Energy-efficient data routing in cooperative UAV swarms for medical assistance after a disaster.

Medical assistance is crucial to disaster management. In particular, the situation of survivors as well as the environmental information after disasters should be collected and sent back to cloud/data centers immediately for further interpretation and analysis. Recently, unmanned aerial vehicle (UAV)-aided disaster management has been considered a promising approach to enhance the efficiency of searching and rescuing survivors after a disaster, in which a group of UAVs collaborates to accomplish the search and rescue task. However, the battery capacity of UAVs is a critical shortcoming that limits their usage. Worse still, the unstable network connectivity of disaster sites could lead to high latency of data transmission from UAV to remote data centers, which could make significant challenges on real-time data collecting and processing. To solve the above problems, in this paper, we investigate an energy-efficient multihop data routing algorithm with the guarantee of quality-of-service for UAV-aided medical assistance. Specifically, we first study the data routing problem to minimize the energy consumption considering transmission rate, time delay, and life cycle of the UAV swarms. Then, we formulate the issue as a mixed-integer nonlinear programming model. Because of the Non-deterministic Polynomial-hardness of this problem, we propose a polynomial time algorithm based on a genetic algorithm to solve the problem. To achieve high efficiency, we further enhance our algorithm based on DBSCAN and adaptive techniques. Extensive experiments show that our approach can outperform the state-of-the-art methods.

Hausdorff Distance Model-Based Identity Authentication for IP Circuits in Service-Centric Internet-of-Things Environment.

Rapid advances in the Internet-of-Things (IoT) have exposed the underlying hardware devices to security threats. As the major component of hardware devices, the integrated circuit (IC) chip also suffers the threat of illegal, malicious attacks. To protect against attacks and vulnerabilities of a chip, a credible authentication is of fundamental importance. In this paper, we propose a Hausdorff distance-based method to authenticate the identity of IC chips in IoT environments, where the structure is analyzed, and the lookup table (LUT) resources are treated as a set of reconfigurable nodes in field programmable gate array (FPGA)-based IC design. Unused LUT resources are selected for insertion of the copyright information by using the depth-first search algorithm, and the random positions are reordered with the Hausdorff distance matching function next, so these positions are mapped to satisfy the specific constraints of the optimal watermark positions. If the authentication process is activated, virtual positions are mapped to the initial key file, yet the identity of the IC designed can be authenticated using the mapping relationship of the Hausdorff distance function. Experimental results show that the proposed method achieves good randomness and secrecy in watermark embedding, as well the extra resource overhead caused by watermarks are promising.

Accelerated Partially Shared Dictionary Learning With Differentiable Scale-Invariant Sparsity for Multi-View Clustering.

Multiview dictionary learning (DL) is attracting attention in multiview clustering due to the efficient feature learning ability. However, most existing multiview DL algorithms are facing problems in fully utilizing consistent and complementary information simultaneously in the multiview data and learning the most precise representation for multiview clustering because of gaps between views. This article proposes an efficient multiview DL algorithm for multiview clustering, which uses the partially shared DL model with a flexible ratio of shared sparse coefficients to excavate both consistency and complementarity in the multiview data. In particular, a differentiable scale-invariant function is used as the sparsity regularizer, which considers the absolute sparsity of coefficients as the l0 norm regularizer but is continuous and differentiable almost everywhere. The corresponding optimization problem is solved by the proximal splitting method with extrapolation technology; moreover, the proximal operator of the differentiable scale-invariant regularizer can be derived. The synthetic experiment results demonstrate that the proposed algorithm can recover the synthetic dictionary well with reasonable convergence time costs. Multiview clustering experiments include six real-world multiview datasets, and the performances show that the proposed algorithm is not sensitive to the regularizer parameter as the other algorithms. Furthermore, an appropriate coefficient sharing ratio can help to exploit consistent information while keeping complementary information from multiview data and thus enhance performances in multiview clustering. In addition, the convergence performances show that the proposed algorithm can obtain the best performances in multiview clustering among compared algorithms and can converge faster than compared multiview algorithms mostly.

Double Sparse Deep Reinforcement Learning via Multilayer Sparse Coding and Nonconvex Regularized Pruning.

Deep reinforcement learning (DRL), which highly depends on the data representation, has shown its potential in many practical decision-making problems. However, the process of acquiring representations in DRL is easily affected by interference from models, and moreover leaves unnecessary parameters, leading to control performance reduction. In this article, we propose a double sparse DRL via multilayer sparse coding and nonconvex regularized pruning. To alleviate interference in DRL, we propose a multilayer sparse-coding-structural network to obtain deep sparse representation for control in reinforcement learning. Furthermore, we employ a nonconvex log regularizer to promote strong sparsity, efficiently removing the unnecessary weights with a regularizer-based pruning scheme. Hence, a double sparse DRL algorithm is developed, which can not only learn deep sparse representation to reduce the interference but also remove redundant weights while keeping the robust performance. The experimental results in five benchmark environments of the deep q network (DQN) architecture demonstrate that the proposed method with deep sparse representations from the multilayer sparse-coding structure can outperform existing sparse-coding-based DRL in control, for example, completing Mountain Car with 140.81 steps, achieving near 10% reward increase from the single-layer sparse-coding DRL algorithm, and obtaining 286.08 scores in Catcher, which are over two times the rewards of the other algorithms. Moreover, the proposed algorithm can reduce over 80% parameters while keeping performance improvements from deep sparse representations.

Homophily Preserving Community Detection.

As a fundamental problem in social network analysis, community detection has recently attracted wide attention, accompanied by the output of numerous community detection methods. However, most existing methods are developed by only exploiting link topology, without taking node homophily (i.e., node similarity) into consideration. Thus, much useful information that can be utilized to improve the quality of detected communities is ignored. To overcome this limitation, we propose a new community detection approach based on nonnegative matrix factorization (NMF), namely, homophily preserving NMF (HPNMF), which models not only link topology but also node homophily of networks. As such, HPNMF is able to better reflect the inherent properties of community structure. In order to capture node homophily from scratch, we provide three similarity measurements that naturally reveal the association relationships between nodes. We further present an efficient learning algorithm with convergence guarantee to solve the proposed model. Finally, extensive experiments are conducted, and the results demonstrate that HPNMF has strong ability to outperform the state-of-the-art baseline methods.

Identification of weak peaks in X-ray fluorescence spectrum analysis based on the hybrid algorithm combining genetic and Levenberg Marquardt algorithm.

Accurate measurement of cadmium content in rice is of utmost importance to determine if the inspected rice product is safe to people. X-ray fluorescence analysis is frequently used for multi-element analysis because it has characteristics of fast, accurate and nondestructive. However, due to the low content of cadmium in rice, its corresponding characteristics energy peak is relatively weak and is sensitive to the background information in the X-ray energy spectrum. Thus, it is very tough to obtain the accurate values of cadmium content by utilizing traditional X-ray fluorescence analysis. In this paper, the identification of weak peaks of cadmium is much improved by proposing a hybrid algorithm combining genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM). The hybrid algorithm not only takes full advantages of GA and LM respectively but also inhibits their unwanted properties: poor local search ability of GA and locally convergent of LM. The proposed hybrid algorithm is employed to identify weak peaks in X-ray spectra of six contaminated rice samples with different contents of cadmium. Two comparative experiments are conducted to compare the performance between GA, LM and the proposed hybrid algorithm. One of the comparative experiments has the relative error varying with the number of calculations, which aims to verify the accuracy and stability. The results show that the hybrid algorithm is a better option in terms of accuracy and stability. Another comparative experiment of which the average relative error varies with the number of iterations is conducted to verify the computing efficiency. The experiments show that the hybrid algorithm exhibits a faster convergence rate. Two numerical experiments demonstrate that the proposed algorithm can well resolve the identification issue of the cadmium in the X-ray spectra and significantly improve the content measurement accuracy of cadmium in the quality evaluation experiment of rice products.

Manifold optimization-based analysis dictionary learning with an ℓ1∕2-norm regularizer.

Recently there has been increasing attention towards analysis dictionary learning. In analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting solutions efficiently while simultaneously avoiding the trivial solutions of the dictionary. In this paper, to obtain the strong sparsity-promoting solutions, we employ the ℓ1∕2 norm as a regularizer. The very recent study on ℓ1∕2 norm regularization theory in compressive sensing shows that its solutions can give sparser results than using the ℓ1 norm. We transform a complex nonconvex optimization into a number of one-dimensional minimization problems. Then the closed-form solutions can be obtained efficiently. To avoid trivial solutions, we apply manifold optimization to update the dictionary directly on the manifold satisfying the orthonormality constraint, so that the dictionary can avoid the trivial solutions well while simultaneously capturing the intrinsic properties of the dictionary. The experiments with synthetic and real-world data verify that the proposed algorithm for analysis dictionary learning can not only obtain strong sparsity-promoting solutions efficiently, but also learn more accurate dictionary in terms of dictionary recovery and image processing than the state-of-the-art algorithms.