many approaches introduce message-passing to enhance CNN features or predictions. However, the messages are mainly transmitted in two ways, by feature-to-feature passing, and by prediction-to-prediction passing. In this paper, we add message-passing between features and predictions and propose a deep unified CRF saliency model . We design a novel cascade CRFs architecture with CNN to jointly refine deep features and predictions at each scale and progressively compute a final refined saliency map. We formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-prediction, from the coarse scale to the finer scale, to update the features and the corresponding predictions. Also, we formulate the mean-field updates for joint end-to-end model training with CNN through back propagation. The proposed deep unified CRF saliency model is evaluated over six datasets and shows highly competitive performance among the state of the arts.

Saliency integration has attracted much attention on unifying saliency maps from multiple saliency models. Previous offline integration methods usually face two challenges: 1) if most of the candidate saliency models misjudge the saliency on an image, the integration result will lean heavily on those inferior candidate models; and 2) an unawareness of the ground truth saliency labels brings difficulty in estimating the expertise of each candidate model. To address these problems, in this paper, we propose an arbitrator model (AM) for saliency integration. First, we incorporate the consensus of multiple saliency models and the external knowledge into a reference map to effectively rectify the misleading by candidate models. Second, our quest for ways of estimating the expertise of the saliency models without ground truth labels gives rise to two distinct online model-expertise estimation methods. Finally, we derive a Bayesian integration framework to reconcile the saliency models of varying expertise and the reference map. To extensively evaluate the proposed AM model, we test 27 state-of-the-art saliency models, covering both traditional and deep learning ones, on various combinations over four datasets. The evaluation results show that the AM model improves the performance substantially compared to the existing state-of-the-art integration methods, regardless of the chosen candidate saliency models.

Sparse coding has achieved a great success in various image processing studies. However, there is not any benchmark to measure the sparsity of image patch/group because sparse discriminant conditions cannot keep unchanged. This paper analyzes the sparsity of group based on the strategy of the rank minimization. Firstly, an adaptive dictionary for each group is designed. Then, we prove that group-based sparse coding is equivalent to the rank minimization problem, and thus the sparse coefficients of each group are measured by estimating the singular values of each group. Based on that measurement, the weighted Schatten p-norm minimization (WSNM) has been found to be the closest solution to the real singular values of each group. Thus, WSNM can be equivalently transformed into a non-convex ℓp-norm minimization problem in group-based sparse coding. Experimental results on two applications: image in painting and image compressive sensing (CS) recovery show that the proposed scheme outperforms many state-of-the-art methods.

With the growing concern over energy consumption, green communication is becoming more and more important. Lots of efforts have been put into investigating in energy-efficient wireless systems. Since Fractional Frequency Reuse (FFR) is a widely-used Inter-cell-interference coordination scheme in the OFDMA wireless network, we consider the energy-efficient power control in the FFR system. With demanded user rate constraints, we aim at minimizing the Joule consumed per transmission bit per-cell in the downlink. Based on Dinkelbach's theory, we transform the objective function into a parametric programming problem. By proving the problem is convex, we derive the optimal analytical solution by Karush-Kuhn-Tucker (KKT) conditions, along with an energy-efficient power control algorithm. Numerical results are given to verify our analysis, and demonstrate that the power control algorithm significantly improves the energy efficiency in FFR. The optimal frequency reuse factor is also given when considering the fairness of energy efficiency and power consumption.

Frequency reuse (FR) has been widely deployed to achieve increased throughput and interference mitigation. Different FR schemes can prove to be efficient under different conditions and parameters. This paper formulates a generic mathematical model which can be used to obtain an FR scheme that can be tailored according to our requirements. The paper proposes a fuzzy set theory based generic mathematical model for deriving various Soft Fractional FR (SFFR) schemes. The derived schemes are evaluated using various parameters such as average throughput, spectral and power efficiency. To the best of our knowledge, the use of fuzzy set theory for deriving different SFFR schemes is the first effort that can be found in the literature. The analysis provided in the paper discovers an optimal SFFR scheme which provides higher spectral efficiency and throughput. Our proposed scheme, at FR =1.35, improves the power efficiency (8.68 Watts/(bps/Hz)) by staggering 21% and 57% as compared to FR=1 (11 Watts/(bps/Hz)) and FR=3 (20 Watts/(bps/Hz)), respectively.

In this paper, we present a multi-layered, middleware-driven, multi-agent, interoperable architecture for distributed sensor networks, that bridges the gap between the programmable application layer consisting of software agents and the physical layer consisting of extremely small, low power devices that combine programmable computing with sensing, tracking and wireless communication capabilities. We develop an energy-efficient approach for collaborative processing among multiple sensor nodes using a mobile-agent-based computing model. Unlike the traditional client/server-based model where each sensor node sends data to the sink, in this model the sink/base-station deploys mobile agents that migrate from node to node following a certain itinerary and fuses the information/data locally at each node. This way, the intelligence is distributed throughout the network edge and communication cost is reduced to make the sensor network energy-efficient. Simulation results show that the mobile-agent-based approach is advantageous for large distributed sensor network environment

Distributed computing paradigm plays an important and fundamental role in facilitating collaboration among sensor nodes. The mobile agent computing paradigm has many benefits in the context of sensor networks. However, the most challenging problem in mobile agent based computing, the design of mobile agent itinerary, is left unanswered. An improper design of itinerary (or route) of mobile agent migration can largely deteriorate the performance of collaborative processing. In this paper, we study the key problem of deriving mobile agent itinerary for collaborative processing, especially the dynamic mobile agent planning since it is more suitable for the wireless sensor networks. This paper presents two dynamic itinerary planning algorithms, the dynamic and the predictive dynamic approaches. We design three metrics (energy consumption, network lifetime, and the number of hops ) and use simulation tools to quantitatively measure the performance of different itinerary planning algorithms. Simulation results show considerable improvement over the dynamic itinerary approach using the predictive dynamic itinerary algorithm

In sensor networks, collaborative processing between multiple sensor nodes is essential in order to complement for each others sensing capability, tolerate faults, and provide reliable information. The client/server-based paradigm is typical for distributed processing. However, it is not the most efficient in the context of sensor networks. In this paper, we present a mobile agent-based paradigm to carry out collaborative processing, where instead of each sensor node sending local information to a processing center, as is typical in the client/server-based computing, the processing code is moved to the sensor nodes through mobile agents. This approach has great potential in providing energy-efficient and scalable collaborative processing with low latency. We design two metrics (execution time and energy consumption) and use simulation tools to quantitatively measure the performance of different computing models in collaborative processing. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this result, we develop a cluster-based hybrid computing paradigm to combine the advantages of both paradigms. We analyze two different scenarios in hybrid computing and simulation results show that there is always one scenario that performs better than either the client/server- or mobile-agent-based paradigm.

In this paper, we develop an energy-efficient, fault-tolerant approach for collaborative signal and information processing (CSIP) among multiple sensor nodes using a mobile-agent-based computing model. In this model, instead of each sensor node sending local information to a processing center for integration, as is typical in client/server-based computing, the integration code is moved to the sensor nodes through mobile agents. The energy efficiency objective and the fault tolerance objective always conflict with each other and present unique challenge to the design of CSIP algorithms. In general, energy-efficient approaches try to limit the redundancy in the algorithm so that minimum amount of energy is required for fulfilling a certain task. On the other hand, redundancy is needed for providing fault tolerance since sensors might be faulty, malfunctioning, or even malicious. A balance has to be struck between these two objectives. We discuss the potential of mobile-agent-based collaborative processing in providing progressive accuracy while maintaining certain degree of fault tolerance. We evaluate its performance compared to the client/server-based collaboration from perspectives of energy consumption and execution time through both simulation and analytical study. Finally, we take collaborative target classification as an application example to show the effectiveness of the proposed approach.

The emergence of mobile ad hoc sensor networks has brought new challenges to traditional network design. This paper compares the performance of two distributed computing paradigms, the client/server-based paradigm and the mobile-agent-based paradigm, through mathematical modeling and simulation. Previous works have shown that the mobile-agent-based paradigm is more appropriate to handle computations in ad hoc sensor networks. However, no simulation work has been done to quantitatively measure the performance. This paper first describes how computing is accomplished in the mobile-agent-based paradigm. It then presents a modified mathematical model and uses the execution time as a metric to measure the performance. Eight experiments are designed to show the effect of different parameters to the performance of the paradigms. Experimental results show that in the context of mobile ad hoc sensor networks with hundreds or even thousands of nodes, unreliable communication links and reduced bandwidth, the mobile-agent-based computing provides solutions to low network latency and reliable data processing.