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

The Role of Workplace Enviousness on Individual Productivity and Organizational Behavior

As a result of its negative consequences for college academics, workplace envy is one of the important areas to study and investigate its results. Thus, this study attempts to find out individuals’ productivity affected by envy in the workplace (CWBI) and organizational citizenship behavior directly benefi ts individuals (OCBI) in the context of Iraqi colleges. A survey was conducted among the lecturers at Baghdad University’s colleges in Iraq, with 182 lecturers representing a response rate of 50%. The researcher used two-step approach with partial least squares-structural equation modeling by Smart PLS to test the hypotheses. As anticipated, the research findings point that workplace envy positively influenced CWBI (accepting H1). Furthermore, results indicated that workplace envy negatively influenced OCBI (accepting H2). The results of this research have various implications for colleges in general and Iraqi colleges in particular. The scientific impact that the results of the present study will contribute to practitioners is demonstrated by highlighting the factors that lead to reducing CWBI and strengthening OCBI in colleges. To enhance the OCBI and minimize the CWBI in colleges, it is advised that the management should encourage academic cooperation and create a work environment suitable for the academics. This can be achieved by forming research teams to work on joint scientific projects and by allowing qualified academics to participate in teaching, supervising postgraduate students, and discussion committees. Providing positive organizational support can motivate the academics to perform better, exhibit responsible behavior, and become productive members of the college community

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 Frequency Bin Analysis of Distinctive Ranges Between Human and Deepfake Generated Voices

Deepfake technology has advanced rapidly in recent years. The widespread availability of deepfake audio technology has raised concerns about its potential misuse for malicious purposes, and a need for more robust countermeasure systems is becoming ever more important. Here we analyse the differences between human and deepfake audio and introduce a novel audio pre-processing approach. Our analysis aims to show the specific locations in the frequency spectrum where these artefacts and distinctions between human and deepfake audio can be found. Our approach emphasises specific frequency ranges that we show are transferable across synthetic speech datasets. In doing so, we explore the use of a bespoke filter bank derived from our analysis of the WaveFake dataset to exploit commonalities across algorithms. Our filter bank was constructed based on a frequency bin analysis of the WaveFake dataset, we apply this filter bank to adjust gain/attenuation to improve the effective signal-to-noise ratio, doing so we reduce the similarities while accentuating differences. We then take a baseline performing model and experiment with improving the performance using these frequency ranges to show where these artefacts lie and if this knowledge is transferable across mel-spectrum algorithms. We show that there exist exploitable commonalities between deepfake voice generation methods that generate audio in the mel-spectrum and that artefacts are left behind in similar frequency regions. Our approach is evaluated on the ASVSpoof 2019 Logical Access dataset of which the test set contains unseen generative methods to test the efficacy of our filter bank approach and transferability. Our experiments show that there is enhanced classification performance to be gained from utilizing these transferable frequency bands where there are more artefacts and distinctions. Our highest-performing model provided a 14.75% improvement in Equal Error Rate against our baseline model

Optimization of Generalized Multichannel Quantum Defect reference functions for Feshbach resonance characterization

This work stresses the importance of the choice of the set of reference functions in the Generalized Multichannel Quantum Defect Theory to analyze the location and the width of Feshbach resonance occurring in collisional cross-sections. This is illustrated on the photoassociation of cold rubidium atom pairs, which is also modeled using the Mapped Fourier Grid Hamiltonian method combined with an optical potential. The specificity of the present example lies in a high density of quasi-bound states (closed channel) interacting with a dissociation continuum (open channel). We demonstrate that the optimization of the reference functions leads to quantum defects with a weak energy dependence across the relevant energy threshold. The main result of our paper is that the agreement between the both theoretical approaches is achieved only if optimized reference functions are used.Comment: submitte to Journal of Physics

An Efficient Inventory Model-Based GA For Food Deterioration Products In The Tourism Industry

Background: The inventory control practice of deteriorating food products that are subject to an expiration date is a challenging process. Inappropriate inventory control practice leads to substantial waste of products and significant holding and purchasing costs. Purpose: This paper aims to develop an inventory control model-based Genetic Algorithm (GA) to minimize the Total Annual Inventory Cost (TAIC) function developed explicitly for the proposed model. Methodology: GA is used and tailored to provide the best reorder level of deteriorating food products. A case study of one of the five-star hotels in Iraq is conducted, followed by a sensitivity analysis study to validate the proposed model for varying reorder levels. Results and Conclusion: A minimum inventory cost is obtained with an optimum reorder level achieved by running GA. It is concluded that the optimal reorder level provided by the proposed GA minimized the monthly inventory cost of products

Preliminary geotechnical characterization of a site in southwest Nigeria using integrated electrical and seismic methods

Geophysical investigation using Vertical Electrical Sounding (VES), Electrical Resistivity Tomography (ERT) and Seismic Refraction at a proposed conference center site along Ajibode-Labani road, Ibadan, southwestern Nigeria has been carried out. The investigation aims at characterizing and delineating the subsurface strata to understand the weathered profile at the site. Understanding the weathered profile is essential in determining the suitability of the site for engineering construction of the future conference center. A total of 25 VES and 10 ERT profiles were acquired in a systematic grid pattern using both Schlumberger andWenner configurations with Allied omega terrameter. TheVES data were processed and analyzed using WinResist and the ERT data were inverted using RES2DINV. The data were combined to form a 3-D data set of the site and RES3DINV was used to produce the depth slices. Seismic refraction data were also acquired with an ABEM seismograph and processed using SeisImager and Fajseis software. Seismic data were used in understanding the velocity distribution and thickness. The results of VES, ERT and seismic refraction show good correlation. Four sub-surface layers were delineated: top layer of reworked sand, clayey sand/ lateritic hard pan, clay/ sandy clay and fracture/ fresh basement. The 3-D model permits a pictorial view of the sub-surface in relation to materials that overlie the basement. The thickness of unconsolidated materials to bedrock varies from 2.7 m to 12.2 m which revealed inhomogeneity in weathering under the shallow sub-surface. It is found that the integrated geophysical tool is well suited to characterize and delineate sub-surface structure (weathered profile) for engineering site characterization

Geonets and Geotextiles as Leachate Containment Materials in Landfills: System Dynamics Modeling Perspective

The effectiveness of geonet, geotextile and their composite as containment materials of landfill leachate has been examined in this paper through the use of system dynamics modeling method. The study area is Oriire Local Government Area of Oyo State, Nigeria. Three materials were studied, which include: GN (geonet), GT (geotextile) and GC (geocomposite). The water absorption, hydraulic conductivity, porosity and thickness were the major properties studied in these liners. Governing equations coded in Visual Basic Computer Programming Language was employed in developing a model. Validation of the model was done with data on the study area. The interrelationship of the properties and the breakthrough times for each material was found through the STELLA 9.1.4 software application. This research showed that the effectiveness of the studied of the order GC < GN < GT. GT is, therefore, recommended for use as landfill liners in the study area

Geoelectric Survey of Foundation Beds of the Proposed Faculty of Engineering Building, OSUTECH Permanent Site, Okitipupa, Nigeria

Geoelectric resistivity method was employed to characterize the geo-materials at Ondo State University of Science and Technology (OSUSTECH) Okitipupa, Dahomey Basin, Nigeria, for suitability for foundation purposes. The methods involved Constant Separation Traversing (CST) using Wenner array and Vertical Electrical Sounding (VES) using Schlumberger array. The data obtained were processed with Ipi2win and excel software. The results showed that the subsurface structures were made up of lateritic topsoil with resistivity varying from 85 Ohm-m to 612 Ohm-m and thickness varying from 0.5 to 2.14 m; clayed sand with resistivity varying from 295 to 2,587 ohm-m and thickness vary from 0.67 to 3.4; clay with resistivity varying from 10 to 350 ohm-m and thickness varying from 3.8 to 26 m; and sand with resistivity ranging from 383 ohm-m to 59,707ohm-m. The clayed sand would have been the best layer to host the foundation because of its depth to the surface but it is generally less than 1.5 m and underlay by thick column of clay. The only competent layer that can host the foundation of high-rise building is the sand layer, therefore, the building foundation should be piled to the sand layer or pilling should be suspended within the thick column of clay

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