14606163
Gene intrusion on a society of Hill-Climbers
In this paper, a new algorithm of Gene Intrusion on a Society of Hill-Climbers (GI-SoHC) is introduced. In GI-SoHC, the genes which contribute most to the best fitness of the best climber in each generation are selected and inserted to other climbers for replacing their own genes, such that the society can have faster climbing speed and better climbing locations.The experiment results show that with Gene Intrusion, climbers generally and averagely perform better in that they climb more quickly than the original Historical Society of Hill-Climbers, especially in the early stage of the climbing process.
17397024
Multi-level secure video streaming over SRTP
In this paper, we propose a set of solutions to four different levels on data selection for encrypting Motion JPEG video frames over SRTP. These solutions are based on our intensive analysis of the JPEG image compression procedure. We select the most important data which contains most information of an image for encryption; in consequence provide a set of balances between security and computation for different hardware environments. Extensive experiments are conducted and performance data has been analyzed to evaluate the proposed solutions. Results show that these solutions are well suited in different scenarios with various computation power and security requirements.
16274066
Density-based multipath secure communication over mobile ad hoc networks
Security is a critical issue in mobile ad hoc networks (MANETs), where mobile nodes communicate with each other over relatively unreliable wireless links with no fixed infrastructure. One example of this occurs on battle fields, where soldiers often wear cameras and wireless mobile devices to send or receive images/video of different angles or locations in order to obtain better observation of the area. Such an environment could be highly insecure since an enemy could place devices to eavesdrop on or compromise the soldier nodes. In this paper, we consider a case where time sensitive multimedia is sent using multiple paths which can make better use of load balancing strategies, increase bandwidth and save node energy. However, an enemy node could listen in on more than one path at the same time, allowing it to collect sufficient shares of an image or video frame to recover the image. Our proposed algorithm is based on a quantitative analysis of security to protect against such multi-path eavesdropping. We prove that our algorithm can achieve better level of security by detouring traffic via paths without or with less vulnerable areas at the same amount of redundancy. On the other hand, we also prove, using simulation results and mathematical derivation, our algorithm can provide much more redundancy for better multimedia quality purpose while at the same time guaranteeing the same level of security.
37631893
Stereo Vision Based Floor Plane Extraction and Camera Pose Estimation
In this paper, we present a unified approach for floor plane extraction and camera pose estimation. A histogram based method is performed on the reconstructed 3-D points obtained by an onboard video-rate stereo vision system to extract candidate points of the floor plane and determine the pose of vision system simultaneously. The obstacle area is easily localized given the floor plane region. In order to improve reliability and accuracy of camera pose estimation results, the Least Median of Squares (LMedS) based fitting method is applied to estimate the floor plane parameters with the extracted candidate points. The precise pose of the onboard stereo vision system is directly acquired related to the floor plane parameters. Experimental results in real indoor environments are discussed and show the good performance.
14090386
In-network Outlier Cleaning for Data Collection in Sensor Networks
Outliers are very common in the environmental data monitored by a sensor network consisting of many inexpensive, low fidelity, and frequently failed sensors. The limited battery power and costly data transmission have introduced a new challenge for outlier cleaning in sensor networks: it must be done innetwork to avoid spending energy on transmitting outliers. In this paper, we propose an in-network outlier cleaning approach, including wavelet based outlier correction and neighboring DTW(Dynamic Time Warping) distance-based outlier removal. The cleaning process is accomplished during multi-hop data forwarding process, and makes use of the neighboring relation in the hop-count based routing algorithm. Our approach guarantees that most of the outliers can be either corrected, or removed from further transmission within 2 hops. We have simulated a spatialtemporal correlated environmental area, and evaluated the outlier cleaning approach in it. The results show that our approach can effectively clean the sensing data and reduce outlier traffic.
2155976
Maximal Subspace Coregulated Gene Clustering
Clustering is a popular technique for analyzing microarray data sets, with n genes and m experimental conditions. As explored by biologists, there is a real need to identify coregulated gene clusters, which include both positive and negative regulated gene clusters. The existing pattern-based and tendency-based clustering approaches cannot directly be applied to find such coregulated gene clusters, because they are designed for finding positive regulated gene clusters. In this paper, in order to cluster coregulated genes, we propose a coding scheme that allows us to cluster two genes into the same cluster if they have the same code, where two genes that have the same code can be either positive or negative regulated. Based on the coding scheme, we propose a new algorithm for finding maximal subspace coregulated gene clusters with new pruning techniques. A maximal subspace coregulated gene cluster clusters a set of genes on a condition sequence such that the cluster is not included in any other subspace coregulated gene clusters. We conduct extensive experimental studies. Our approach can effectively and efficiently find maximal subspace coregulated gene clusters. In addition, our approach outperforms the existing approaches for finding positive regulated gene clusters.
6331812
Fine-Grained Power Gating Based on the Controlling Value of Logic Elements
Leakage power consumption of logic elements has become a serious problem, especially in the sub-100-nanometer process. In this paper, a novel power gating approach by using the controlling value of logic elements is proposed. In the proposed method, sleep signals of the power-gated blocks are extracted completely from the original circuits without any extra logic element. A basic algorithm and a probability-based heuristic algorithm have been developed to implement the basic idea. The steady maximum delay constraint has also been introduced to handle the delay issues. Experiments on the ISCAS'85 benchmarks show that averagely 15-36% of logic elements could be power gated at a time for random input patterns, and 3-31% of elements could be stopped under the steady maximum delay constraints. We also show a power optimization method for AND/OR tree circuits, in which more than 80% of gates can be power-gated.
2880552
Mixed Type Audio Classification with Support Vector Machine
Content-based classification of audio data is an important problem for various applications such as overall analysis of audio-visual streams, boundary detection of video story segment, extraction of speech segments from video, and content-based video retrieval. Though the classification of audio into single type such as music, speech, environmental sound and silence is well studied, classification of mixed type audio data, such as clips having speech with music as background, is still considered a difficult problem. In this paper, we present a mixed type audio classification system based on support vector machine (SVM). In order to capture characteristics of different types of audio data, besides selecting audio features, we also design four different representation formats for each feature. Our SVM-based audio classifier can classify audio data into five types: music, speech, environment sound, speech mixed with music, and music mixed with environment sound. The experimental results show that our system outperforms other classification systems using k nearest neighbor (k-NN), neural network (NN), and Naive Bayes (NB)