Iris Recognition System Using Support Vector Machines

In recent years, with the increasing demands of security in our networked society, biometric systems for user verification are becoming more popular. Iris recognition system is a new technology for user verification. In this paper, the CASIA iris database is used for individual user’s verification by using support vector machines (SVMs) which based on the analysis of iris code as feature extraction is discussed. This feature is then used to recognize authentic users and to reject impostors. Support Vector Machines (SVMs) technique was used for the classification process. The proposed method is evaluated based upon False Rejection Rate (FRR) and False Acceptance Rate (FAR) and the experimental result show that this technique produces good performance

Design and Evaluation of a Pressure Based Typing Biometric Authentication System

The design and preliminary evaluation of a pressure sensor-based typing biometrics authentication system (PBAS) is discussed in this paper. This involves the integration of pressure sensors, signal processing circuit, and data acquisition devices to generate waveforms, which when concatenated, produce a pattern for the typed password. The system generates two templates for typed passwords. First template is for the force applied on each password key pressed. The second template is for latency of the password keys. These templates are analyzed using two classifiers. Autoregressive (AR) classifier is used to authenticate the pressure template. Latency classifier is used to authenticate the latency template. Authentication is complete by matching the results of these classifiers concurrently. The proposed system has been implemented by constructing users’ database patterns which are later matched to the biometric patterns entered by each user, thereby enabling the systemto accept or reject the user. Experiments have been conducted to test the performance of the overall PBAS system and results obtained showed that this proposed system is reliable with many potential applications for computer security

A real valued neural network based autoregressive energy detector for cognitive radio application

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application

Evaluating the effect of voice activity detection in isolated Yoruba word recognition system

This paper discusses and evaluates the effect of voice Activity Detection (VAD) in an isolated Yoruba word recognition system (IYWRS). The word database used in this paper are collected from 22 speakers by repeating the numbers 1 to 9 three times each. A hybrid configuration of Mel-Frequency Cepstral coefficient (MFCC) and Linear Predictive Coding (LPC) have been used to extract the features of the speech samples. Artificial Neural Network algorithms are then used to classify these features. An overall accuracy of about 60% has been achieved from the two proposed feature extraction methods

A real valued neural network based autoregressive energy detector for cognitive radio application

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application

The Rise of Internet of Things and Big Data on the Cloud: Challenges and Future Trends

Huge growth in the scale of data generated through cloud computing has been led to Internet of Things (IoT). Fog computing has been recently adopted to improve some features of cloud computing and makes cloud computing more attractive to users. Furthermore, it comes to improve some parameters such as latency, security and load network. The combination of cloud and fog computing is seen a new progress in distributed computing and the appropriate platform for the data. Fog computing is defined as a new paradigm that works at the edge of the network to improve the quality of the network. In this paper, the use of fog computing in cloud computing is reviewed in this work. The characteristics, architectures and discussions and the relationship is further elaborated. Additionality, recommendation for further research is discussed

Bioethanol Production from Low-Value Feedstocks: Wild Cocoyam, Waste Cassava Peels, and Waste Sugar Cane Molasses

Abstract: Bioethanol, produced by the anaerobic fermentation of carbohydrates, can be used as a renewable fuel, as vital ingredient in the production of beer, wine, or high-valued distillate alcoholic drink. Different plants have been installed in different parts of the world as carbon source to produce bioethanol. Feedstocks is a fundamental requirement for successful and efficient operations of these bioethanol manufacturing plants. One major challenge in choosing suitable feedstock is food versus fuel debate, that is, reducing to the barest minimum food crops serving as main source of food for human consumption. Thus, the focus of this review is to explore some crops rich in carbohydrate but less commonly consumed as food such as wild cocoyam, cassava peels and waste product of sugar refinery, sugar cane molasses as alternative feedstocks. In this review, the harvested wild cocoyam corms and cassava peels were washed, dried, ground and then made into a gelatinized solution to increase the surface area. The starch present in the slurry mixtures was then saccharified by the action of different hydrolytic enzymes, like alpha-amylase, protease, amylitic-TS, and amyloglucosidase. It was reported that the enzymatic hydrolysis of ground cocoyam and cassava was effective in yielding favorable levels of fermentable glucose. The saccharified wort was then inoculated with viable yeast strains to begin the fermentation process. On the other hand, sugar cane molasses considered highly rich in sugar content was converted to bioethanol using a gram negative, facultative anaerobic, rod shaped strain’’ Zymomonas mobilis’’ as the microorganism under anaerobic fermentation condition. The fermentation process varied for several days from 48 h to 168 h depending on the feedstock. Percent alcohol concentration produced from wild cocoyam sample was 12.90 % after 168 h of anaerobic fermentation, whilst sugar cane molasses recorded 9.3 % bioethanol content after 48 h of fermentation process. The percent alcohol recovered from waste cassava peel was 8.5 % after 96 h of fermentation. Keywords: Bioethanol, Anaerobic fermentation, wild cocoyam, molasses, and cassava peels. Title: Bioethanol Production from Low-Value Feedstocks: Wild Cocoyam, Waste Cassava Peels, and Waste Sugar Cane Molasses Author: Isah S., Ahiakwo J, Odusina A., Equere-Obong A., George J., Ojo E.M., Udoh S., Anwuchughum C., Edward A., Enahoro E., Salami A International Journal of Novel Research in Physics Chemistry & Mathematics ISSN 2394-9651 Vol. 10, Issue 3, September 2023 - December 2023 Page No: 1-19 Novelty Journals Website: www.noveltyjournals.com Published Date: 18-September-2023 DOI: https://doi.org/10.5281/zenodo.8355346 Paper Download Link (Source) https://www.noveltyjournals.com/upload/paper/Bioethanol%20Production-18092023-4.pdfInternational Journal of Novel Research in Physics Chemistry & Mathematics, ISSN 2394-9651, Novelty Journals, Website: www.noveltyjournals.co

Global analysis of data on the spin-orbit coupled A1Σu+A^{1}\Sigma_{u}^{+} and b3Πub^{3}\Pi_{u} states of Cs2

We present experimentally derived potential curves and spin-orbit interaction functions for the strongly perturbed $A^{1}\Sigma_{u}^{+}$ and $b^{3}\Pi_{u}$ states of the cesium dimer. The results are based on data from several sources. Laser-induced fluorescence Fourier transform spectroscopy (LIF FTS) was used some time ago in the Laboratoire Aim\'{e} Cotton primarily to study the $X ^{1}\Sigma_{g}^{+}$ state. More recent work at Tsinghua University provides information from moderate resolution spectroscopy on the lowest levels of the $b^{3}\Pi_{0u}^{\pm}$ states as well as additional high resolution data. From Innsbruck University, we have precision data obtained with cold Cs$_{2}$ molecules. Recent data from Temple University was obtained using the optical-optical double resonance polarization spectroscopy technique, and finally, a group at the University of Latvia has added additional LIF FTS data. In the Hamiltonian matrix, we have used analytic potentials (the Expanded Morse Oscillator form) with both finite-difference (FD) coupled-channels and discrete variable representation (DVR) calculations of the term values. Fitted diagonal and off-diagonal spin-orbit functions are obtained and compared with {\it ab initio} results from Temple and Moscow State universities

Investigation of the characteristics of geoelectric field signals prior to earthquakes using adaptive STFT techniques

An earthquake is one of the most destructive natural disasters that can occur, often killing many people and causing large material losses. Hence, the ability to predict earthquakes may reduce the catastrophic effects caused by this phenomenon. The geoelectric field is a feature that can be used to predict earthquakes (EQs) because of significant changes in the amplitude of the signal prior to an earthquake. This paper presents a detailed analysis of geoelectric field signals of earthquakes which occurred in 2008 in Greece. In 2008, 12 earthquakes occurred in Greece. Five of them were recorded with magnitudes greater than Ms = 5R (5R), while seven of them were recorded with magnitudes greater than Ms = 6R (6R). In the analysis, the 1st significant changes of the geoelectric field signal are detected. Then, the signal is segmented and windowed. The adaptive short-time Fourier transform (adaptive STFT) technique is then applied to the windowed signal, and the spectral analysis is performed thereafter. The results show that the 1st significant changes of the geoelectric field prior to an earthquake have a significant amplitude frequency spectrum compared to other conditions, i.e. normal days and the day of the earthquake, which can be used as input parameters for earthquake prediction

Probucol Release from Novel Multicompartmental Microcapsules for the Oral Targeted Delivery in Type 2 Diabetes

In previous studies, we developed and characterised multicompartmental microcapsules as a platform for the targeted oral delivery of lipophilic drugs in type 2 diabetes (T2D). We also designed a new microencapsulated formulation of probucol-sodium alginate (PB-SA), with good structural properties and excipient compatibility. The aim of this study was to examine the stability and pH-dependent targeted release of the microcapsules at various pH values and different temperatures. Microencapsulation was carried out using a Büchi-based microencapsulating system developed in our laboratory. Using SA polymer, two formulations were prepared: empty SA microcapsules (SA, control) and loaded SA microcapsules (PB-SA, test), at a constant ratio (1:30), respectively. Microcapsules were examined for drug content, zeta potential, size, morphology and swelling characteristics and PB release characteristics at pH 1.5, 3, 6 and 7.8. The production yield and microencapsulation efficiency were also determined. PB-SA microcapsules had 2.6 ± 0.25% PB content, and zeta potential of −66 ± 1.6%, suggesting good stability. They showed spherical and uniform morphology and significantly higher swelling at pH 7.8 at both 25 and 37°C (p < 0.05). The microcapsules showed multiphasic release properties at pH 7.8. The production yield and microencapsulation efficiency were high (85 ± 5 and 92 ± 2%, respectively). The PB-SA microcapsules exhibited distal gastrointestinal tract targeted delivery with a multiphasic release pattern and with good stability and uniformity. However, the release of PB from the microcapsules was not controlled, suggesting uneven distribution of the drug within the microcapsules