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With a framework like OpenTuner, one could build domain-specific multi-objective program auto-tuners and gain significant performance improvements. But explaining why and interpreting the results are often hard, mainly due to the large number of parameters and the inability to figure out how each parameter affects the performance improvement. We have a solution that can explain the performance improvements...
Privacy preserving techniques have been actively studied on the time-series data in various fields like financial, medical and weather analysis. I focused towards preserving the data through anonymity and generalization, to resist homogeneity attack. First investigate, what's the privacy to be incorporated in the time-series data and after finding the data which needs to be preserved various perturbation...
Back off techniques are employed in syllable based unit selection speech synthesis systems to maintain the naturalness of the speech in spite of the missing syllables. In synthesizing the missing complex consonant clusters syllables of Telugu, we introduced reduced vowel epenthesis as a rule-based backoff strategy[1]. In this paper, we refine the scope of the approach in selectively applying vowel...
Numerous research papers have shown that cost and schedule growth are interrelated for NASA space science missions. Although there has shown to be a strong correlation of cost growth with schedule growth, it is unclear what percentage of cost growth is caused by schedule growth and how schedule growth can be controlled. This paper attempts to quantify this percentage by looking at historical data...
We take university library as an example to mine association rules from circulation records in university libraries. To satisfy the requirement of information service in libraries, a mining algorithm is presented. This paper using association rule mining techniques to find law from the library borrowed document, and recommendation service is found by these rules. Furthermore, a recommendation service...
Road Sign Detection (RSD) is becoming a major goal of the safety Advanced Driving Assistance Systems (ADAS). Automotive research area share many publications based various techniques used to detect and classify signs. This paper provides a hardware detection-based correlation architecture using Xilinx System Generator (XSG). This proposed architecture outsets with pre-processing step: RGB to YCrCb...
We investigate the problem of finding unknown associations between 'concepts' in a given text corpus. A 'concept' is an entity, which is referred to by a phrase or multiple phrases (in case an entity has several names or synonyms, e.g. "illness", "disease"), while an 'association' is a relationship defined in a particular domain. The pairwise associations computation poses major...
In this paper, we propose a new approach to compress Electroencephalogram (EEG) signals using the WAAVES compression algorithm and Independent Component Analyses (ICA). Firstly, ICA is applied to the 1D-EEG signals as a preprocessing stage to uncorrelate signals. Then, the output of the ICA is scaled and reformatted into a 2D-matrix to be compressed as an image using the WAAVES coder. This scheme...
This paper addresses the problem of anomaly detection on rotating machinery in industrial environments using single channel audio signals. The proposed algorithm is based on image processing feature analysis obtained from the image representation of the Short-time Fourier Transform of reference and degraded audio signals. In order to assess the potential of the algorithm, a 8 signals database is recorded...
The paper presents two systems to recognize five facial expressions (anger, surprise, joy, sadness and neutral) and gives a performance review on them. Both systems are developed on the same facial features extraction process which is histograms of oriented gradients extraction. Vectors of facial features are classified by the systems using the following proposed methods: template matching method...
This paper presents a full-reference (FR) image quality assessment (IQA) method based on a deep convolutional neural network (CNN). The CNN extracts features from distorted and reference image patches and estimates the perceived quality of the distorted ones by combining and regressing the feature vectors using two fully connected layers. The CNN consists of 12 convolution and max-pooling layers;...
Many people undergo from the stress in their everyday life. Due to there close relationship between the stress, mental health and psychological aspect. Stress management using EEG data is very challenging task. In this paper, classification of EEG data is done by using statistical techniques like, mean, standard deviation, variance and correlation. This algorithm got the 0.0056 correlation which is...
The process of quantifying image quality consists of engineering the quality features and pooling these features to obtain a value or a map. There has been a significant research interest in designing the quality features but pooling is usually overlooked compared to feature design. In this work, we compare the state of the art quality and content-based spatial pooling strategies and show that although...
Building natural scene statistic models is a potentially transformative development for a wide variety of visual applications, ranging from the design of faithful image and video quality models to the development of perceptually optimized image enhancing techniques. Most predominant statistical models of natural images only characterize the univariate distributions of divisively normalized bandpass...
In this paper, we propose an emotion-based feature fusion method using the Discriminant-Analysis of Canonical Correlations (DCC) for facial expression recognition. There have been many image features or descriptors proposed for facial expression recognition. For the different features, they may be more accurate for the recognition of different expressions. In our proposed method, four effective descriptors...
Objective image quality assessment plays an important role in various image processing applications, where the goal of this process is to automatically evaluate the image quality in agreement with human visual perception. In this paper, we propose three different nonlinear learning approaches in order to design image quality assessment models, which serve to predict the perceived image quality. The...
An integration of real-time EEG-based human emotion recognition algorithms in brain-computer interfaces can make the user's experience more complete, more engaging, less emotionally stressful or more stressful depending on the target of the application. Valence component of emotion, level of pleasantness, is one of the most important criteria of online assessment of social processes from brain signals...
Previous feature learning based blind image quality assessment (BIQA) methods invariably require large codebook or codebook updating procedure to obtain satisfying performance. In this paper, we propose a novel general purpose BIQA method, local feature aggregation (LFA) model, which requires only a much smaller codebook without the need for codebook updating. The proposed model consists of three...
Cough is an important symptom in many diseases and at times is the only major symptom to diagnose some particular ailments. Cough is the powerful mechanism of human body to clear the central airways. Analyzing the cough type, its intensity and sound, the medical experts can estimate enough details about the ailment and appropriate cure. Hence, it should be possible to estimate the cough type and the...
With the increasing importance of high dynamic range (HDR) imaging and low availability of HDR displays, the need for efficient tone mapping techniques is very crucial. However the tone mapping operators tend to introduce distortions in the HDR images, thus making it visually unpleasant in normal displays. Subjective evaluation of images is important for rating the tone mapping operators as the users...
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