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Data collection is a considerable operation in Wireless sensor network (WSN) to minimize the energy dissipation of sensor nodes and thereby increasing the network lifetime. A lot of research works have been designed for data gathering in WSN. A multi-mobile agent itinerary planning based energy and fault aware data aggregation and zone-based energy-aware data collection routing protocol was introduced to obtain better energy usage and data delivery in WSN. However, data aggregation accuracy was not increased and energy consumption during data aggregation was not reduced. In order to overcome such limitations, an Energy Aware Decision Stump Linear Programming Boosting Node Classification based Data Aggregation (EADSLPBNC-DA) Model is proposed. Initially, residual energy for every sensor node is calculated for performing the node classification. After that, linear programming boosting classification (LPBC) model is applied in EADSLPBNC-DA Model which increased the margin between the training samples (i.e., sensor nodes) of different classes. The LPBC model constructs a strong classifier by combining number of weak decision stump result. Subsequently, strong classifier in EADSLPBNC-DA Model accurately classifies each input sensor node as higher energy or lower energy node which reduced the misclassification error. Then, the lesser energy sensor nodes in WSN transmit the data packets to the neighboring higher energy sensor nodes through calculating the distance by Manhattan distance formula. Finally, the sink node gathers the data packets from the higher energy sensor nodes. As a result, EADSLPBNC-DA Model attains energy efficient data collection in WSN. Experimental evaluation of EADSLPBNC-DA Model is carried out on factors such as energy consumption, delay, data aggregation accuracy, network lifetime and data aggregation time with respect to number of sensor nodes and number of data packets. From the simulation results, proposed EADSLPBNC-DA Model significantly reduces the energy consumption by 22%, delay by 35% and data aggregation time by 28% when compared to the existing techniques. In addition, the data aggregation accuracy and network lifetime gets increased by 13% and 10% respectively compared to state-of-the-art works.

Objective: A massive Multiple Input and Multiple Output (MIMO) receiver utilizes the proposed detection algorithm to reduce the complexity. Methods: The existing research work namely Noise and Relevancy aware Low Complexity Detection (NRLCD) algorithm for massive MIMO receiver utilizes normalized cross correlation based pruning strategy to viably evacuate uncorrelated signals. However, the existing research work still has more complexity with increasingly number of iterations to find more relevant signal vector. In this research paper, it is proposed to investigate execution of massive MIMO system utilizing Continuous Phase Modulation (CPM) modulation which is used to carry out signal modulation. Then, Hybrid Particle Swarm Optimization-and-Branch-and-Bound (Hybrid PSO-BB) algorithm is proposed for low complexity detection. Findings: CPM demonstrated to give superior performance over Quadrature Amplitude Modulation (QAM) technique with the presence of phase noises. Hybrid PSO-BB is anticipated; in which the best attainable solution were found and renewed using PSO. The performance assessment of the proposed research work and existing methods is done under Adaptive Additive Gaussian Channel (AWGN) using MATLAB Communication tool box. Improvements: From the simulation results, it is inferred that the Hybrid PSO-BB algorithm is superior to the existing methods in-terms of Bit Error Rate (BER) performance and complexity. Keywords: Bit Error Rate (BER), Continuous Phase Modulation (CPM), Low Complexity, Massive Multiple Input and Multiple Output (MIMO), Particle Swarm Optimization (PSO)

The increase in number of disease challenges medical practitioner in making right decisions. As most diseases have the same set of symptoms, the medical practitioner struggles to take decision on recognizing disease as well as right treatment methods. A number of approaches are available for the disease identification and providing treatment, but finding the right approach is what matters. To solve this issue, an inter-/intra-disease impact, the disease-based prediction and recommendation generation method, is presented. The method first reads the input data set and produces a series of clusters with the samples obtained. In the second level, the method estimates inter-disease impact measure and intra-disease impact measure on various disease classes for every data point of the data collection. Using these two measures, the method computes I2-Disease weight for each data point in assigning a label to the data points. For the classification, the method estimates symptomatic disease weight based on inter-/intra-symptom correlation assessment. Based on the selected disease class, a set of treatment samples is populated and ranked according to their curing rate.

Orthogonal Frequency Division Multiplexing (OFDM) is one of the upcoming techniques for achieving large data rates in 4G/5G wireless communication. In OFDM system, the data are transmitted over sizable subcarriers and modulated with low symbol rate. High-peak-to-average-power ratio (PAPR) is the major disadvantage in OFDM systems that results in out of band energy distortion, intermodulation among the subcarriers, and saturation in power amplifiers. This paper deals with the analyzing the PAPR reduction in OFDM systems using digital communication codes. Adaptive Huffman codes (AHC) is selected and applied in OFDM transmitter section for reducing the high peak signals. The encoding part selected in AHC is Vitter’s algorithm. QAM16 and PSK mapping schemes are used for modulating the data. Simulation is carried out for the proposed work using MATLAB software, and PAPR results are showed in Complementary Cumulative Distribution Function (CCDF) plot. Further, the PAPR results are compared with Huffman coding and using no codes. Simulation results show that for all subcarriers, the PAPR reduction of adaptive Huffman codes dominates the Huffman codes and using no codes.

The machine learning uses statistical algorithms to provide the ability to learn with and without data programming. Image classifier is used to categorize a subject or an object present in an image into predefined classes. A statistical algorithm used in numerous fields like pattern classification, regression, control systems, identification, and prediction. This research paper presents a novel approach performance comparison of machine learning classifiers used for the classification of ultrasonic fetal images. It presents Gabor feature extraction and various classification techniques to classify three different classes of ultrasound images. The proposed method is presented as follows. Initially, the Gabor features are obtained from raw images. To remove redundancy in features and dimensionality reduction principal component analysis (PCA) is applied. Finally, the features obtained from PCA are fed to the various machine learning classifiers and its performances are evaluated. The simulations results are carried out using MATLAB image processing toolbox. From the results, it is observed that decision tree (DT) algorithm and multi-layer perceptron (MLP) perform to be closer and this classifier outperforms all other classifiers.

Traffic engineering (TE) is one of the effective methods of IP routing used in Service provider networks when routers are overloaded due to uneven utilization of links. Multiprotocol Label Switching (MPLS) is a routing technique used in computer networks that guides data packets from one node to other node based on the concept of short path labels and speeding the traffic flows to support multiple customers. This project describes the design and Implementation of MPLS VPN (Virtual Private Network) along with dedicated traffic tunneling known as MPLS-TE with the help of Resource Reservation Protocol (RSVP), Open Shortest Path First protocol (OSPF) and Border gateway protocol (BGP) for IPv6 customers. The network is designed and simulated in GNS3 platform with Wireshark and various QoS performance metrics of the network are calculated to analyze the operational behavior of the network. A performance comparison of tunnel enabled and disabled MPLS VPN network is executed as an additional objective.

The most difficult task in agriculture is watering the fields. The different types of watering system are drip system, nozzles type, tube method and sprinkler system. This paper focus about the drip system. The work specifies the moisture level sensing of the crops and supply the require water when needed. The components included in this work are ATmega 328 Microcontroller, GSM Module, Humidity sensor and soil moisture sensor. A prototype has been developed to protect the plants or crops more self sufficient from watering and sunlight. The model reports the status of current condition and also reminds the need of water through GSM module. The prototype model is a loop closed control system is designed to continuously monitor the humidity, temperature, soil moisture level and controlling the irrigation systems from the output of the pumping unit.

Wireless sensor network consists of set of source, sink nodes and communication devices to interact without any support of the infrastructure. Unlike wired networks, the challenges faced in mobile ad-hoc networks possessed such as security design, network infrastructure, stringent energy resources and network security issues. The need to these security issues is much focused in overcoming the challenges in WSN. Here the perpetual work focuses on the secure communication using a novel defense framework named role based control model is proposed to analyze the network flow and to identify the misbehaving nodes. The communication is performed based on the cluster of immense size these confided in node(s) will most likely be passing on together, in the meantime allowing or section entry/correspondence of the unauthorized node(s) to continue keeping up a constant, tied down, dependable communication of versatile nodes. The simulation is performed using network simulator where the network parameters such as throughput, packet delivery ratio, delay and packet loss are analyzed to identify the malicious nodes.

In today’s world more death occurs due to not immediate treatment for bed ridden patients. It also requires more man power. This document presents the model, installation and looks of a hardware and software platform for health monitoring system for patients. The main purpose of this system is to set up a system that can watch over bedridden patient’s health. This project depends on both hardware and software. It is developed using sp02, temperature, heartbeat connected to a raspberry pi micro controller, which get the sensor values and process it in machine learning process to produce a dosage prediction. It also intimates the doctor by triggering an automatic email. In addition, one can know the patients records and prediction of dosage from anywhere using the mobile application linked with it.

Need of Digital Signal Processing (DSP) systems which is embedded and portable has been increasing as a result of the speed growth of semiconductor technology. Multiplier is a most crucial part in almost every DSP application. So, the low power, high speed multipliers is needed for high speed DSP. Array multiplier is one of the fast multiplier because it has regular structure and it can be designed very easily. Array multiplier is used for multiplication of unsigned numbers by using full adders and half adders. It depends on the previous computations of partial sum to produce the final output. Hence, delay is more to produce the output. In the previous work, Complementary Metal Oxide Semiconductor (CMOS) Carry Look-ahead Adders (CLA) and CMOS power gating based CLA are used for maximizing the speed of the multiplier and to improve the power dissipation with minimum delay. CMOS logic is based on radix 2(binary) number system. In arithmetic operation, major issue corresponds to carry in binary number system. Higher radix number system like Quaternary Signed Digit (QSD) can be used for performing arithmetic operations without carry. The proposed system designed an array multiplier with Quaternary Signed Digit number system (QSD) based Carry Look-Ahead Adder (CLA) to improve the performance. Generally, the quaternary devices require simpler circuit to process same amount of data than that needed in binary logic devices. Hence the Quaternary logic is applied in the CLA to improve the speed of adder and high throughput. In array multiplier architecture, instead of full adders, carry look-ahead adder based on QSD are used. This facilitates low consumption of power and quick multiplication. Tanner EDA tool is used for simulating the proposed multiplier circuit in 180 nm technology. With respect to area, Power Delay Product (PDP), Average power proposed QSD CLA multiplier is compared with Power gating CLA and CLA multiplier.

Steganography is derived from Greek word, ‘steganos’ which refers to covered writing of messages. It is a mechanism of hiding messages or data by embedding them into some other data. Through this mechanism, the secured message can be completely hidden from unauthorized access. In existence, Steganography along with SPIHT compression is done using asymmetric cryptographic algorithm. In this model, Steganography is being employed with SPIHT compression where wavelet transformation of image takes place and AES cryptographic algorithm along Diffie-Hellman key exchange mechanism is used for providing additional security and processing speed. In the proposed system, the secret image is compressed using SPIHT compression and encrypted using cover image. Hence, the encrypted stego image with cover image and secret image is obtained. To perform decryption over the stego-image which, the key is to be exchanged using Diffie-Hellman technique. After decrypting, the image is subjected into SPIHT decompression process which is employed to retrieve the original secret image.

This paper presents the performance analysis and comparison of full chip and half chip rate of noncoherent (NC) and differentially coherent (DC) code acquisition scheme in (multiple input-multiple output) MIMO assisted by direct sequence spread spectrum (DS-CDMA) wireless system when communicated over uncorrelated Rayleigh channel. Four schemes are investigated, namely, SISO with full chip rate, SISO with Half chip rate, MIMO with full chip rate, and MIMO with half chip rate by varying the code acquisition technique. The simulation is done in RF signal processing Lab using matlab tool box, and the performance metrics are considered, namely, Bit Error Rate (BER) and mean acquisition time (MAT). The simulation results indicate that DC performance is superior than NC in both full chip and half chip rate and also shown that half chip performance is better than full chip in both DC and NC code acquisition methods.

The effect of fading and interference effects can be combated with equalizer. This paper analyses the performance of MMSE equalizer based receiver for MIMO wireless channel .The BER characteristics for the various transmitting and receiving antenna is simulated in mat lab tool box and many advantages and disadvantages the system is described. The simulation carried out signal processing lab show that the MMSE equalizer based receiver is a good choice for removing some ISI and minimizes the total noise power. The results show that the BER decreases as the m x n antenna configurations is increased. General Terms Equalizer, Bit error rate, Signal to noise ratio (eb/N0),transmitting antenna, receiving antenna

It is extremely time consuming for researchers looking for particular events of interest to manually search in the video database. Therefore, there is enormous scope in research in the field of automatic extraction of key frames from underwater video sequences. Analysis of underwater video poses many challenges to existing techniques in computer vision including camera movement, turbidity, uneven background, non uniform lighting, moving objects in the underwater and inaccessibility of environment. The main aim of this paper is to (i) describe about the existing methods for key frame extraction, (ii) emphasize the key challenges in underwater environment, (iii) propose a new method to extract key frames which suits for underwater environment, (iv) perform the automatic detection and characterization of events of interest in underwater videos.

Large scale multiple input multiple output (LS-MIMO) system is an emerging technology that has large antennas at both transmitter and receiver. In this paper, a novel approach is proposed namely Cholesky Decomposition based quasi maximum likelihood (ML) detector followed by list sequential (LISS) decoder (Quasi ML-LISS) receiver for LS-MIMO system. The refined empirical log-likelihood ratio (RELLR) generated by ML soft detector is decoded by using LISS decoder. The performance of the receiver is enhanced for turbo coded MIMO system by generating the RELLR at the detector output for higher order modulation. Simulation results show that bit error rate (BER) performance of proposed receiver is closer to the ML performance and superior to the multiple stage (MS) receiver.

This paper proposes a novel approach of an Approximate Message Passing (AMP) detection method for Large Scale Multiple Input Multiple Output (LS-MIMO) receiver which uses MT transmit antennas and MR receive antennas to detect the information bits transmitted. An efficient transmission technique called Spatial Modulation (SM) in which the information bits are encoded and conveyed through the indexes of the transmit antennas using M-ary Amplitude and Phase Shift Keying (APSK) modulation to increase the efficiency of the receiver. Simulation results show that the Bit Error Rate (BER) performance of the proposed detection method performs near to the Maximum Likelihood (ML) detection and outperforms than the Modified Maximum Ratio Combiner (MMRC) detection.

Feature selection (FS) is a process which attempts to select more informative features. In some cases, too many redundant or irrelevant features may overpower main features for classification. Feature selection can remedy this problem and therefore improve the prediction accuracy and reduce the computational overhead of classification algorithms. The main aim of feature selection is to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In this paper, Principal Component Analysis (PCA), Rough PCA, Unsupervised Quick Reduct (USQR) algorithm and Empirical Distribution Ranking (EDR) approaches are applied to discover discriminative features that will be the most adequate ones for classification. Efficiency of the approaches is evaluated using standard classification metrics., Comment: 7 pages, Conference Publications

The dynamic road traffic management is based on the dynamic vehicle routing during peak hour traffic. The real time traffic management has become a hectic problem in daily life due to the increasing traffic, sudden accidents, and bottle necks due to various reasons. In Dynamic Vehicle Navigation System (DVNS), the real time traffic junctions are mapped as nodes and the traffic rate between the signals is considered as the link weight for the selection of routes from source to destination. The selection of the route depends on various parameters such as traffic rate, speed of the vehicle, shortest path etc. The dynamic route selection is implemented using the krushkal's algorithm based on different parameters. The end-user selects the source and destination and sends the information to our system using Google maps; our system suggests the optimal route to the end-user based on the dynamic routing table. The traffic in-charge personnel have to update the traffic rates using their mobile PDA. The Dynamic routing table is updated periodically to accomplish the optimal solution.