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All sorts of Malwares severely threaten users in Internet. These malwares do share some common characteristics, despite malware and its variants may vary a lot from content signatures. The common characteristics they shared can be used to reveal the real intent of malware. In this paper, we study on the behavior characteristics of malwares in Internet, and based on which we present the method to extract...
Low-Density Parity-Check (LDPC) codes belong to a class of linear block codes that can approach the Shannon limit. However, due to the high decoding complexity, LDPC codes with extremely long block lengths will result in large decoding latency in practical applications. Recently, Cloud Radio Access Networks (C-RANs) have attracted much attention because of their innovative architecture, which involves...
We address the vehicle detection and classification problems using Deep Neural Networks (DNNs) approaches. Here we answer to questions that are specific to our application including how to utilize DNN for vehicle detection, what features are useful for vehicle classification, and how to extend a model trained on a limited size dataset, to the cases of extreme lighting condition. Answering these questions...
In sound reproduction system such as loudspeaker-room system, the acoustic characteristics of the room will affect sound quality. Equalization is therefore essential for room response. Besides, adding loudness conversion filter to traditional equalizer can result in an optimum effect in auditory scale. Considering the human auditory characteristics and the limitation of single-point equalization,...
This paper studies co-prime sampling for two-dimensional synthetic aperture radar (SAR) imaging and proposes a new approach based on co-prime up-sampling and compressive sensing to improve the resolution of SAR images. In order to decrease the redundancy in SAR phase history, we extend the co-prime down sampling structure to the fast-time domain and introduce a random matrix to compress the data in...
Recently, sparse representation has been widely used in computer vision and visual tracking applications, including face recognition and object tracking. In this paper, we propose a novel robust multi-target tracking method by applying sparse representation in a particle probability hypothesis density (PHD) filter framework. We employ the dictionary learning method and principle component analysis...
The precise estimation of the frequency of the signal is of great significance in many areas of application. In this paper, a new algorithm for estimating the frequency of one real tone in noise from a block of N uniformly spaced samples is proposed. This algorithm is based on the fundamental principle of the DFT. As a stand-alone algorithm, this estimator provides excellent performance but the computation...
Channel variability is one of the largest challenges for speaker verification (SV) techniques. Techniques in the feature, model and score domains have been applied to mitigate the channel impact. In this paper, we strive to study on robust deep feature learning with the deep belief network (DBN) by using traditional spectral features such as MFCC or PLP. In detail, during the training phase, a DBN...
This paper proposes a deep neural network (DNN) based non-intrusive speech quality estimation method in real-time voice communication systems. Since the proposed method only utilizes real-time control protocol (RTCP) information in the receiver side and does not need a reference signal, it is possible to continuously monitor the quality of service (QoS). Unlike the conventional non-intrusive E-model...
This paper deals with two problems: (1) the selection of a set of music features in order to achieve high genre classification accuracies; (2) the absence of a representative music dataset of regional Brazilian music. In this paper, we propose a set of features to classify genres of music. The features proposed were obtained by a methodical selection of important features used in the literature of...
Successive cancellation list (SCL) decoding for polar codes is promising in data communication. However, in addition to L times complexity of conventional SC, both path selecting and updating result in extra complexity. In detail, the copy of intermediate values suffers from a long latency, especially when list size L is large. In this paper, a stage-located copy algorithm is proposed to avoid copying...
Computationally efficient methods for accurate, bias free DOA estimation from a source signal impinging on a sparse array are presented. In particular, the presence of I/Q mismatch and D.C. offsets are discussed. Since the methods meet Cramer-Rao bounds and able to cope array imperfections such as nonuniform gains, element failure, they are useful in short sparse array implementations with simplified...
Seismocardiography (SCG) measures the precordial vibrations using a sensor called accelerometer, which is of small size and low weight. These features support better attachment of it to the subject's body and hence get less affected by the slow motion of the subject. However, noise generated due to the footsteps, while walking, contaminates the SCG signal. Therefore, in this paper, a novel method...
This work investigates content sharing in cellular device-to-device underlay based wireless distributed storage systems by exploiting an (n, k, d) distributed content coding scheme. To guarantee that content requesters (CRs) can rely on enough eligible content helpers (CHs) for content download, the selection of CH5 and the determination of the coding parameter k are optimized by evaluating the physical...
As Unmanned Aerial Vehicle's (UAV) battery life and stability develop, multiple UAVs are having more and more applications in the uninterrupted patrol and security. Thus UAV's searching, tracking and trajectory planning become important issues. This paper proposes an online distributed algorithm used in UAV's tracking and searching, with the consideration of UAV's practical need to recharge under...
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