Hairong Qi

Mobile-agent-based collaborative signal and information processing in sensor networks

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.

Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps

Human action recognition based on the depth information provided by commodity depth sensors is an important yet challenging task. The noisy depth maps, different lengths of action sequences, and free styles in performing actions, may cause large intra-class variations. In this paper, a new framework based on sparse coding and temporal pyramid matching (TPM) is proposed for depth-based human action recognition. Especially, a discriminative class-specific dictionary learning algorithm is proposed for sparse coding. By adding the group sparsity and geometry constraints, features can be well reconstructed by the sub-dictionary belonging to the same class, and the geometry relationships among features are also kept in the calculated coefficients. The proposed approach is evaluated on two benchmark datasets captured by depth cameras. Experimental results show that the proposed algorithm repeatedly achieves superior performance to the state of the art algorithms. Moreover, the proposed dictionary learning method also outperforms classic dictionary learning approaches.

Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution

This paper addresses the problem of learning over-complete dictionaries for the coupled feature spaces, where the learned dictionaries also reflect the relationship between the two spaces. A Bayesian method using a beta process prior is applied to learn the over-complete dictionaries. Compared to previous couple feature spaces dictionary learning algorithms, our algorithm not only provides dictionaries that customized to each feature space, but also adds more consistent and accurate mapping between the two feature spaces. This is due to the unique property of the beta process model that the sparse representation can be decomposed to values and dictionary atom indicators. The proposed algorithm is able to learn sparse representations that correspond to the same dictionary atoms with the same sparsity but different values in coupled feature spaces, thus bringing consistent and accurate mapping between coupled feature spaces. Another advantage of the proposed method is that the number of dictionary atoms and their relative importance may be inferred non-parametrically. We compare the proposed approach to several state-of-the-art dictionary learning methods by applying this method to single image super-resolution. The experimental results show that dictionaries learned by our method produces the best super-resolution results compared to other state-of-the-art methods.

Achieving k-Barrier Coverage in Hybrid Directional Sensor Networks

Barrier coverage is a critical issue in wireless sensor networks for security applications (e.g., border protection) where directional sensors (e.g., cameras) are becoming more popular than omni-directional scalar sensors (e.g., microphones). However, barrier coverage cannot be guaranteed after initial random deployment of sensors, especially for directional sensors with limited sensing angles. In this paper, we study how to efficiently use mobile sensors to achieve \(k\) -barrier coverage. In particular, two problems are studied under two scenarios. First, when only the stationary sensors have been deployed, what is the minimum number of mobile sensors required to form \(k\) -barrier coverage? Second, when both the stationary and mobile sensors have been pre-deployed, what is the maximum number of barriers that could be formed? To solve these problems, we introduce a novel concept of weighted barrier graph (WBG) and prove that determining the minimum number of mobile sensors required to form \(k\) -barrier coverage is related with finding \(k\) vertex-disjoint paths with the minimum total length on the WBG. With this observation, we propose an optimal solution and a greedy solution for each of the two problems. Both analytical and experimental studies demonstrate the effectiveness of the proposed algorithms.

Mobile agent migration modeling and design for target tracking in wireless sensor networks

Computing paradigms play an important and fundamental role in collaborative processing in wireless sensor networks. The client/server based paradigm and the mobile agent based paradigm are two popular computing models used to facilitate collaboration among sensor nodes. In this paper, we study the key problem of determining the mobile agent itinerary for collaborative processing and model the dynamic mobile agent planning problem. We then present three itinerary planning algorithms, the static, the dynamic, and the predictive dynamic approaches to solve the target tracking problem in wireless sensor networks. 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 static itinerary and the dynamic itinerary approaches using the predictive dynamic itinerary algorithm.

Band selection using independent component analysis for hyperspectral image processing

Although hyperspectral images provide abundant information about objects, their high dimensionality also substantially increases computational burden. Dimensionality reduction offers one approach to Hyperspectral Image (HSI) analysis. Currently, there are two methods to reduce the dimension, band selection and feature extraction. In this paper, we present a band selection method based on Independent Component Analysis (ICA). This method, instead of transforming the original hyperspectral images, evaluates the weight matrix to observe how each band contributes to the ICA unmixing procedure. It compares the average absolute weight coefficients of individual spectral bands and selects bands that contain more information. As a significant benefit, the ICA-based band selection retains most physical features of the spectral profiles given only the observations of hyperspectral images. We compare this method with ICA transformation and Principal Component Analysis (PCA) transformation on classification accuracy. The experimental results show that ICA-based band selection is more effective in dimensionality reduction for HSI analysis.

An energy-efficient QoS-aware media access control protocol for wireless sensor networks

We present an innovative MAC protocol (Q-MAC) that minimizes the energy consumption in multi-hop wireless sensor networks (WSNs) and provides quality of service (QoS) by differentiating network services based on priority levels. The priority levels reflect application priority and the state of system resources, namely residual energy and queue occupancies. The Q-MAC utilizes both intra-node and inter-node arbitration. The intra-node packet scheduling is a multiple queuing architecture with packet classification and weighted arbitration. We also introduce the power conservation MACAW (PC-MACAW) - a power-aware scheduling mechanism that, together with the loosely prioritized random access (LPRA) algorithm, govern the inter-node scheduling. Performance evaluation are conducted between Q-MAC and S-MAC with respect to two performance metrics: energy consumption and average latency. Simulation results indicate that the performance of the Q-MAC is comparable to that of the S-MAC in non-prioritized traffic scenarios; when packets with different priorities are present, Q-MAC supiors in average latency differentiation between the classes of service, while maintaining the same energy level as that of S-MAC

A Maximum Entropy Approach to Unsupervised Mixed-Pixel Decomposition

Due to the wide existence of mixed pixels, the derivation of constituent components (endmembers) and their fractional proportions (abundances) at the subpixel scale has been given a lot of attention. The entire process is often referred to as mixed-pixel decomposition or spectral unmixing. Although various algorithms have been proposed to solve this problem, two potential issues still need to be further investigated. First, assuming the endmembers are known, the abundance estimation is commonly performed by employing a least-squares error criterion, which, however, makes the estimation sensitive to noise and outliers. Second, the mathematical intractability of the abundance non-negative constraint results in computationally expensive numerical approaches. In this paper, we propose an unsupervised decomposition method based on the classic maximum entropy principle, termed the gradient descent maximum entropy (GDME), aiming at robust and effective estimates. We address the importance of the maximum entropy principle for mixed-pixel decomposition from a geometric point of view and demonstrate that when the given data present strong noise or when the endmember signatures are close to each other, the proposed method has the potential of providing more accurate estimates than the popular least-squares methods (e.g., fully constrained least squares). We apply the proposed GDME to the subject of unmixing multispectral and hyperspectral data. The experimental results obtained from both simulated and real images show the effectiveness of the proposed method

Binary Tree-based Generic Demosaicking Algorithm for Multispectral Filter Arrays

In this paper, we extend the idea of using mosaicked color filter array (CFA) in color imaging, which has been widely adopted in the digital color camera industry, to the use of multispectral filter array (MSFA) in multispectral imaging. The filter array technique can help reduce the cost, achieve exact registration, and improve the robustness of the imaging system. However, the extension from CFA to MSFA is not straightforward. First, most CFAs only deal with a few bands (3 or 4) within the narrow visual spectral region, while the design of MSFA needs to handle the arrangement of multiple bands (more than 3) across a much wider spectral range. Second, most existing CFA demosaicking algorithms assume the fixed Bayer CFA and are confined to properties only existed in the color domain. Therefore, they cannot be directly applied to multispectral demosaicking. The main challenges faced in multispectral demosaicking is how to design a generic algorithm that can handle the more diversified MSFA patterns, and how to improve performance with a coarser spatial resolution and a less degree of spectral correlation. In this paper, we present a binary tree based generic demosaicking method. Two metrics are used to evaluate the generic algorithm, including the root mean-square error (RMSE) for reconstruction performance and the classification accuracy for target discrimination performance. Experimental results show that the demosaicked images present low RMSE (less than 7) and comparable classification performance as original images. These results support that MSFA technique can be applied to multispectral imaging with unique advantages

Scalable multi-agent system for real-time electric power management

A scalable multi-agent paradigm is presented for control of distributed energy resources to achieve higher reliability, higher power quality, and more efficient (optimum) power generation and consumption. A dynamic hybrid multiagent system is proposed in this paper as a means to achieve scalability for control of a large network of power generation, transmission, load, and compensation sources. Example ancillary agents are developed for system stability and harmonic and reactive current compensation.