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The primary objective of this paper is to explore the applicability of sparse representation based classification (SRC), particularly at the fingerprint recognition problem. This paper proposes sparse proximity based fingerprint matching methodology. The sparse representation based classification problem can be solved as representing the test sample in terms of training set with some sparse residual...
With the advent of QR readers and mobile phones the use of graphical codes like QR codes and data matrix code has become very popular. Despite the noise like appearance, it has the advantage of high data capacity, damage resistance and fast decoding robustness. The proposed system embeds the image chosen by the user to develop visually appealing QR codes with improved decoding robustness using BCH...
We address domain adaptation in the context of clustering where we are given a set of unlabeled data, coming from several domains, and the goal is to group data into different categories regardless of the domain they come from. This is a challenging problem since we do not have any supervision unlike most adaptation scenarios studied earlier, and is very relevant in practical industry applications...
PoC is a group communication service using VoIP technology based on 3G cellular network packet domain carrier. ROHC can complete the header compression in PS domain of 3G network, which can improve the efficiency of wireless transmission. This paper analyzes the working principle and the operation modes of ROHC, designs the processing of the compressor and decompressor. Simultaneity an improved algorithm...
A new design method for PID controller using user-specified gain margin and phase margin for delayed systems has been proposed. The proposed PID controller is particularly useful in auto-tuning. The controller parameters are calculated using several approximations and elimination processes. The robustness of the proposed controller is verified by different simulation examples.
In this paper, we propose a robust descriptor named as multiple gradient-related features (MGRF) in virtue of local and overall order encoding. Specifically, three types of features are introduced, including multidirectional gradient, gradient orientation, and first derivative of gradient orientation, each of which represents different aspect of region of interest (ROI). To extract these features,...
Developing cross-corpus, cross-domain, and cross-language emotion recognition algorithm has becoming more prevalent recently to ensure the wide applicability of robust emotion recognizer. In this work, we propose a computational framework on fusing multiple emotion perspectives by integrating cross-lingual emotion information. By assuming that each data is ‘perceived’ not only by a main perspective...
Face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poorly on such images. In this paper we present a method for face recognition adapted to real-world conditions that can be trained using very few training...
This paper considers communication over the point-to-point Gaussian channel with stationary and ergodic channel gain and full channel state information. The capacity of this channel can be achieved in a straightforward manner via any capacity-achieving codebook for the non-fading Gaussian channel, in conjunction with separable coding, i.e., coding independently over each fading state. Despite its...
LSB substitution steganography only takes the least significant bits in the carrier into account, which has the problems of low security and poor robustness. This paper proposes a self-contained steganography combining the MSB matching and LSB substitution. It contains two types of encoding rules to define the matching result between the secret information binary stream and the most two-significant-bit...
The brain-inspired spiking neurons are more biologically plausible and computationally powerful than traditional rate-based artificial neurons. Most of the existing spiking neuron based applications assume noise-free condition for learning and testing. However, noise widely exists in the network of spiking neurons and the neural response can be significantly disturbed by different types of noise....
LSB techniques generally embed data in the same LSB position of consecutive samples which helps intruders to extract secret information easily. This paper solve this problem by introducing a robust audio steganography technique where data is embedded in multiple layers of LSB chosen randomly and in non-consecutive samples. The choice of random LSB layers and non-consecutive pixels for embedding increases...
Road detection from images is a challenging task in computer vision. Previous methods are not robust, because their features and classifiers cannot adapt to different circumstances. To overcome this problem, we propose to apply unsupervised feature learning for road detection. Specifically, we develop an improved encoding function and add a feature selection process to obtain robust and discriminative...
Big data workflows often require the assembly and exchange of complex, multi-element datasets. For example, in biomedical applications, the input to an analytic pipeline can be a dataset consisting thousands of images and genome sequences assembled from diverse repositories, requiring a description of the contents of the dataset in a concise and unambiguous form. Typical approaches to creating datasets...
The presented work proposes a simple feature extraction technique which is designed for robust detection of event related potentials (ERP). This technique was tested to detect the N400 which is an ERP generally associated with recall. The chief advantages of the proposed technique are that it is robust to different ocular artifacts and yet sensitive to event related potentials. Further each signal...
Motivated by real applications, heterogeneous learning has emerged as an important research area, which aims to model the co-existence of multiple types of heterogeneity. In this paper, we propose a HEterogeneous REpresentation learning model with structured Sparsity regularization (HERES) to learn from multiple types of heterogeneity. HERES aims to leverage two kinds of information to build a robust...
We propose a new transductive label propagation method, termed Adaptive Neighborhood Propagation (Adaptive-NP) by joint L2,1-norm regularized sparse coding, for semi-supervised classification. To make the predicted soft labels more accurate for predicting the labels of samples and to avoid the tricky process of choosing the optimal neighborhood size or kernel width for graph construction, Adaptive-NP...
Due to the friendship paradox, the average robustness of the single mutation neighbours (µn) of genotypes on a neutral network is larger than the average robustness of the genotypes (µg). Random walks on neutral networks have an average degree equal to µn and, intuitively, we expect that evolution will not converge on populations whose average degree is considerably lower than this. This paper argues...
This paper introduces PSCAN, a port scanning-based network covert channel that violates non-discretionary system security policy that does not allow data transfer from a given process (the sender) to another given process (the receiver). Using PSCAN, the sender opens and closes network ports in a way that encodes covert data. The receiver performs a synchronized port scanning procedure on the sender's...
In order to efficiently recognize actions from depth sequences, we propose a novel feature, called Global Ternary Image (GTI), which implicitly encodes both motion regions and motion directions between consecutive depth frames via recording the changes of depth pixels. In this study, each pixel in GTI indicates one of the three possible states, namely positive, negative and neutral, which represents...
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