Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach

The human liver disorder is a genetic problem due to the habituality of alcohol or effect by the virus. It can lead to liver failure or liver cancer, if not been detected in initial stage. The aim of the proposed method is to detect the liver disorder in initial stage using liver function test dataset. The problem with many real-world datasets including liver disease diagnosis data is class imbalanced. The word imbalance refers to the conditions that the number of observations belongs to one class having more or less than the other class(es). Traditional K- Nearest Neighbor (KNN) or Fuzzy KNN classifier does not work well on the imbalanced dataset because they treat the neighbor equally. The weighted variant of Fuzzy KNN assign a large weight for the neighbor belongs to the minority class data and relatively small weight for the neighbor belongs to the majority class to resolve the issues with data imbalance. In this paper, Variable- Neighbor Weighted Fuzzy K Nearest Neighbor Approach (Variable-NWFKNN) is proposed, which is an improved variant of Fuzzy-NWKNN. The proposed Variable-NWFKNN method is implemented on three real-world imbalance liver function test datasets BUPA, ILPD from UCI and MPRLPD. The Variable-NWFKNN is compared with existing NWKNN and Fuzzy-NWKKNN methods and found accuracy 73.91% (BUPA Dataset), 77.59% (ILPD Dataset) and 87.01% (MPRLPD Dataset). Further, TL_RUS method is used for preprocessing and it improved the accuracy as 78.46% (BUPA Dataset), 78.46% (ILPD Dataset) and 95.79% (MPRLPD Dataset).

     

Interference aware resource allocation for hybrid hierarchical wireless networks

This paper addresses the problem of interference aware resource allocation for OFDMA based hybrid hierarchical wireless networks. We develop two resource allocation algorithms considering the impact of wireless interference constraints using a weighted SINR conflict graph to quantify the interference among the various nodes: (1) interference aware routing using maximum concurrent flow optimization; and (2) rate adaptive joint subcarrier and power allocation algorithm under interference and QoS constraints. We exploit spatial reuse to allocate subcarriers in the network and show that an intelligent reuse of resources can improve throughput while mitigating interference. We provide a sub-optimal heuristic to solve the rate adaptive resource allocation problem. We demonstrate that aggressive spatial reuse and fine tuned-interference modeling garner advantages in terms of throughput, end-to-end delay and power distribution.     

Real world representation of a road network for route planning in GIS

This paper addresses a methodology to properly represent a road network in the geographic information system (GIS) for network analysis. Over the years, the real world has become too complex to model properly within a given information system, such as GIS. Ideally, when the real world is represented as accurately as possible, a GIS can answer a question in its virtual world that coincides with the exact answer in the real world. However, existing methods related to impedance modeling for each segment of a road network in a route planning analysis that includes only a distance or time variable do not give proper results. Hence, this study investigates how a road network can represent the real world in a GIS and offer route planning tools. To address this, first, additional realistic variables are taken into account. These include weather, sight-seeing information, road type, and so on. Second, to combine these variables, an impedance model (IM) using the analytical hierarchical process (AHP) method is proposed. Finally, all of the models are implemented and verified with a sensitivity analysis. The models were successfully implemented in this work. All of the paths of the route planning analysis were successfully matched with the drivers’ paths that would normally be chosen in reality. It is anticipated that the use of other techniques such as analytical network process (ANP) in addition to AHP would be useful to overcome the aforementioned problem.     

Survey of GPU Based Sorting Algorithms

Parallel sorting algorithms are widely studied nowadays. After the introduction of parallel processors such as graphics processing unit (GPU) and easy to use parallel programming languages such as CUDA and OpenCL, literature on parallel sorting algorithms has become vast and richer with new ideas and techniques applied to solve the famous problem of sorting. This paper presents a survey of GPU based sorting algorithms. Four sorting algorithms have been selected for this survey: Radix sort, Merge sort, Sample sort and Quick sort. Methods used in those algorithms are described in brief. The performance of these algorithms as claimed by their authors is also presented. A comparative analysis based on the literature is depicted.     

Comparative study of artificial neural network for classification of hot and cold recombination regions in Saccharomyces cerevisiae

At the chromosomal level of evolution, recombination is a major factor for genetic variations. However, recombination does not occur with equal frequency at various regions of genome. The recombination has the tendency to occur at specific region with higher frequency and with low frequency at other regions, and former regions are named as hot recombination regions whereas later are called cold regions for recombination. In this paper, we have developed supervised machine learning-based models using artificial neural network, support vector machine and Naïve Bayes for efficient and effective classification of such hot and cold recombination regions based on the nucleotide composition of sequences. All models were validated and tested using tenfold cross-validation. Furthermore, neural network model was validated using leave one out and random sampling techniques in addition to tenfold cross-validation. Moreover, models were evaluated using receiver-operating curve. Our results indicate that artificial neural network achieves the best result.

     

FPGA Implementation of Cross Virtual Concatenation Transmitter/ Receiver for Data Transmission over Next Generation SDH Systems

Cross Virtual Concatenation is the new technique proposed for bandwidth efficient transmission of data over SDH networks. SDH networks came into existence for reliable voice transmission. As the demand of data traffic grew in wide area networks, new technologies were developed and standardized for data transmission over SDH networks. The technologies used namely, GFP (generic framing procedure), VCAT (virtual concatenation) and LCAS (link capacity adjustment scheme) enable network operator to provide integrated voice and data services over their legacy SDH infrastructure. Data packets are encapsulated using framing protocols GFP. VCAT is a process of distributing the GFP framed data payload in number of virtual channels of same capacity forming a Virtually Concatenated Group (VCG). LCAS is used for dynamic bandwidth allocation. LCAS enhances the VCAT scheme with hitless in service addition and removal of VC’s to/from the VCG.VCAT combines homogeneous virtual channels together which in some cases limits the performance of VCAT. This paper describes the implementation of new concatenation technology named cross virtual concatenation (CVC), which combines heterogeneous VC’s together to utilize the SDH bandwidth more efficiently. CVC implementation requires only end node equipments to be upgraded as VCG members travel through the link similar to the conventional VCAT. This paper proposes FPGA implementation of transmitter and receiver circuits for 100 Mbps Ethernet data transmission over next Generation SDH systems using CVC, where two types of VC’s namely VC-3 and VC-12 are used for data transmission. Total Transmission delay is calculated as 125 μs. There is no complexity added at the receiver side due to this delay. The receiver is designed for 32 ms differential delay compensation which can be increased up to maximum 256 ms by increasing the buffer size at the receiver.     

Atomic scale modeling of electrically doped p-i-n FET from adenine based single wall nanotube

The Field Effect Transistor (FET) characteristics has been observed from a single-walled Adenine nanotube device using Density Functional Theory associated with Non Equilibrium Green’s Function based First Principle approach. This device is electrically doped which shows both n and p channel characteristics of a p-i-n FET. This device is designed and originated from a single-walled biomolecular nanotube structure. The p and n regions have been induced at the two ends of the device using electrical doping process. Thus both n and p channel current-voltage response can be obtained within a single nano-scale device at room temperature operation. The device is 3.35 nm long and 1.4 nm wide. The quasi-ballistic quantum transmission property reveals impressive and almost ideal current-voltage characteristics of the FET. Highest Occupied Molecular Orbital (HOMO) and Lowest Unoccupied Molecular Orbital (LUMO) gap reveals the possibility of quasi-ballistic coherent transmission of the device. The electronic properties based on Molecular Projected Self-consistent Hamiltonian are analyzed using Hilbert space spanned basis functions. The maximum tunneling current observed for the bio-molecular FET is 15.9 μA for n-channel and 13.8 μA for p-channel. The device is operated in atomic scale regime with 1000 THz frequency. The present results reveal the role of quantum-ballistic tunneling phenomenon in the current-voltage characteristics and channel conductance properties of the bio nanotube structure, which is useful in future generation nano-electronics.     

A review of particle swarm optimization and its applications in Solar Photovoltaic system

Particle swarm optimization is a stochastic optimization, evolutionary and simulating algorithm derived from human behaviour and animal behaviour as well. Special property of particle swarm optimization is that it can be operated in continuous real number space directly, does not use gradient of an objective function similar to other algorithms. Particle swarm optimization has few parameters to adjust, is easy to implement and has special characteristic of memory. Paper presents extensive review of literature available on concept, development and modification of Particle swarm optimization. This paper is structured as first concept and development of PSO is discussed then modification with inertia weight and constriction factor is discussed. Issues related to parameter tuning, dynamic environments, stagnation, and hybridization are also discussed, including a brief review of selected works on particle swarm optimization, followed by application of PSO in Solar Photovoltaics.     

Vinyl carbazole (VC) functionalized mesoporous silica polymer nanocomposites (SBA/VC) for the antibacterial activity studies

Vinyl carbazole (VC) functionalized ordered mesoporous silica polymer nanocomposites (SBA/VC) were synthesized by in situ radical polymerization of monomers inside the mesoporous framework and characterized for the antibacterial activity studies against gram positive and gram negative bacteria. Powder X-ray diffraction and N₂ adsorption isotherms of SBA/VC nanocomposites showed the presence of mesoporous nature. The antimicrobial activity results showed increasing trend with the increase in the concentration of vinyl carbazole (VC) and the maximum antibacterial activity was achieved with SBA/VC₆₄ nanocomposites.     

Leading One Detection Hyperbolic CORDIC with Enhanced Range of Convergence

This paper focuses on developing an area efficient hyperbolic Coordinate Rotation Digital Computer (CORDIC) algorithm with performance improvement. The algorithm eliminates the need of scale factor calculation in the Range of Convergence (ROC). At the same time the range of convergence offered is higher than the conventional CORDIC ROC in the hyperbolic rotation mode. Being the only kind of algorithm in hyperbolic rotation with sign sequence μ?=?1 always, one complete operation requires just 5 iterations. Thus the pipelined implementation has 5 stages which provides a 50% increase in throughput in comparison to conventional CORDIC. As far as the area improvement is considered, 16-bit processor can be realized using 56% less number of full adders required by Flat-CORDIC. The x and y datapath are based on series expansion of hyperbolic functions. The complete algorithm design along with pipelined architecture implementation is detailed.