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During the transition to professional universities, it is normal that students bring preconceptions with them from many domains of knowledge as well as misconceptions and numerous other idiosyncrasies based on their previous learning experiences. Knowing how our students think will help us to understand their mathematical thinking skills, to make sense of their errors and to improve our instruction...
Functional brain mapping under naturalistic stimuli such as video watching has been receiving greater interest in recent years. We presented a sparse representation based data-driven strategy to explore consistent functional brain networks during free viewing of continuous video streams. Compared with the traditional independent component analysis (ICA) based method, the novelty of our method is taking...
We propose a new T2 mapping method to improve the CS reconstruction based on the theory of structured sparse representation. The proposed method learns the PCA sub-dictionaries for adaptive sparse representation and suppresses the sparse coding noise to obtain good reconstructions. Experimental results demonstrate that the proposed method capable of delivering state-of-the-art performance at CS reconstruction...
Every software company has its own set of coding guidelines that increases code readability, code reuse and helps in code storage in organized structure. These guidelines also using various tools help in the determination of various attributes of code such as its complexity, duplicity, warnings, memory leaks, coverage etc. However coding guidelines are not present in academics field. Scholars of educational...
This article describes lossless compression algorithms for multisets of sequences, taking advantage of the multiset's unordered structure. Multisets are a generalisation of sets where members are allowed to occur multiple times. A multiset can be encoded naïvely by storing its elements in some sequential order, but then information is wasted on the ordering. We propose a technique that...
Semantics of communicated data can lead to conclusions with varying degrees of priorities. Depending on the interests of the communicating parties, some facts lead to conclusions that carry a high risk when ignored, and others may not be worth the resources to share the facts leading to those uninteresting conclusions. This paper studies the worst-case semantic data compression problem for sharing...
The problem of compressing a large collection of feature vectors so that object identification can further be processed on the compressed form of the features is investigated. The idea is to perform matching against a query image in the compressed form of the feature descriptor vectors retaining the metric. Specifically, we concentrate on SIFT (Scale Invariant Feature Transform), a known object detection...
Higher-order compression is a scheme for compressing data in the form of functional programs that generate the data. This compression scheme can be viewed a generalization of grammar-based compression, and retains its advantage that compressed data can be manipulated without decompression. Furthermore, the higher-order compression can achieve a high compression ratio and also discover patterns that...
In this paper we present a SNR scalable extension design on three-dimensional video compression using High Efficiency Video Coding (3D-HEVC). A multi-loop decoder solution is integrated into the proposed scalable coding serves as the whole framework for the SNR scalable 3D-HEVC. To effectively improve the coding performance, an inter-layer texture prediction is extended into the proposed scalable...
In this paper we propose a reversible string transformation method that could be used to decrease entropy in input messages. The method is based on context information included in the structure we call a context map. The method is used to manipulate with symbols distribution. Since the change in distribution of symbols is followed by change in entropy, we could use cases when entropy is decreasing...
We consider practical implementations of compressed bit vectors, which support rank and select operations on a given bit-string, while storing thebit-string in compressed form. Our approach relies on variable-to-fixed (V2F) encodings of the bit-string, an approach that has not yet been considered systematically for practical encodings of bit-vectors. This approach leadsto fast practical implementations...
In this paper, we present two adaptive edge encoding schemes for the operational rate-distortion optimal polygon-based shape coding. The encoding edge is represented by an octant number, a major component, and a minor component, where the ranges of the two components are determined at two levels. For the object-level, these ranges are either determined by users or adaptive to the contour characteristics...
During his long and illustrious research career, James L. Massey (1934–2013) devoted his attention to spread-spectrum communications very infrequently and then only for brief periods of time. In a short summer study for NASA in 1969, he obtained several results on the analysis and design of sequences for direct-sequence spread-spectrum communications. These results were not published as journal articles...
Basically, computers just deal with numbers. They can store letters and other characters by assigning a number for each one. Before Unicode was designed, there were found hundreds of different encoding systems for assigning these numbers. No single encoding could contain sufficient characters: As for example, the European Union alone requires several different encodings to cover all its languages...
We view the index coding problem with an arbitrary number of source bits and potentially overlapping demands as an instance of rate-distortion with multiple receivers, each with distinct side information. By applying network-information-theoretic tools, we find the optimal rate for the special case in which each source bit is present at either all of the decoders, none of the decoders, all but one...
Sparse coding algorithm is an learning algorithm mainly for unsupervised feature for finding succinct, a little above high - level Representation of inputs, and it has successfully given a way for Deep learning. Our objective is to use High - Level Representation data in form of unlabeled category to help unsupervised learning task. When compared with labeled data, unlabeled data is easier to acquire...
Steganography is the art of hiding information, which prevents the detection of hidden messages. It is derived from a Greek word technically meaning "Covered Writing". Comparing with cryptography, we can say that cryptography is a method where the message is not understood whereas steganography is a method where the message is not visible. Steganography can help us transcend the limitations...
This paper presents a novel data hiding using Integer Wavelet Transform (TWT) through lifting scheme that aims to achieve high quality of stego image. This method transforms a spatial domain cover image into a frequency domain cover image. It hides the secret message into detail coefficients (CH, CV, CD) of IWT by construct a binary image in any of selected bit in CH, CV, CD separately and compresses...
IEEE-754 specifies interchange and arithmetic formats and methods for binary and decimal floating-point arithmetic in computer programming world. The implementation of a floating-point systemusing this standard can be done fully in software, or in hardware, or in any combination of software and hardware. This project propose VHDL implementation of IEEE-754 Floating point unit. In proposed work the...
The links of network on chip(NOC) power dissipation compete with the power dissipation of other elements of the communication subsystem, namely, the routers and network interfaces(NIs). A set of data encoding schemes aimed at reducing the power dissipation in the links of an NoC. Self switching activity and coupling switching activity are responsible for reducing the power dissipation. Thus, both...
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