Real-time face image recognition is a face recognition system that is done directly using a webcam camera from a computer. Face recognition system aims to implement a biometrics system as a real-time facial recognition system. This system is divided into two important processes, namely the training process and the identification process. The registration process is a process where a user registered their name in a system and then registers their face. Face data that has been registered will be used for the next process, namely the identification process. The face registration process uses face detection using the OpenCV library. The feature extraction process and introduction to the recognition system use the Eigenface method. The results of this study found that, the Eigenface method is able to detect faces accurately up to 4 people simultaneously. The greater the threshold value will result in a greater value of FRR, while there isn’t any FAR value found from different thresholds. The level of lighting, poses, and facial distance from the camera when training and testing the face image heavily influences the use of the eigenface method.

There are many graph abstraction methods that are existed as solutions for problems of graphs visualization. Visualization problems include edge crossings and node occlusions that hide the potential existed patterns. The aim of this research is to abstract graphs using one of network analysis metrics which is node betweenness centrality. Betweenness centrality is calculated for all graph nodes. Graph abstraction is done by removing the nodes with their attached edges such that they have betweenness centrality lower than a certain examined threshold. Experiments have been conducted and results show that the proposed abstraction method can effectively reduce the complexity of the graph visualization in term of node degree. Modularity of clusters after filtering is decreased but the final graph visualization is simpler and more informative.

The risk of cardiovascular diseases is growing worldwide, and its early detection is necessary to reduce the level of risk. Structural parameters of the carotid artery as intima-media thickness and functional parameters such as arterial elasticity are directly associated with cardiovascular diseases. Segmentation of the carotid artery is required to measure the structural parameters and its temporal value that is used to estimate the arterial elasticity. This paper has two primary objectives: (i) Segmentation of the sequence of carotid artery ultrasound to measure temporal value of intima-media thickness and lumen-diameter, and (ii) Young’s modulus of elasticity estimation. The proposed segmentation method uses the contextual feature of the image pattern and is based on multi-layer extreme learning machine auto-encoder network. This segmentation method has two parts: (a) region of interest localization and (b) lumen-intima interface and media-adventitia interface detection at the far wall. ROI localization algorithm divides the ultrasound frame into columns and also divides each column into overlapping blocks, ensuring that every column has a region of interest block. A multi-layer extreme learning machine with auto-encoder is trained with labelled data and in testing; system classifies the blocks into ‘region of interest’ and ‘non-region of interest’. Pixels belonging to the region of interest are classified in the first part and a similar network-based method is proposed for lumen-intima and media-adventitia interface detection at the near wall of the carotid artery. Structural parameter of the artery, intima-media thickness and lumen diameter are measured in a sequence of images of the cardiac cycle. The temporal values of structural parameters are used to estimate the young’s modulus of elasticity.

Removing the brain part, as the epilepsy source attack, is a surgery solution for those patients who have drug resistant epilepsy. So, the epilepsy localization area is an essential step before brain surgery. The Electroencephalogram (EEG) signals of these areas are different and called as focal (F) whereas the EEG signals of other normal areas are known as non-focal (NF). Visual inspection of multi-channels for F EEG detection is time-consuming along with human error. In this paper, an automatic and adaptive method is proposed based on second order difference plot (SODP) of EEG rhythms in empirical wavelet transform (EWT) domain as an adaptive signal decomposition. SODP provides the data variability rate or gives a 2D projection for rhythms. The feature vector is obtained using the central tendency measure (CTM). Finally, significant features, chosen by Kruskal–Wallis statistical test, are fed to K nearest neighbor (KNN) and support vector machine (SVM) classifiers. The achieved results of the proposed method in terms of three objective criteria are compared with state-of-the-art papers demonstrating an outstanding algorithm here in.

In this work, a 5 state left to right HMM-based Bangla Isolated word speech recognizer has been developed. To train and test the recognizer, a small corpus of various sampling frequencies have been developed in noisy as well as the noiseless environment. The number of filter banks is varied during the feature extraction phase for both MFCC and PLP. The effects of 2nd and 3rd differential coefficients have also been observed. Experimental results exhibit that MFCC based feature extraction technique is better in CLASSROOM environment on the contrary PLP based technique performs better not only in a noiseless environment but also in when AC or FAN noise is present. We have also noticed that higher sampling frequency and higher filter order don’t always help to improve the performance.

This paper proposes a 10-bit mixed current mode low power SAR ADC for sensor node application. The different entities of a successive approximation register (SAR) analog-to-digital converter (ADC) circuit has a hybrid or mixed mode approach i.e.,voltage mode regenerative comparator; mixed SAR logic; and current mode digital-to-analog converter (DAC). The performance limitation of speed and the kick-back noise of a dynamic comparator is resolved using duty cycle controlled regenerative comparator. A mixed mode logic of a SAR is partitioning the design into synchronous ring counter and asynchronous output register. The data shifting of a ring counter is with the common clock tick while the output register exchanged it asynchronously using handshake signals, resulting in a low power SAR. The current mode switching function in a DAC to reduce asynchronous switching effect resulting in a low energy conversion per step. In overall, the proposed mixed SAR ADC consumes a 41.6  power and achieves an SFDR 69.3 dB at 10 MS/sec and 1 V supply voltage. It is designed and simulated in the 0.18 m TSMC CMOS process.