Md. Al Mehedi Hasan

Face recognition using PCA and SVM

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.

Comparative analysis the performance of AODV, DSDV and DSR routing protocols in wireless sensor network

In this paper, we have analysed the comparative performance of AODV, DSDV and DSR routing protocols in different wireless sensor network scenarios (i.e. scenarios where number of nodes changes or mobility of nodes changes). The performance matrix includes Packet Delivery Ratio, Throughput, End to End Delay, and Normalized Routing Load. We mainly try to address the behaviour of the protocols in different scenarios. Simulation results show that different routing protocol performs well in different scenarios and good for specific performance metrics. For example, DSDV perform better in the high density networks or the network with strict requirement on time whereas DSR performs well in smaller network. AODV is more adaptable in the networks with high throughputs and preferable for low loss rate environment.

Effect of signal length in cross-correlation based underwater network size estimation

Signal length possesses a very important role in size estimation of underwater wireless sensor network (UWSN). As size estimation is very tough in UWSNs using conventional protocol techniques, a cross-correlation based technique is introduced to estimate the number of nodes. In UWSN, The greater the signal length, the more energy is required to perform the estimation. In this paper, we observe the effect of signal length (Ns) over the number of estimated nodes (N) in a spherical region of an underwater acoustic sensor network (UASN) using three sensors and investigate the error in node estimation for different Ns. As the theoretical required Ns is infinity (Ns = 106 samples is used in simulation), using minimum Ns how it is possible to go through a successful estimation process i.e., accurate node estimation is being observed and discussed.

Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction

Summary For the importance of protein subcellular localization in different branch of life science and drug discovery, researchers have focused their attentions on protein subcellular localization prediction. Effective representation of features from protein sequences plays most vital role in protein subcellular localization prediction specially in case of machine learning technique. Single feature representation like pseudo amino acid composition (PseAAC), physiochemical property model (PPM), amino acid index distribution (AAID) contains insufficient information from protein sequences. To deal with such problem, we have proposed two feature fusion representations AAIDPAAC and PPMPAAC to work with Support Vector Machine classifier, which fused PseAAC with PPM and AAID accordingly. We have evaluated performance for both single and fused feature representation of Gram-negative bacterial dataset. We have got at least 3% more actual accuracy by AAIDPAAC and 2% more locative accuracy by PPMPAAC than single feature representation.

Performance of Wavelet Transform on Models in Forecasting Climatic Variables

An attempt has been made to show whether the recently developed wavelet transformation in forecasting the climatic time series in Bangladesh improves the performance of existing forecasting models, such as ARIMA. These models are applied to forecast the humidity of Rajshahi, Bangladesh. Then the wavelet transformation has been used to decompose the humidity series into a set of better-behaved constitutive series. These decomposed series and inverse wavelet transformation are used as a pre-processing procedure of forecasting humidity series using the same models in two approaches. Finally, the forecasting ability of these two models with and without wavelet transformation is compared using the statistical forecasting accuracy criteria. The results show that the use of wavelet transformation as a pre-processing procedure of forecasting climatic time series improves the performance of forecasting models. The reason is the better behavior of the constitutive series for the filtering effect of the wavelet transform.

Development of a highly optimized Preemptive Real Time Operating System (pRTOS)

A real-time operating system (RTOS) is software which ensures that time critical events are processed as efficiently as possible. In this paper, an attempt has been taken to develop a real time operating system, named preemptive real time operating system (pRTOS), in which all of the important issues regarding to a real time application have been considered. In this pRTOS, strictly preemptive scheduling algorithm has been used. This scheduling policy makes sure that important tasks are handled first and the less important later. The Bitmap technique has been used to find out the highest priority task from the unsorted ready list. The complexity of this technique for selecting the highest priority task is O(1), which is much faster than the linear search technique having complexity of O(n). This pRTOS can support 64 priority levels ranges from 0 to 63. In addition with this, it is a highly configurable RTOS. Moreover, it can be adopted in a board range of hardware platform, say, Intel x86, MIPS, Hitachi SH, Power PC and Strong ARM processors. This RTOS has been tested on Intel x86 and from the obtained result, it has been found that our developed pRTOS can be used for various application, say, for automated industrial systems, control-systems, high-tech electronics/electrical products and home applications.

Gram-positive bacterial protein subcellular localization prediction using features fusion strategy

Prediction of protein subcellular localization is the most challenging field for the researchers because of its importance in different branch of molecular biology and drug discovery. Last two decades, a large number of machine learning approaches have been tested into sequence based features for the prediction of subcellular localization. Single features like amino acid composition (AAC), pseudo amino acid composition (PseAAC) and physiochemical property model (PPM)) contain insufficient information due to their single perspectives. To overcome this problem, the main contribution of our work is to propose two feature fusion representations AACPPM and PAACPPM which can be fused PPM with AAC and PseAAC respectively. Support Vector Machine (SVM) is applied as a classifier on to both single and fused feature representations of Gram-positive bacterial dataset. The actual accuracy of AACPPM is 72.4% which is 2% higher than single feature representations and 6% higher than X. Qu et al [1]. The locative accuracy of both AACPPM and PAACPPM is 73.2% which is also 2% higher than single feature representations.

An algorithm for PAPR reduction by SLM technique in OFDM with hadamard matrix row factor

This paper is concerned with the performance improvement of PAPR reduction of orthogonal frequency division multiplexing (OFDM) using selective mapping based design. Note that OFDM is one of the most proficient multi-carrier transmission scheme which has been implemented in long term evolution (LTE) downlink. However peak to average power ratio (PAPR) is the main problem with OFDM, therefore in this paper we have proposed a reduction procedure of the PAPR by using selective mapping technique. Here we have used hadamard matrix row factor as its phase sequence to investigate the reduction of the PAPR. Our results show that the proposed method with the hadamard matrix row factor significantly enhances the PAPR performance.