Ekstraksi Fitur Citra Radiografi Thorax Menggunakan DWT dan Moment Invariant

COVID-19 is an infectious disease caused by thecoronavirus family, namely severe acute respiratorysyndrome coronavirus 2 (SARS-CoV-2). The fastest methodto identify the presence of this virus is a rapid antibody or antigen test, but confirming the positive status of a COVID-19 patient requires further examination. Lung examination using chest X-ray images taken through X-rays of COVID-19patients can be one way to confirm the patient's conditionbefore/after the rapid test. This paper proposes a featureextraction model to detect COVID-19 through chestradiography using a combination of Discrete WaveletTransform (DWT) and Moment Invariant features. In thiscase, haar wavelet transform and seven Hu moments wereused to extract image features in order to find unique featuresthat represent chest radiographic images as suspectedCOVID-19, pneumonia, or normal. To find out theuniqueness of the proposed features, it is coupled with thekNN and generic ANN classification techniques. Based on theperformance parameters assessed, it turns out that thewavelet-based and moment invariant thorax radiographicimage feature model can be used as a unique featureassociated with three categories: Normal, Pneumonia, andCovid-19. This is indicated by the accuracy value of 82.7% inthe kNN classification technique and the accuracy, precision,and recall of 86%, 87%, and 86% respectively with the ANNclassification technique.

MATHEMATICAL BASIS FOR ANALYSING SIGNALS REMOVED FROM FROM THE HUMAN CEREBRAL CORTEX USING DOBESHI AND HAAR METHODS

The article presents the results of the study of signals taken from the human cerebral cortex, and presents the mathematical foundations of analysis using the methods of Daubechy and Haar. A comparative analysis of the method of the Daubechy and Haar wavelet transform implemented in MATLAB and developed using the C++ programming language in the course of the study on the example of a recorded audio signal with natural interference is given.

FPGA Implementation of Eye Movement Detection Algorithm

In this paper, we propose an simple and efficient VLSI hardware architecture is implemented for eye movement detection. For Eye movement detection reading activity Electrooculography (EOG) signal is considered. Here, for denoising the noisy EOG signal efficient FIR filter and for decomposition of denoised EOG signal an efficient Haar wavelet transform architecture is used respectively. The modified VLSI hardware architecture method detected the saccade (left movement of eye and right movement of eye) and blink efficiently. The hardware architecture of the eye movement detection algorithm functionality is verified by using Xilinx System Generator hardware co-simulation tool. The eye movement detection algorithm is implemented on the ZedBoard FPGA using Xilinx Vivado design suite.

Computation of Wavelet and Multiwavelet Transforms Using Fast Fourier Transform

A novel fast and efficient algorithm was proposed that uses the Fast Fourier Transform (FFT) as a tool to compute the Discrete Wavelet Transform (DWT) and Discrete Multiwavelet Transform. The Haar Wavelet Transform and the GHM system are shown to be a special case of the proposed algorithm, where the discrete linear convolution will adapt to achieve the desired approximation and detail coefficients. Assuming that no intermediate coefficients are canceled and no approximations are made, the algorithm will give the exact solution. Hence the proposed algorithm provides an efficient complexity verses accuracy tradeoff. The main advantages of the proposed algorithm is that high band and the low band coefficients can be exploited for several classes of signals resulting in very low computation.

Novel Model for Automatic Classification of the Epileptic Seizures Using Fast Fourier Series-Haar Wavelet Transform

The disorder based on neurological can be considered as epilepsy that leads to the recurrent seizures in occurrence. The electronic characteristics of brain can be monitor by the electroencephalogram (EEG). It is most commonly used in the medical application. The function monitoring records can be non linear as well as non stationary functioning. The present work produce a novel methodology, it is depend on Fast Fourier series (FFS) and wavelet transform based on Haar. These methods are used for the various kinds of epileptic seizure the electroencephalogram based signal. The detection of boundary is occur by the representation of scale-space and it also adapted to the image segmentation of the spectrum depends on the FBSE that can be obtained with the electroencephalogram based signal and the purpose of the EWT is also used to attain the narrow sub band based signals. These image segmentation and classification process implementation by FPGA based microprocessor and systems. The FFS-HMT can produce the sub band signal from the Hilbert marginal spectrum it is represented as HMS. The HMS can be used to compute the line length and the entropy characteristics due to the corresponding various kinds of the level based oscillatory of the electroencephalogram signal. Here we apply the selected feature extraction depends on the ranking parallel vector. With the use of an electroencephalogram signal, the robust random forest is utilized to classify selected feature extraction in normal and epileptic participants. The assessment of performance based on classification can be measured in FPGA microprocessor the term of classification accuracy for different sample length of EEG. The current methodology aids neurologists in distinguishing between healthy and epileptic people using electroencephalogram signals.

GPU based Image Compression with Scaling-Hard-Threshold Using HWT (Haar-Wavelet-Transform) + RL (Run-Length) Pair Encoding

In 5G MASSIVE MIMO the main problem for better video communication is of transmission bandwidth issues which in turn reflects in video conferencing, Mobile-learning, gaming, graphics applications etc. However, in regard to a versatile framework i.e. a computerized camera with web and correspondence office, at that point the transmission capacity for correspondence and additionally stockpiling are of genuine concern. Tremendous valuable data must be put away and recovered proficiently for down to earth purposes. This is the novel methodology that scaling technique has proposed for 2D Haar wavelet transform based compression of picture utilizing parameter CR- ratio of Compression with PSNR improvement, with maintaining picture quality, less bandwidth usage with better compression ratio which increase by increasing speed with the help of NVIDIA GPU.

An Efficient Method for Stamps Recognition Using Histogram Moment with Haar Wavelet Sub-bands

One major problem facing some environments, such as insurance companies and government institutions, is when a massive amount of documents has to be processed every day. Thus, an automatic stamp recognition system is necessary. The extraction and recognition of a general stamp is not a simple task because it may have various shapes, sizes, backgrounds, patterns, and colors. Moreover, the stamp can be printed on documents with bad quality and rotation with various angles. Our proposed method presents a new approach for the preprocessing and recognition of color stamp images. It consists of four stages, which are stamp extraction, preprocessing, feature extraction, and matching. Stamp extraction is achieved to isolate complex background and remove unwanted data or noise that is surrounding the stamp area. The preprocessing stage is necessary to improve the stamp brightness and eliminate the rotation that occurs during the stamping process. In feature extraction, the extracted information will be representing the desirable feature vector in order to discriminate between stamps using local distribution of statistical features and Haar wavelet with histogram moment. Finally, each extracted feature vector will be saved in the dedicated system database for matching purpose. The test results indicate that the proposed system provides a high recognition rate for two sets of the proposed features (i.e., 99.29% recognition rate for the local distribution of statistical features and 96.01% recognition rate for the Haar wavelet transform with histogram and moment).