Pembinaan Sistem Pintar Untuk Penentuan Kualiti Air Berdasarkan Rangkaian Neural.

Alga merupakan organisma mikro yang digunakan dalam pemerhatian secara biologi bagi penentuan kualiti air sungai. Algae are microorganisms which are being used in biological monitoring to determine the quality of river's water

Evaluation on palm-print ROI selection techniques for smart phone based touch-less biometric system

There are many methods have been carried out for human recognition such aspersonal identification number (PIN), password or ID card but all of these methods can beguessed, hacked or stolen. Palm-print verification system is a biometric technology which isdeveloped to authenticate person based on individual palm-print pattern. This paper presentsan initial effort to perform touch-less palm-print recognition system by considering theeffective way to extract the palm-print region of interest (ROI). The system starts with handimage collection using smart phone device. This project proposes two hand trackingalgorithms i.e. two point method and canny method so as to detect the peak and valley of thepalm. Afterwards, the desired ROI is selected and the palm-print ROI is stored in database forthe evaluation of their appropriateness to be used for the touch-less palm-print recognitiondata

Research on Mixed Matrix Estimation Algorithm Based on Improved Sparse Representation Model in Underdetermined Blind Source Separation System

The estimation accuracy of the mixed matrix is very important to the performance of the underdetermined blind source separation (UBSS) system. To improve the estimation accuracy of the mixed matrix, the sparsity of the mixed signal is required. The novel fractional domain time–frequency plane is obtained by rotating the time–frequency plane after the short-time Fourier transform. This plane represents the fine characteristics of the mixed signal in the time domain and the frequency domain. The rotation angle is determined by global searching for the minimum L1 norm to make the mixed signal sufficiently sparse. The obtained time–frequency points do not need single source point detection, reducing the calculation amount of the original algorithm, and the insensitivity to noise in the fractional domain improves the robustness of the algorithm in the noise environment. The simulation results show that the sparsity of the mixed signal and the estimation accuracy of the mixed matrix are improved. Compared with the existing mixed matrix estimation algorithms, the proposed method is effective

Electrocardiogram Biometrics Using Transformer’s Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification

The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not able to relate ECG collected during enrollment and ECG collected during classification. In this research, we propose the sequence pair feature extractor, inspired by Bidirectional Encoder Representations from Transformers (BERT)’s sentence pair task, to obtain a dynamic representation of a pair of ECGs. We also propose using the self-attention mechanism of the transformer to draw an inter-identity relationship when performing ECG identification tasks. The model was trained once with datasets built from 10 ECG databases, and then, it was applied to six other ECG databases without retraining. We emphasize the significance of the time separation between enrollment and classification when presenting the results. The model scored 96.20%, 100.0%, 99.91%, 96.09%, 96.35%, and 98.10% identification accuracy on MIT-BIH Atrial Fibrillation Database (AFDB), Combined measurement of ECG, Breathing and Seismocardiograms (CEBSDB), MIT-BIH Normal Sinus Rhythm Database (NSRDB), MIT-BIH ST Change Database (STDB), ECG-ID Database (ECGIDDB), and PTB Diagnostic ECG Database (PTBDB), respectively, over a short time separation. The model scored 92.70% and 64.16% identification accuracy on ECGIDDB and PTBDB, respectively, over a long time separation, which is a significant improvement compared to state-of-the-art methods

Score information decision fusion using support vector machine for a correlation filter based speaker authentication system

Abstract. In this paper, we propose a novel decision fusion by fusing score information from multiple correlation filter outputs of a speaker authentication system. Correlation filter classifier is designed to yield a sharp peak in the correlation output for an authentic person while no peak is perceived for the imposter. By appending the scores from multiple correlation filter outputs as a feature vector, Support Vector Machine (SVM) is then executed for the decision process. In this study, cepstrumgraphic and spectrographic images are implemented as features to the system and Unconstrained Minimum Average Correlation Energy (UMACE) filters are used as classifiers. The first objective of this study is to develop a multiple score decision fusion system using SVM for speaker authentication. Secondly, the performance of the proposed system using both features are then evaluated and compared. The Digit Database is used for performance evaluation and an improvement is observed after implementing multiple score decision fusion which demonstrates the advantages of the scheme

Proposte metodologiche per lo scavo di necropoli romane

Partendo dalle sperimentazioni attuate nelle più recenti campagne di scavo della necropoli romana di Sarsina viene proposta e illustrata una nuova metodologia di scavo stratigrafico degli ambiti funerari incentrata sull'individuazione e la documentazione dei piani di calpestio antichi e delle relative tracce materiali e rituali extratombal