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Suprarenal or adrenal gland haemorrhage is an uncommon but potentially lethal condition if unrecognised. Adrenal masses rarely present with haemorrhage, but they remain an important differential aetiology for adrenal bleeding. We present a novel case of primary adrenal lymphoma with adrenal haemorrhage in a middle-aged woman who presented with right-sided abdominal pain and class 1 haemorrhagic shock. She was found to have spontaneous unilateral adrenal gland haemorrhage in the absence of any underlying previous pathology. Presenting features, diagnosis and subsequent oncological management are reported.

Wireless sensor network (WSN) becomes a hot research topic owing to its application in different fields. Minimizing the energy dissipation, maximizing the network lifetime, and security are considered as the major quality of service (QoS) factors in the design of WSN. Clustering is a commonly employed energy-efficient technique; however, it results in a hot spot issue. This paper develops a novel secure unequal clustering protocol with intrusion detection technique to achieve QoS parameters like energy, lifetime, and security. Initially, the proposed model uses adaptive neuro fuzzy based clustering technique to select the tentative cluster heads (TCHs) using three input parameters such as residual energy, distance to base station (BS), and distance to neighbors. Then, the TCHs compete for final CHs and the optimal CHs are selected using the deer hunting optimization (DHO) algorithm. The DHO based clustering technique derives a fitness function using residual energy, distance to BS, node degree, node centrality, and link quality. To further improve the performance of the proposed method, the cluster maintenance phase is utilized for load balancing. Finally, to achieve security in cluster based WSN, an effective intrusion detection system using a deep belief network is executed on the CHs to identify the presence of intruders in the network. An extensive set of experiments were performed to ensure the superior performance of the proposed method interms of energy efficiency, network lifetime, packet delivery ratio, average delay, and intrusion detection rate.

This paper describes a novel multistage CIC filter which plays a vital role in software-defined radio (SDR) applications. In order to operate the baseband signal, wireless networking standards demand different sample rates. As a result, sample rate conversion (SRC) turns out as a pivotal method of SDR. Thus, cascaded integrator comb (CIC) filter is a distinct kind of linear-phase FIR filter which can be used as a decimation filter for SRC operation. The architecture of this filter is multiplierless with low passband droop; hence, it requires less area in contrast to other decimation filters. In this brief, the fundamental structure of CIC filter is examined with various parallel prefix adders and also the significant parameters of the CIC filter for different adders are exemplified. The proposed CIC filter with various parallel prefix adders has been designed in Verilog HDL using Xilinx ISE 14.7 and implemented on Kintex7 FPGA. The schemed CIC filter design is compared with the traditional CIC filter in view of area, speed and power consumption. Based on the obtained results on Kintex7 FPGA, the CIC filter with Brent Kung adder outperforms in terms of LUTs by 41.67% and power by 34.78% compared with the classical CIC filter and among the CIC filters using other parallel prefix adders. The proposed CIC uses new polynomial method which provides 33.33% reduction in passband droop and 41.14% reduction in stopband ripple compared to the existing sharpened CIC.

In a developing country like India, agriculture is one of the most significant sectors in terms of financial gain and healthy foods to survive. Agriculture has always been our country's primary occupation. Smart food plays a vital role once it involves food security and that's littered with food loss and food wastage. Reports suggests that, almost fifty proportion of the food produced world-wide doesn't even reach the individuals as they're wasted throughout harvest, transportation or storage. One in all the key threats to the farmers is that the loss throughout storage of crops in granaries and warehouse. If crop losses are reduced, it'll mechanically increase the number of food accessibility. Our work takes into thought this drawback that affects a lot of individuals globally and has tried to bring out an easy answer by building a sensing element based mostly module that monitors and additionally controls the deposition system. In order to assist the farmer, WHO protects the crops that are stored in a warehouse, an IoT enabled observation system is planned to be deployed in remote areas, wherever the accessibility is incredibly minimum for farmers with smart storage facilities to scale back the food losses and increase food safety.

With increased penetration of Wind energy as a renewable energy source, grid codes worldwide necessitate those wind turbines (WT) should essentially be coupled to grid throughout as well as post fault conditions and must source reactive power to the grid with an objective of maintaining grid voltage. This chapter focuses on the Low Voltage Ride Through (LVRT) enhancement of Doubly fed induction generator (DFIG) wind turbine especially on the control of DFIG wind turbine and of its power converter. The main protection issues for DFIG wind systems during voltage transients are excessive currents flowing in the Rotor-Side Converter (RSC) and overvoltage in the dc-link capacitor of the partial scale back to back converter. Many DFIGs employ a crowbar-based system to shelter the converter at the rotor side during disturbed and/or distorted grid voltage circumstances. Although it helps in protecting the generator, it does not provide an appropriate grid support behavior. This shortcoming led to designing a new coordinated controller that excludes or even cancels the need for a crowbar. This chapter proposes a fault confrontation controller (FCC) design to augment the feature of low voltage ride through (LVRT) in this turbine. Considering the system’s nonlinear nature, an attractive Fault Confrontation Controller was constructed using computational intelligence (CI) techniques, namely fuzzy logic, backpropagation network (BPN), and adaptive neuro-fuzzy inference system (ANFIS). The simulation study demonstrates that the ANFIS system gives the best results for the proposed system.

A few of the biggest reasons of cancer mortality amongst men is prostate cancer. Effective care and management planning will significantly reduce the rate of mortality due to prostate cancer. Magnetic Resonance Imaging is an excellent method for initial treatment of cancer (MRI). Recent studies have shown that MRI can also be used to classify prostate cancer. The approach addresses various stages of a computer-aided diagnosis of prostate cancer, including cancer detection, clinical signifying cancer.

Wide Area Measurement Systems (WAMS) is a collective technology to monitor power system dynamics in real time, identify system stability related weakness and helps to design and implement counter measures. It uses a global positioning system(GPS) satellite signal to time synchronize from phasor measurement units (PMUs) at important nodes in the power system, sends real-time phasor (angle and magnitude) data to a Control Centre. The acquired phasor data provide dynamic information on power systems, which help operators to initiate corrective actions to enhance the power system reliability. The goals of WAMS are real time monitoring, post disturbance analysis, adaptive protection and power system restoration. The major components of WAMS are Phasor Measurement Unit (PMU), Phasor Data Concentrator (PDC), Global Positioning System (GPS for Time Synchronization of the phasors), Communication channel (Preferably optical fiber cable), Visualization and analysis tools, Wide area situational awareness system and Wide area protection and control. This chapter is going to discuss about the goals and benefits of using PMUs, comparison between PMUs and SCADA system, Detailed description of WAMS components, synchronized PMUS, different kind of applications of WAMS in power sector, Components and operation of PMUS, real time examples of WAMS in power system operation and control.

Agricultural takes a major percentage in a country’s economic growth. Crop production plays an essential role in agriculture. Countries’ economical growth rate is reduced due to less crop production. Foods are essential for every living being, since we need proper food for survival. Hence, it is essential for every farmer to cultivate a healthy plant to increase the crop production. However, in nature, every plant can get attacked by some sort of disease but the level of damage occurred to the crops are different for every plant. If a fully matured plant get affected by a simple disease, it will not affect the full plant but if a small plant gets affected by the same disease, it causes severe damage to the plant, as we cannot manually monitor the plants and cannot detect the disease occurring in the plants everyday. Huge manpower is needed to monitor every plant in the farm so it needs time for monitoring every crop in the field. In this paper, image recognition using conventional neural network (CNN) has been proposed to reduce the time complexity and manpower requirement. The proposed algorithm accurately detects the type of diseases that occurs in the plants.

The Universal Filtered Multi-Carrier (UFMC) system is a promising contender for next-generation 5G wireless communication. UFMC has better sub-carrier separation like Filer Bank Multi-Carrier (FBMC) and provides minimal complexity like Orthogonal Frequency Division Multiplexing (OFDM). Hence, in this chapter, the UFMC system has been proposed with different prototype filters for shaping the waveforms and to increase performance. The performance of the UFMC system is analysed for important parameters such as Power Spectral Density (PSD), Block Error Rate (BLER), Bit Error Rate (BER) and Peak-to-Average Power Ratio (PAPR). The filters considered for the analysis are the Hermite filter, the PHYDIAS filter, the Root Raised Cosine (RRC) filter and the Rectangular filter. The analysis shows that among the four filters, the Hermite filter offers the best performance. This filter provides 72%, 63%, 67% and 29% enhanced performance for the parameters PSD, BLER, BER and PAPR, respectively.

Currently in many real time applications, recommender systems play a prominent role in helping the customers by selecting the most suitable products of their requirements. Generally the scheme of recommending the products is observed as single individual task. To enhance this task, the collaborating filtering is used as more effective charge in many situations. One of the major issues of the recommender system is difficult in learning the behavior of the group of the products and nature of the customers. Also in this internet era, the data collected day by day keeps on increasing and leads to information overloaded issue .Under this situation it is recommended to propose a novel collaborative filtering based methodology named ‘Hellinger Coefficient Based Collaborative Filtering (HCBCF)’ to search, find and rate the recommending products. The efficiency of the proposed method is evaluated on movie lens and Netflix recommendation datasets, and also this proves to be superior among other existing recommendation algorithms on comparing its efficiency metrics.

In this paper, the adversarial haze attack problem is addressed using the dark channel prior (DCP) de-hazing method. The adversarial attack affects rank-1 accuracy, where searching a target image against each test image is a specific search problem. To resolve this kind of problem, a feature fusion model is proposed to fuse handcrafted features and a pre-trained network model to obtain robust and discriminative features. The proposed model learns global features using transfer learning architecture whereas local features are obtained using the conventional method. Three pre-trained CNN models (AlexNet, ResNet, and Inception-v3) are used for feature extraction via transfer learning. The experiments are performed on publicly available datasets, achieving 68.6% accuracy in rank-1 with VIPER dataset and 79.6% accuracy with CHUK03 dataset. The proposed model enhances rank-1 accuracy of person re-identification when comparing with other state-of-the-art methods.

Digital Ledger is an innovative technology that has changed the way we work with data. Digital Ledger otherwise popularly known as blockchain enables us to store data in the truest form which avoids the necessity to gather, collect, examine and represent data for various platforms and systems. Blockchain is revolutionizing the digital world with numerous applications breaking out in different fields, predominantly finance. Apart from crypto currency, blockchain has also found its use in healthcare, travel and voting system, to name a few. With the advent of blockchain, several sectors have begun using and experimenting with this technology. The implementation of blockchain in emerging fields of Internet of Things, Cyber Physical Systems, edge computing, social networking, crowd sourcing is being studied and tested. Blockchain has enormous potential to make lasting changes in the world. The purpose of this document is to highlight the need of current technologies, algorithms, platforms which can be made suitable to support the understanding of blockchain. This paper presents a comprehensive overview on blockchain technology. It also presents a brief description about the current and future trends of this revolutionary technology.

Among the various encoding techniques which is effective, polar encoding is found to be the best because of its unique characteristics of channel achieving property. The coding scheme chosen by Third Generation Partnership Project (3GPP) is polar code since it has attracted the attention towards the 5$^{th}$ generation wireless system. The emerging research is going on the polar codes in the application of MIMO system. Polar encoder is mainly designed because of its high processing speed as compared with other techniques. The VLSI implementation process can be extended to a large extent by using radix-k based design.

We have designed Internet of Thing (IoT) system based on for monitoring and controlling the water parameters in the aquaculture. The system can detect and control the parameters such as temperature, pH value, dissolved oxygen, water level, foul smell detector and ammonia in the water. The sensors nodes gather the real time data from the water and send it to the aurdino processor for processing. If the measured parameters exceeds the desired range then the processor activates the corresponding controller for taking necessary action. The measured values from the sensors are also sent to the cloud using wi-fi modem and can be viewed in the control room. Also the values are sent as short messages to the concern person using GSM modem. The proposed system is compatible and can be used for any type of aquaculture system.

Over the course of time, electronic voting has evolved as a substitute for paper ballot voting to decrease redundancies and inconsistencies. Due to the many security and privacy vulnerabilities experienced over time, the past results of e-voting in the last three decades indicate that it has not been very successful. All cryptocurrencies are actually based on block chain. Block chain is based on the principle of distribution and decentralization. It is a continuous and continuously growing ledger that holds, in a secure, chronological and immutable way, a permanent record of all the transactions that have taken place. Value in a block chain can be anything. In the case of cryptocurrencies, it takes the meaning of money or currency. While in a game it takes the form of points or score. The value can take the form of a vote or a ballot while e-voting. The blockchain can be used in this article to pass votes between two parties. In our case, the electorate is one party and the candidate who earns the vote is the other. Without having a controlling central authority body, the block chain can be implemented in a more stable manner in mass electoral voting practise. A voting system that uses a more stable, tamper-proof block chain (immutable to voting modifications either by other voters or by any third party) and is cost-effective.

In current development in science and technology, Machine learning algorithms play an essential role for prediction, classification, data analysis and data visualization. With this efficient algorithm, we can solve many real-world problems in all domains like education, healthcare, banking, geographical analysis, etc., in the current scenario; much research work is going on with the new virus's infection called the corona. This Corona virus is a comprehensive unit of virus this cause illness in humans or animals, now in East Asian countries, this virus affected more people. In India, the first case was found in January month, originated from China. The entire world is focusing on the disease, and day by day, the infection and death rate is increasing. In this, we intended to focus on the spread of this deadly disease and to demonstrate which countries are the most affected by doing statistical analysis. On December 2019, As of 10 February 2020, China reported overall of 40,235 cases 909 deaths, evoking local and foreign terror. Here we provide estimates of the major epidemiological parameters, Based on the epidemiological data available to the public for Hubei, China, 11 January to 10 February 2020. In particular, we give an estimate Fatality and case recovery rates, along with their 90 per cent confidence levels as the epidemic progresses. For this work implementation, Extreme Learning Machine algorithm used. ELM is a feed-forward network and its learning rate also fast when compare to normal neural network. No need to provide any weights and bias values. This algorithm will give a promising result with the best accuracy.

This article presents a brief survey of Digital down Converter (DDC) architecture and its extensive applications. The distinctive feature of the DDC architecture is its frequency translation from real data signal at an intermediate frequency to a complex baseband signal at zero frequency. The architecture of DDC comprises of Numerically Controlled Oscillator (NCO) and decimation filters. The components of ultra efficient DDC architecture have been implemented in Field Programmable Gate Array (FPGA) device. This down converter provides high malleability, customizability and bearable cost. Due to its amenities, digital down converter was utterly studied and applied in many wireless communication technologies.

The recent technology aims at complex systems ,multiple computational algorithms,High efficient hardware which works with low power.But to operate all these systems the power requirement for testing is huge.To solve this problem in testing a Built in Self Testing methodology is used.Along with this BIST concept Weighted Pseudo random pattern Generator is used to minimize the power consumption and dissipation.The input patterns will increase the switching activity which inturn increase the energy requirement. Hence, to reduce the switching activity the weighted patterns are generated. This reduces number of patterns given to the CUT and hence theswitching activity factor also reduced.The proposed weighted pattern generated architecture was designed and implemented in Altera Cyclone FPGA. Implementation results show that the proposed BIST architecture consumes less power.

Now- $a$ -days, reversible logic is getting huge interest among the IC designer because it consumes less power. Reversible logic has been found in applications like Digital signal processing, DNA and quantum computing and high speed VLSI design. The implementation of reversible logic contains number of reversible logic gates. In this work, a multiplier is designed using HNG gate. An efficient high speed multiplier has been proposed using verilog coding and Cadence 180 nm technology is used for its implementation. Compared to the existing multiplier, the proposed multiplier consumes less power and comparatively less quantum cost. Hence, the proposed multiplier results in less power consumption without sacrificing the speed.

Intima media thickness is the main indicator of the heart disease, and it is one of the major criteria for early screening and diagnosis of heart diseases. In the proposed work, left and right common carotid artery (CCA) intima media thickness is measured by taking ultrasound image of heart. A multistep algorithm is presented based on Otsu’s thresholding to segment the carotid artery in the ultrasound image. The noises presented in the image are removed using filters such as Lee, Kuan, wavelet denoising, SRAD, and Kalman filters. Morphological operations enhance the carotid artery region, and it makes easy to measure the intima media thickness (IMT). Finally, the upper and lower intima media thickness range is measured accurately and the images are classified using the SVM and RBF classifiers which provide efficiency of 96 and 98%, respectively. The obtained efficiencies are more accurate compared to the existing one.

Finite Impulse Response (FIR) filters are broadly utilized in many high speed DSP applications. Among the FIR filters reconfigurable FIR filters are having applications in Software Defined Radio and Cognitive Radio. This paper proposes effective consistent multiplier (CM) engineering in light of vertical even parallel regular sub expression disposal calculation for planning a reconfigurable FIR filter. In FIR filter, the product function is computed between the input and constants coefficients defined as the multiple constant multiplication (MCM). This multiplication procedure is connected on to the common reconfigurable FIR channel as the application improvement. Due to the proposed sub expression elimination, the number of addition stage in the multiplication process and the repeated additions were reduced. The proposed multiplier with FIR filter has been designed using verilog coding and implemented in cyclone IV FPGA. The proposed design uses 83% reduced logic utilization and 2% reduces power consumption compared to the FIR filter with normal multiplier. The performance of the proposed design show that the efficiency of the design.

Big data is a broad term for data sets so large or complex the traditional data processing applications are inadequate. Challenges include analysis, capture, data duration, search, sharing, storage, transfer, visualization, querying and information privacy. The processing and storage of big data although made efficient and easy by hadoop, there are two things to be considered, a storage part (Hadoop Distributed File System) and a processing part (Map reduce). The performance of a map reduce depends on how the data are partitioned and then controlled in parallel manner. The current methods are far from optimal. The skew aware partitioning technology is used to detect the problems related to data partitioning that affects performance. In this paper, we enhance the split mechanism of Hadoop without making it harder than existing. There are a series of works on data partitioning in database communities, we want to employ more intelligent methods, like sampled data for record skew, program code analysis for computation cost estimation etc., to enhance our works for optimizing data partition in MapReduce. Experiments results demonstrated that our solutions can improve the MapReduce processing performance remarkably than traditional Hadoop Implementation.

Agricultural productivity is affected worldwide due to anthropogenic and climate change-induced abiotic stresses, posing a threat to food security. Use of microorganisms for abiotic stress management in agriculture is emerging as economically viable and environmental-friendly option. Actinomycetes, the Gram-positive bacteria with filamentous structure that are common associates of plants (as rhizosphere inhabitants and as plant endophytes), are receiving attention for their potential application in stressed ecosystems. Many actinomycetes exhibit plant growth-promoting (PGP) properties including indole acetic acid (IAA) production, phosphate solubilization, siderophore production, biocontrol of phytopathogens, and 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase activity. Besides, they can grow under diverse stress conditions such as moisture stress, high temperature, salinity, alkalinity, and wide pH range. Recently, many reports have documented the role of actinomycetes in alleviating salinity and drought stress in crop plants. However, there is a need to further strengthen the research to explore their potential to improve plant productivity under diverse environmental stress conditions by conducting extensive pot and field trials and to understand the underlying mechanisms.

The increasing demand for the high fidelity portable devices has laid emphasis on the development of low power and high performance systems. In the next generation processors, the low power design has to be incorporated into fundamental computation units, such as adders and multipliers. In this paper SERF and Modified SERF full adder topologies are presented. The analysis of Power, Delay, Power Delay Product (PDP) optimization characteristics of SERF Adder is designed. In order to achieve optimal power savings at smaller geometry sizes, proposed a heuristic approach known as Modified SERF adder model. The proposed Modified SERF adder model consumed the least power compare to SERF Adder with no deterioration in performance. Taken together, the results suggest that the Modified SERF Adder is well suited for ultra low power design and fast computation at smaller geometry sizes.

The quality of the electrical power delivered to consumers is heavily affected by the power electronics based controllers, introduced in both domestic and industrial sectors, which in turn results in malfunctioning of equipment or eventual damage. Series compensators, Shunt compensators and series-shunt compensators are some of the strategies applied to address the power quality issues effectively. In this work a series compensating device, viz. 27 level-cascaded multilevel inverter based Dynamic Voltage Restorer (DVR) with multicarrier SPWM technique is proposed to mitigate voltage swells and voltage sags. The PWM technique used in this work is Alternate Phase Opposition PWM (APODPWM), which is one of the vertical arrangement multicarrier sinusoidal pulse width modulation techniques, to control the cascaded H-bridge inverter. The single-phase version of the proposed system is simulated to verify the effectiveness in addressing voltage issues and it is found that the obtained simulation results are satisfactory. The THD is found to be 3.40%, which is well below IEEE standards apart from considerable improvement in response time. The prototype of the proposed model is developed and the pic-microcontroller PIC16F887 is employed to implement the APODPWM. The experimental results obtained from the prototype are compared with the respective simulation results and they match with reasonable accuracy.

This paper proposes the design and control of single phase Power Electronic Transformer (PET). This PET has two stages; the input stage consists of a matrix converter and the output stage has another matrix converter which are connected by a high frequency transformer. It has bidirectional power flow controllability. It can convert high/low voltage into low/high voltage at any frequency. Power factor correction can also be achieved in this transformer. The proposed system uses only two stages, so that the number of switches used is considerably decreased. The converter is simulated using Multisim 13.0 package. Average current controller is used to improve the input power factor and to reduce the source current Total Harmonic Distortion (THD) below 5%. Closed loop control is simulated with the help of LabVIEW 14.0 and found that the Total Harmonic Distortion is well below the acceptable limits. The power factor near to unity is also achieved.

By using the mobile sink, we can reduce the energy consumption of nodes and to prevent the formation of energy holes in wireless sensor networks(WSNs)and this was mainly used for delay-sensitive applications within a given time constraint. My approach is that mobile sink node only visits rendezvous point(RPs) and this may be opposed to all the nodes. In this sensor nodes not having rendezvous point(RPs) means multihopping can be done to the nearest RPs. To computing a tour, that visits all these RPs. Calculating the optimal tour is an NP-hard problem. To address the problem called as weighted rendezvous planning(WRP)is proposed, there by each sensor node is assigned a weight to its hop distance. WRP is to be validated through computer simulation and our WRP as a mobile sink to retrieve all sensed data within a given time constraint. WRP reduces energy consumption by 22% and increases network lifetime by 44%.

Image Acquisition is the method of capturing the image. Entire image is not needed for Enhancement Restoration and Object Recoginition. Image segemention is to get the information which is needed for Authentication System. Processing the segemented image will be cost effective and time effective. Each person can be differentiated based on the physiological and behavioural characteristics. Example for physiological characteristics are palm, fingerprint, iris etc. Palmprint recognition system consists of capturing image, segmentation, feature extraction, matching and result. For any image the noise should be removed and only the region which is needed should be extracted. In Palm Acquisition, the whole palm is captured but it is not required for recognition or authentication. Only certain features such as palmline and texture are extracted from whole palm, which contains the necessary information. In this paper we proposed a methodology for extracting Region of Interest which consists of Otsu thresholding scheme[18], Morphological operations, Canny edge detection, marking reference point and palm image alignment. Experimental results show that the approach is efficient both in computational cost and segmentation quality of Multispectral palm Images. The performance of Region of Interest (ROI) segementation is tested with CASIA MultiSpectral Palmprint Image Database V1.0. ROI extraction for peg free palm image depends on the hand position, there is no constant time. In our methodology ROI is extracted in 2.45 seconds.

Multispectral palm print is one of the most reliable and unique Biometric. MSI have faster acquisition time and better quality images than normal images. The advantages of the proposed method include better hygiene and higher verification performance. In this we proposed Local binary Pattern (LBP) based histogram for multispectral palm print representation and to choose the best spectrum for authentication. Here the central part of the palm print image is resized to the size of 180 × 180 and divided into non overlapping sub-images. The size of the sub-image various from 2×2 pixels to 90×90 pixels. The histogram is obtained for each block and the values are used for comparison. Totally 36 images per person are taken from standard database available. Training set is prepared with the help of 2 images from each spectrum. Results are checked against remaining images in authentication mode. Results are represented in terms of Genuine acceptance rate(%). Most of the palm print recognition systems use white light to acquire Images. This study analyzes the palm print recognition performance under six different illuminations, including the white light. The experimental results with a large database show that white light is not the optimal illumination, while 700nm light could achieve higher palm print recognition accuracy than the white light. In authentication mode 98% recognition rate is obtained for the spectrum 700nm. The experiment was conducted for six spectrums like 460,630,700,850,940nm, White Light. We use the CASIA-MS-Palmprint V1 database of size 7200 images collected by the Chinese Academy of Sciences' Institute of Automation (CASIA).

In this paper we proposed a detection of invalid packets in VANETs (DIP) scheme to classify and detect the invalid packets at the time of group communication in VANETs. The invalid packets are classified and detected using enhanced detection algorithm. This algorithm is based on the Elliptic Curve Diffie Hellman (ECDH) key agreement scheme, which reduces the processing time of invalid packets and reduce the rebatch verification time. The DIP scheme evaluated through the performance of the transmission overhead and message delay using the simulation tools. The transmission overhead and message delay are reduced and compared with the existing system. The simulation results shows that the DIP scheme is less than an existing scheme for value added services in VANETs (ABAKA).

Assessing vulnerability to climate change and variability is an important first step in evolving appropriate adaptation strategies to changing climate. Such an analysis also helps in targeting adaptation investments, specific to more vulnerable regions. Adopting the definition of vulnerability given by IPCC, vulnerability was assessed for 572 rural districts of India. Thirty eight indicators reflecting sensitivity, adaptive capacity and exposure were chosen to construct the composite vulnerability index. Climate projections of the PRECIS model for A1B scenario for the period 2021-2050 were considered to capture the future climate. The data on these indicators were normalized based on the nature of relationship. They were then combined into three indices for sensitivity, exposure and adaptive capacity, which were then averaged with weights given by experts, to obtain the relative vulnerability index. Based on the index, all the districts were divided into five categories with equal number of districts. One more district was added to 'very high' and 'high' categories. The analysis showed that districts with higher levels of vulnerability are located in the western and peninsular India. It is also observed that the highly fertile Indo-Gangetic Plains are relatively more sensitive, but less vulnerable because of higher adaptive capacity and lower exposure.

This study shows the lifestyle of bus drivers on their service. Nowadays we are facing problems while moving one place to another place. For this we need to plan where we want to move, through which mode to use to travel, like this we need to consider so many factors. So the transportation plays a major role for the smooth journey. In this regard the bus drivers play an important role to take the people from one place to another safely. We travelled a lot but we don't know the problems faced by the drivers and expectations of the drivers in their services. In this situation they need the support from various sources like passengers, bus owners, government, and general public etc. The factors determine the job satisfaction, working time, travelling time (task), bus maintenance, incentives, owners support, stress and relationship with passengers are dealt. The research design used for the study was descriptive research design. In this study simple random sampling techniques was employed in selecting the samples. The drivers (samples) were selected randomly and interviewed. The sample size is 75 and data collected from the respondents were converted into readable form to the process of classification and arrangements. The data was tabulated and analyzed for logical statement using statistical method like simple percentage analysis and Chi – square. The analysis says that the drivers’ satisfaction level and their expectations for better performance.

Elliptic Curve Cryptography (ECC) is a form of public-key cryptography. This implies that there is the involvement of a private key and a public key for the purpose of cryptography. ECC can be used for a wide range of applications. The keys used are much smaller than the non-ECC cryptographic algorithms. 256 bit and 384 bit ECC are used by NSA for storage of classified intel as ECC is considered to be a part of suit B cryptography by the NSA. When it comes to normal usage, other versions of ECC are used. So, many of the applications protected by ECC are vulnerable to side channel attacks. So, the objective is to modify the existing method of implementation of ECC is some regular domains like media, smart grid, etc., such that the side-channel attacks [7], [3] vulnerabilities are fixed.

Rainfed agriculture with nearly 58% of the cultivated area is home to about 40% of human and 60% of livestock population in India and contributes 40% of the country's food production. Even after full realizing the full irrigation potential of the country, half of the cultivated area will continue to be under rainfed farming which highly dependent on monsoon rainfall. It is widely believed that increasing rainfall variability is likely to affect the livelihoods of millions of small and marginal farmers in the years to come. The National Initiative on Climate Resilient Agriculture (NICRA) was initiated in 2011 to primarily strengthen research to address climate change and to demonstrate proven resilient practices in most climate vulnerable parts of the country. Farmer participatory technology demonstrations are being carried out since 2011-12 in 100 vulnerable districts prone to recurrent drought, floods, and extreme weather events like cyclone, heat and cold wave. Interventions broadly fall into four modules related to natural resource management (NRM), crop, livestock and fisheries production systems and capacity building. During these three years, rainfall variability such as delayed onset and prolonged breaks in monsoon was experienced in several parts of the country. A total of 1259 NRM interventions to impact resilience through in situ soil moisture conservation, rainwater harvesting and efficient recycling were demonstrated, which resulted in a significant increase in crop yields by about 20-40% even under variable rainfall situations. Short duration, drought and flood tolerant varieties in paddy, pulses and oilseeds crops were demonstrated in about 2700 ha covering 7831 farmers with adaptation gains ranging from 10 to 31% [1,2]. Some of the climate resilient practices and technologies which have been found promising are being mainstreamed into the National Mission for Sustainable Agriculture (NMSA), a flagship government initiative for enhancing productivity and resilience of Indian agriculture in accordance with the stated aim of the National Action Plan for Climate Change (NAPCC).