ISSN 2414-102X (Online)

E is pleased to present the sixth volume of ULAB's Journal of Science and Engineering (JSE). You are very welcome to read this issue of the ULAB Journal of Science and Engineering. Abstract – The aim of this work is to evaluate the performance of the noise robust speaker identification system using Kohonen self-organization mapping based neural network algorithm.

Linear Discriminant Analysis based dimensionality reduction technique has been used to reduce the dimension of the extracted speech features. After recording the speech signal, it is necessary to eliminate the noise from the speech signal. Since Sort Time Fourier Transform (STFT) is the appropriate method to analyze the speech characteristics, windowing technique has been used to reduce the effect of the spectral artifacts resulting from the framing process.

To reduce the dimension of the speech feature vector, Linear Discriminant Analysis (LDA) based dimension reduction technique has been used. The NOIZEUS speech database was used to measure the accuracy of the proposed speaker identification system. The results of the proposed speaker identification system are shown in Tables 1 to 9.

In this experiment, eight different real-world environmental noises are added to successfully evaluate the performance of the proposed speaker identification system.

Figure 1: Architecture of the proposed speaker identification system.

Intensity Modulated Direct Detection (IM/DD) When the optical power output of a source is varied in

Based on Monte Carlo simulations, mathematical models are developed for the temporal properties of optical pulse propagation through clouds. The simulation results strongly support the use of a double-gamma function model to best describe optical pulse propagation through clouds [15]. An FSO link normally consists of a transmitter, a channel which will be the medium of transmission and it will be atmospheric which includes cloud and finally a receiver to reproduce that transmitted signal.

These devices modulate and detect only the intensity of carrier waves and not their phase, implying that all transmitted signal intensities are non-negative. The binary input data is used to modulate the laser using intensity modulation and thus passes through the transmitted filter and then through the atmosphere where the impulse response has a greater impact.

Cumulus Cloud Model and Gamma Constant Cumulus clouds are generally located at the height of

The received optical signal is given by .. 6) where Ps is the received optical power and g (t) = h (t)p(t) is the received optical pulse shape superimposed on a number of bits and produce Inter Symbol Interference (ISI). In this section we compare the BER performance of the SISO optical communication system in the presence of cloud. We present the numerical results of the BER of the FSO link considering the cloud effect and without loss.

Figure 2 shows the optical pulse shape obtained for different carrier wavelengths for a transmission bandwidth of 1 GHz. All optical system performance can be measured in terms of power penalty for a better understanding. 9 shows the required optical power in dBm for various transmission bandwidths to maintain a bit error rate (BER) of 10-10. From Fig.

8, the optical power required (for cloud thickness of 200 m) to overcome the interference of intermediate symbols varies from about 55 dBm to 59 dBm for a wide range of transmission bit rates (1Gb/s-16 Gb/ s) for carrier wavelength λ=1.3 μm. In Figure 9, the optical power required (for cloud thickness of 250 m) to overcome intersymbol interference varies from 50 dBm to 53 dBm for a wide range of transmission bit rates (1Gb/s-16 Gb/s ) for the carrier wavelength of λ=1.3 µm.

C ONCLUSION

Mahfuzul Haque is currently working as a lecturer in the Department of Electrical and Electronics Engineering at the Bangladesh University of Business and Technology (BUBT). He previously worked as a lecturer in the Department of Information and Communication Technology at the Metropolitan University, Sylhet. Abu Jahid is currently working as a lecturer in the Department of Electrical and Electronic Engineering at the Bangladesh University of Business and Technology (BUBT).

He previously worked as a lecturer at MIST and Assistant Professor at the Department of Electrical &. Among several types of field-effect transistors, the universal gate junctionless nanowire FET (GAA-JL-NW-FET) is the most recently invented one. In this article, the temperature dependence of the threshold voltage of GAA-JL-NW-FET is analyzed for different channel materials such as Si, GaAs, InAs and InP.

From the simulation result, it is observed that the threshold voltage is minimum for InAs and decreases as the temperature increases for all the aforementioned channel materials. The bipolar junction transistor (BJT) contains two p-n junctions, and so does the metal-oxide-semiconductor field-effect transistor (MOSFET) and most modern transistors. The junction field-effect transistor (JFET) has only one p-n junction, and the metal-semiconductor field-effect transistor (MESFET) contains a Schottky junction.

With the continuous scaling of the device dimension, these bulk MOSFETs are facing serious challenges, such as increased gate leakage current and more serious short-channel effects [2]. Gate-all-around (GAA) nanowire transistors are considered a promising device structure to extend the scaling limit due to their superior gate controllability. On the other hand, the formation of unexpected source-drain junctions in conventional NW devices imposes severe challenges on doping techniques and thermal budget [3].

To reduce short channel effects, GAA-FETs are best as they provide the best all round channel control [4]. AREFIN: THE EFFECT OF TEMPERATURE ON THE THRESHOLD VOLTAGE OF A GATE-ALL-AROUND EFFECT-CONTACTLESS NANOWIRE TRANSISTOR 15. However, it is also slightly degraded by the high doping concentrations used in the channel.

2 GAA-JL-NW-FET 2.1 NW-FET

JL-NW-FET
GAA-JL-NW-FET
Testing the Significance of Correlation Coeffi- cient under Bivariate Normality
Testing the Significance of Correlation Coeffi- cient under Bivariate t-Distribution
Independently and Identically Distributed Ob- servations from N 2 ( , )
Uncorrelated and Identically Distributed Obser- vations from Bivariate T-Distribution
Division of labor in ants
Division of labor in swarm robotics
Specification of the model
Behavioral rules
Parameters used in this model
Robot Utilization

These results are clear indications of the material dependence on the threshold voltage of GAA-JL-NW-FET. V mS  Assuming that the observations come from a bivariate normal population, Bose in 1935 and Finny in 1938 derived the density function of the variance ratio. In this paper, we prove that if the sample observations are uncorrelated t-distributions, the distribution of the variance ratio remains the same.

The density function of elements A based on the UIBT model (2.7) is given by (2.9), where Acceptance of the null hypothesis does not imply independence unless the sample is from a bivariate normal distribution. In this section, we will prove that even if each of the sample observations X X1, 2, XN(N2) follows the identical bivariate t-distribution and has the sample model (2.7), the distribution of HU V/ remains the same as.

The distribution of the ratio between the estimates of the two variances in a sample from a bivariate normal population, Biometrika. Distribution of the correlation coefficient for the class of bivariate elliptic models, Canadian Journal of Statistics. The distribution of the ratio of covariance estimates in two samples drawn from normal bivariate populations, Biometrika.

In this section, we discuss how the number of participating robots affects the efficiency of the swarm system. This is particularly important as there is a notion of understanding in the literature that the increase in number of robots will improve the overall efficiency of the system. Swarm systems are characterized by a large number of redundant agents to improve the efficiency of the swarm.

Robot usage refers to the average number of robots used to transport boxes. It is the ratio of the average. However, if the robots are used too much, the efficiency of the system will decrease. If the number of robots is less than the optimal value, the average energy of the swarm will be lower.

Similarly, if the number of robots participating is more than the optimal value, the average energy of the swarm decreases again. This indicates that if there are too few robots in the world, they have to perform a lot of tasks and therefore the average energy of the swarm remains low.

Figure 4 depicts the GAA-JL-NW-FET with its circular cross-sectional and top views [16]

An Explicit Finite Difference Method

I NTRODUCTION

2.G OVERNING equation

N UMERICAL M ETHOD FOR G OVERNING EQUATION We consider the one–dimensional water pollution model
A LGORITHM FOR THE NUMERICAL SOLUTION To find the numerical solution of the model, we have to
C OMPUTATIONAL R ESULTS
C ONCLUSION

The concentration flux through a plane due to diffusion is the amount of concentration passing through this plane as a result of the diffusion process. According to Fick's law of diffusion, the concentration flux due to diffusion across any cross-section at a point a is proportional to the product of the cross-sectional area and the concentration gradient cx. The concentration flux due to advection across any cross-section at point x is proportional to the product of the velocity, cross-sectional area and concentration.

By coordinate transformation, the exact solution of the advection-diffusion equation in the unbound is given by [1]. Finite difference techniques for solving the one-dimensional advection-diffusion equation can be considered according to the number of spatial grid points involved, the number of time levels used, whether explicit or implicit. A finite difference method proceeds by replacing the derivatives in the differential equation with the finite difference approximations.

This gives a large algebraic system of equations to solve instead of a differential equation, something that is easily solved on a computer. That is, the solution at the new time step (n+1) at a spatial node i is an average of the solutions at the previous time step at spatial nodes i-1, i, and i+1. This means that the extreme value of the new solution is the average of the extreme values of the two previous solutions at three consecutive nodes.

ALGORITHM FOR THE NUMERICAL SOLUTION To find the numerical solution of the model, we must To find the numerical solution of the model, we must collect some variables which are provided in the following algorithm. Now, we calculate the relative error of the explicit difference scheme using the FTBSCS technique which is determined by the relative error in the L -norm as. Numerical calculation of ADE is presented using explicit finite difference methods from FTBSCS techniques and compared to an exact solution of ADE.

To test the accuracy of the numerical scheme using the FTBSCS technique for ADE, we implement a model for some artificial data for pollutant transport in river water. The curve marked with “solid line” shows the concentration profile for 1 minute (left), the curve visible with “dashed line” shows the concentration profile for 2 minutes (left). The "dotted line" curve shows the concentration profile for 3 minutes, the curve is visible with the "dashed line".