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Subspace clustering is one of the active research problem associated with high-dimensional data. Here some of the standard techniques are reviewed to investigate existing methodologies. Although, there have been various forms of research techniques evolved recently, they do not completely mitigate the problems pertaining to noise sustainability and optimization of clustering accuracy. Hence, a novel...
Lung sound is one of the important information in the diagnosis of respiratory disease. Many researchers have developed various algorithms to diagnose lung disease through the lung sounds. One of the parameters used as the feature of lung sound is entropy, a measure of the signal complexity in which the normal biological signal and the pathological biological signal have different complexities. Entropy...
Low-power approximate adders provide basic building blocks for approximate computing hardware that have shown remarkable energy efficiency for error-resilient applications (like image/video processing, computer vision, etc.), especially for battery-driven portable systems. In this paper, we present a novel scalable, fast yet accurate analytical method to evaluate the output error probability of multi-bit...
Breast cancer is invasive cancer among world's women above 35 years of age. The most common symptoms of breast cancer are lumps, change in shape/skin colour and liquid oozing out from nipple. Breast cancer mostly starts from breast tissues that are either in lobules or in milk ducts. Ductal carcinoma is the common type of breast cancer starts from milk ducts and spread across the. Women between the...
The concept of indoor location positioning has been around for decades now. Despite the existence of many algorithms that can achieve location positioning with remarkable performance in terms of accuracy, implementation of such algorithms has not been done on a large scale. There is a trade-off between the positioning accuracy and the complexity of the algorithm. This paper introduces and explains...
In this paper, a novel stochastic approximation technique is presented as a low-complexity alternative to conventional least squares-based digital predistortion model extraction solutions. The proposed technique is based on the simultaneous perturbation stochastic approximation (SPSA) algorithm. It avoids the hardware-intensive matrix operations associated with least squares by using an iterative...
An idea of contextual classifier ensembles extends the application possibility of additional measures of quality of base and ensemble classifiers in the process of contextual ensembles design. These measures besides the obvious classifier accuracy and diversity/similarity take under consideration the complexity, interpretability and significance. The complexity (the number of used measures and multi...
The two classical steps of image or video classification are: image signature extraction and assignment of a class based on this image signature. The class assignment rule can be learned from a training set composed of sample images manually classified by experts. This is known as supervised statistical learning. The well-known Support Vector Machine (SVM) learning method was designed for two classes...
The Zernike moments can achieve high accuracy and strong robustness for the classification and retrieval of images, but involve huge amount of computation caused by its complex definition. It has limited its exploitation in online real-time applications or big data processing. So researches on how to improve the computation speed of Zernike moments are carried out. One of the existing high-accuracy...
Finding all similar time-series patterns in real time under Dynamic Time Warping (DTW) is a huge challenge in nowadays data mining. A vital requirement of the critical task is data normalization so that the search results are accurate. However, DTW and data normalization, particularly in the streaming context, cost great deals of computation time and memory space; so many techniques are required to...
In this paper, we discuss the evaluation of the probabilistic extraction as introduced in [1], by considering three different datasets introduced in [1] -- [3]. the results show the potential of the approach, as well as its reliability and efficiency when analyzing datasets with different properties and structures. This is part of ongoing research aiming to provide a tool to extract, assess and visualize...
Support Vector Machines (SVM) is a supervised Machine Learning and Data Mining (MLDM) algorithm, which has become ubiquitous largely due to its high accuracy and obliviousness to dimensionality. The objective of SVM is to find an optimal boundary -- also known as hyperplane -- which separates the samples (examples in a dataset) of different classes by a maximum margin. Usually, very few samples contribute...
Ray-optical algorithms are an excellent choice to model the radio channel in a deterministic manner. Especially, in vehicular environments where the channel is time-variant and system designers potentially need to consider the non-stationarity of the channel, ray-optical tools are a welcome solution to evaluate the achievable system performance in specific scenarios. The main drawback of ray-optical...
The question how to manage the contradictive requirements of accuracy and compactness in classification systems remains an important question in machine learning and data mining. This paper proposes a approach that belongs to the domain of fuzzy rule-based classification and uses the method of rule granulation for error reduction and the method of rule consolidation for complexity reduction. The cooperative...
Lung sounds provide important information about the health of the lungs and airways. Lung sounds have a special and distinguishable pattern related to abnormalities that might occur in the lungs or respiration tract. Automatic lung sound recognition is directed to reduce subjectivity in assessing lung sounds. Hjorth descriptor is one method used for observing natural biological signals. Hjorth descriptor...
It can be shown that the matrix structure resulting from a fast multipole method (FMM)-based algorithm is an ℋ2-matrix, but with a full-rank representation for electrically large analysis. We compare the computational complexity of a volume integral equation (VIE) solver having a minimal-rank ℋ2-representation with that of a VIE solver using an FMM-based ℋ2-representation. The former is shown to be...
Nowadays some High Level Synthesis (HLS) tools are introduced which are able to generate Hardware Description Language (HDL) codes from high level floating point arithmetic expressions for implementation on FPGAs. Before this conversion, changing the form of high level expressions usually leads to significant improvements in the final implementation in terms of accuracy, resource usage and latency...
Sensor pattern noise (SPN) has been proved to be an inherent fingerprint of a camera, and it has been broadly used in the fields of image authentication and camera source identification. However, the SPN extracted using current denoising algorithm always contains image content residual, which would significatively influence the accuracy of camera source identification. In this paper, a novel patch-based...
This paper addresses the channel estimation in receivers of multicarrier communication signals, particularly in multiuser scenarios of the LTE uplink. B-splines are proposed for estimation of the time-frequency channel response. Cubic B-splines are considered as the appropriate basis functions allowing a trade-off between the estimation accuracy and complexity. We investigate the iterative channel...
In the field of spam detection, concentration methods have been proposed for feature construction in recent years, which convert emails into fixed length feature vectors. This paper presents a novel method aiming to break through the limit of feature vector's length. Specifically, the method uses a fixed-length sliding window to divide each email into several sections. The number of sections depends...
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