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Automatic semantic annotation of high-resolution optical satellite images is a task to assign one or several predefined semantic concepts to an image according to its content. The fundamental challenge arises from the difficulty of characterizing complex and ambiguous contents of the satellite images. To address this challenge, a diversity constrained joint multi-feature learning method is proposed...
Biologically inspired episodic memory is able to store time sequential events, and to recall all of them from partial information. Because of the advantages of episodic memory, the biological concepts of episodic memory have been utilized to many applications. In this research, we propose a new memory model, called Deep ART (Adaptive Resonance Theory), to make a robust memory system for learning episodic...
In recent years, dictionary learning (DL) has shown significant potential in various classification tasks. However, most of previous works aim to learn a synthesis dictionary. The other major category of DL-analysis dictionary learning has not been fully exploited yet. This paper proposes a novel DL method, named Topology Preserving Dictionary Learning (TPDL). First, we propose a triplet-constraint-based...
We propose a new discriminative dictionary learning framework termed Latent Label Consistent K-SVD (LLC-KSVD) for representing and classifying machine faults. Our LLC-KSVD handles the task by minimizing the reconstruction, discriminative sparse-code and classification errors at the same time. To enhance the representation and classification powers, LLC-KSVD aim to decompose given data into a sparse...
A networked controlled system (NCS) in which the plant communicates to the controller over a channel with random delay loss is considered. The channel model is motivated by recent development of tree codes for NCS, which effectively translates an erasure channel to one with random delay. A causal transform coding scheme is presented which exploits the plant state memory for efficient communications...
Large age range is a serious obstacle for automatic face recognition. Although many promising results have been reported, it still remains a challenging problem due to significant intra-class variations caused by the aging process. In this paper, we mainly focus on finding an expressive age-invariant feature such that it is robust to intra-personal variance and discriminative to different subjects...
This paper proposes a new approach for text watermarking that uses the semantic roles to embed watermark information. The technology of natural language processing is applied to find and label the three types of semantic roles A0, A1 and ADV in a text. A watermark message is converted into the hexadecimal Unicode and then compressed with the Huffman encoding to form a digit string that consists of...
Quantum Process Tomography (QPT) is one of the most important task of checking the functionality of quantum information processing devices. However, there is a significant difficulty that the required resources grow scaling exponentially with the number of qubits during the QPT. Recently, a compressed sensing QPT (CS QPT) is proposed that can reduce the required resource significantly. But the work...
Hashing learning has attracted increasing attention these years with the explosive increase of data. The hashing learning can be divided into two steps. Firstly, obtain the low dimensional representation of the original data. Secondly, quantize the real number vector of the low dimensional representation of each data point and map them to binary codes. Most of the existing methods measure the original...
This paper presents a novel image hashing method for authentication and tampering detection. Creation, modification and transfer of multimedia data becomes an easy task due to digitization. Integrity is very important for crucial and sensitive matters like medical records, legal matters, scientific research, forensic investigations and government documents. Image hashing is one of the popular method...
The success of sparse representation, in face recognition and visual tracking, has attracted much attention in computer vision in spite of its computational complexity. However, these sparse representation-based methods often assume that the coding residual follows either Gaussian or Laplacian distribution, which may not be precise enough to describe the coding residuals in real tracking situations...
LDPC decoders on faulty hardware have received increasing attention over the last few years, mainly motivated by reliability issues in emerging nanotechnologies. As a main result, it was shown that LDPC decoders are naturally robust to hardware faults. LDPC encoders on faulty hardware have received less attention, and they are expected to be less robust to hardware faults. In this work, we propose...
Compressed sensing, further to its ability of reducing resources spent in signal acquisition, may be seen as an implicit private-key encryption scheme. The level of achievable secrecy has been analyzed in the most classical settings, when the sensing matrix is made of independent and identically distributed entries. Yet, it is known that substantially improved acquisition can be achieved by tuning...
Intellectual property (IP) cores have emerged as a promising solution to the challenges of future design as well as mounting time to market pressure. However, due to increasing globalization of design supply chain, possibility of intervention and typical attacks is on the rise, which therefore mandates protection of IP cores from piracy/counterfeiting even at behavioral level. This paper presents...
The success of sparse representation, in face recognition and visual tracking, has attracted much attention in computer vision in spite of its computational complexity. These sparse representation-based methods assume that the coding residual follows either Gaussian or Laplacian distribution, which may not be accurate enough to describe the coding residuals in real scenarios. In order to deal with...
Due to the wide variety of copy videos, the existing video copy detection methods using single feature face great challenges, especially for video content matching, which are difficult to deal with various copy video transformations. To overcome this problem, a video copy detection method based on sparse representation of MPEG-2 spatial and temporal features is proposed in this paper. Firstly, the...
Big traffic data analysis for intelligent transportation is attracting more and more attention. Due to different designs of vehicles in the same class and the similarity of shape and textures between different classes, vehicle classification is remaining a challenge. In this paper, different from traditional methods that only classify vehicles to two or three types in one viewpoint, a novel method...
Controllers based on Synchronous Finite State Machines (SFSM) are widely used in the control unit design of complex digital systems. These systems can present serious problems related to the global clock. In this context, the asynchronous paradigm shows interesting features that fit as an alternative for the design, despite of the difficulties of the application of asynchronous logic. An interesting...
NP-hard combinatorial optimization algorithms are often characterized by their approximation ratios. In real world applications, the resilience of algorithms to input fluctuations and to modelling errors pose important robustness requirements. This work suggests a provable algorithmic regularization and validation strategy based on posterior agreement. The strategy regularizes algorithms and ranks...
This article presents a new viral precursor miRNAs identification tool using back-propagation neural network. The tool mainly discriminates the viral precursor miRNAs from coding sequences and other pseudo precursor miRNAs. It was trained with viral precursor miRNAs from miRBase, pseudo precursor miRNAs and coding sequences from ViralmiR and NCBI database, respectively. Top 20 features out of totally...
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