In this paper we propose an automated attendance management system This system, which is based on face detection and recognition algorithms, automatically detects the student when he enters the class room and marks the attendance by recognizing him The system architecture and algorithms used in each stage are described in this paper Different real time scenarios are considered to evaluate the performance of various face recognition systems This paper also proposes the techniques to be used in order to handle the threats like spoofing When compared to traditional attendance marking this system saves the time and also helps to monitor the students

Automated attendance management system based on face recognition algorithms

Electromyography (EMG) is widely used in various fields to investigate the muscular activities. Since EMG signals contain a wealth of information about muscle functions, there are many approaches in analyzing the EMG signals. It is important to know the features that can be extracting from the EMG signal. The ideal feature is important for the achievement in EMG analysis. Hence, the objective of this paper is to evaluate the features extraction of time domain from the EMG signal. The experiment was setup according to surface electromyography for noninvasive assessment of muscle (SENIAM). The recorded data was analyzed in time domain to get the features. Based on the analysis, three features have been considered based on statistical features. The features was then been evaluate by getting the percentage error of each feature. The less percentage error determines the ideal feature. The results shows that the extracted features of the EMG signals in time domain can be implement in signal classification. These findings could be integrated to design a signal classification based on the features extraction.

/pdf/features-extraction-of-electromyography-signals-in-time-1sz99srmg0.pdf

Features extraction of electromyography signals in time domain on biceps brachii muscle

In this paper, the performances of well-known image recognition methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Patterns Histograms (LBPH) and Support Vector Machine (SVM) are tested and compared with proposed convolutional neural network (CNN) for the recognition rate of the input animal images. In our experiments, the overall recognition accuracy of PCA, LDA, LBPH and SVM is demonstrated. Next, the time execution for animal recognition process is evaluated. The all experimental results on created animal database were conducted. This created animal database consist of 500 different subjects (5 classes/ 100 images for each class). The experimental result shows that the PCA features provide better results as LDA and LBPH for large training set. On the other hand, LBPH is better than PCA and LDA for small training data set. For proposed CNN we have obtained a recognition accuracy of 98%. The proposed method based on CNN outperforms the state of the art methods.

Animal recognition system based on convolutional neural network

Face Recognition Systems has been applied in a wide range of applications. However, their efficiency drastically diminish when they are applied under uncontrolled environments such as illumination change conditions, face position and expressions changes. Because of that, it is necessary to evaluate the performance of different feature extraction techniques robust to this kind of transformations for its further integration to a Face Recognition System. In this paper, we study and evaluate the pertinence of using the Histogram Oriented Gradients (HOG) method as a feature extraction technique to deal with the transformations already mentioned. To measure the performance of the proposed feature extraction method, several experiments were performed using two databases: one database under a controlled environment taken from the literature and other built in our laboratory under a semi controlled environment. The experimental results show that using HOG combined with different distances classifiers provides better results than those achieved with the well-know Eigenfaces technique.

Face Recognition Using Histogram Oriented Gradients

This work was funded by a Queen Mary University of London Westfield
Trust Research Studentship.

/pdf/automatic-transcription-of-polyphonic-music-exploiting-p98apsb8xm.pdf

Automatic Transcription of Polyphonic Music Exploiting Temporal Evolution

Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing. Face recognition is an important and very challenging technique to automatic people recognition. Up to date, there is no technique that provides a robust solution to all situations and different applications that face recognition may encounter. In general, we can make sure that performance of a face recognition system is determined by how to extract feature vector exactly and to classify them into a group accurately. It, therefore, is necessary for us to closely look at the feature extractor and classifier. In this paper, Principle Component Analysis (PCA) is used to play a key role in feature extractor and the SVMs are used to tackle the face recognition problem. Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition. We illustrate the potential of SVMs on the Cambridge ORL Face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. The SVMs that have been used included the Linear (LSVM), Polynomial (PSVM), and Radial Basis Function (RBFSVM) SVMs. We provide experimental evidence which show that Polynomial and Radial Basis Function (RBF) SVMs performs better than Linear SVM on the ORL Face Dataset when both are used with one against all classification. We also compared the SVMs based recognition with the standard eigenface approach using the Multi-Layer Perceptron (MLP) Classification criterion.

Face recognition using PCA and SVM

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development and modern Human Computer Interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. For properly analyze EMG signal there need good quality of decomposition so that it can reveal the total characteristics of EMG signals. Because EMG signal is Non-Stationary signal so it needs such a method that can decompose non-stationary signal thus wavelet decomposition is a good choice for this type. There are different types of wavelet available. Henceforth, it is necessary that proper attempt should be taken to choice the best one. Here analyses of EMG Signals were made by Various Wavelet Decomposition method with different types of wavelet and it illustrates the comparative study on best possible energy localization in the time-scale plane in order to show the performance. Thus we can choice the right one. The EMG signals used for this analysis - were found both from locally collected as well as from www.emglab.net[5] which provides EMG signal related raw data and other facilities.

EMG signal decomposition using wavelet transformation with respect to different wavelet and a comparative study

Automatically recovering the musical notation for a given signal allows reproducing and modifying the original performance. Music transcription can be considered as extracting the sequence of the notes that best models our audio signal. So it is understood as the process of analyzing a music signal in order to write down the parameters of the sounds occurring in it. Another principal application is structured audio coding: a MIDI-like representation which is highly compact but holds on to the identification and characteristics of a musical note to a significant extent. The applied notation can be the traditional musical notation which gives sufficient information for performing the piece using the available musical instruments. Automatic Music Transcription (extracting musical notes from an audio stream) is a complicated task due to the complexity of musical sounds that requires an effective solution. The scope of this article is in the automatic transcription of the harmonic and melodic parts of the real-world music signals. Algorithms are proposed that address two distinct sub-problems of music transcription. The main part of the article emphasizes the estimation of the several concurrent musical notes. The other sub-problem discusses musical meter estimation. This has to do with rhythmic aspects of music and refers to the estimation of the regular pattern of strong and weak beats in a piece of music. Signal processing methods for the automatic transcription of music are developed in this paper.

Template music transcription for different types of musical instruments

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Simple Summary Colorectal cancer (CRC) is a complex disease that has a high mortality rate. This study explored CRC-related core genes (CGs) from multiple microarray gene-expression profiles in the NCBI-GEO database by combining some statistics and bioinformatics techniques. It also disclosed their molecular functions, biological processes, cellular components, signaling pathways, and transcriptional and post-transcriptional regulatory factors by using different online bioinformatics tools and databases. The prognostic power of CGs was investigated from the independent TCGA database by using survival probability curves and box plots of CGs-expressions in different stages (control, Stage 1, Stage 2, Stage 3, and Stage 4) of CRC. Finally, a few CGs-guided drug molecules were suggested for the treatment of CRC by molecular docking and dynamic simulation studies. Therefore, the findings of this study would be useful resources for early diagnosis, prognosis, and therapies of CRC. Abstract Colorectal cancer (CRC) is one of the most common cancers with a high mortality rate. Early diagnosis and therapies for CRC may reduce the mortality rate. However, so far, no researchers have yet investigated core genes (CGs) rigorously for early diagnosis, prognosis, and therapies of CRC. Therefore, an attempt was made in this study to explore CRC-related CGs for early diagnosis, prognosis, and therapies. At first, we identified 252 common differentially expressed genes (cDEGs) between CRC and control samples based on three gene-expression datasets. Then, we identified ten cDEGs (AURKA, TOP2A, CDK1, PTTG1, CDKN3, CDC20, MAD2L1, CKS2, MELK, and TPX2) as the CGs, highlighting their mechanisms in CRC progression. The enrichment analysis of CGs with GO terms and KEGG pathways revealed some crucial biological processes, molecular functions, and signaling pathways that are associated with CRC progression. The survival probability curves and box-plot analyses with the expressions of CGs in different stages of CRC indicated their strong prognostic performance from the earlier stage of the disease. Then, we detected CGs-guided seven candidate drugs (Manzamine A, Cardidigin, Staurosporine, Sitosterol, Benzo[a]pyrene, Nocardiopsis sp., and Riccardin D) by molecular docking. Finally, the binding stability of four top-ranked complexes (TPX2 vs. Manzamine A, CDC20 vs. Cardidigin, MELK vs. Staurosporine, and CDK1 vs. Riccardin D) was investigated by using 100 ns molecular dynamics simulation studies, and their stable performance was observed. Therefore, the output of this study may play a vital role in developing a proper treatment plan at the earlier stages of CRC.

/pdf/exploring-core-genes-by-comparative-transcriptomics-analysis-gdlar1bp.pdf

Md. Omar Faruqe

Papers

Face recognition using PCA and SVM

EMG signal decomposition using wavelet transformation with respect to different wavelet and a comparative study

Template music transcription for different types of musical instruments

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Exploring Core Genes by Comparative Transcriptomics Analysis for Early Diagnosis, Prognosis, and Therapies of Colorectal Cancer