National Conference on Digital Signal Processing, Information and Communication Engineering

Abstract: Paroxysmal atrial fibrillation (PAF) is intermittent atrial fibrillation which is not present all the time. Atrial fibrillation (AF) is one of the prominent causes of stroke and its risk increases with age. This paper explains a method to screen the patients with PAF from the normal sinus rhythm patients using Heart Rate Variability (HRV) parameters and also the prediction of onset of PAF. The time domain, frequency domain and nonlinear parameters are estimated. Feature selection is done with the use of Linguistic Hedges (LH) of fuzzy set. The selected features are fed to an Adaptive Neuro-fuzzy Classifier (ANFC) for screening as well as prediction. The accuracy obtained for screening and prediction were 94% and 93.75% respectively. The sensitivity in both cases found to be 100%. Keywords: Paroxysmal Atrial Fibrillation, Heart rate variability, Time domain, Frequency domain, Nonlinear, Linguistic Hedges, Adaptive Neuro-fuzzy Classifier.

Abstract: This paper reveals design and realization of 28 order efficient Finite Impulse Response (FIR) with equiripple characteristics for digital audio signal processing .In this paper the factorized canonical signed digit (FCSD) representation have been used to express the filter coefficients which reduces the design complexity, delay and area of FIR filter. FCSD guarantees the optimality by replacing binary coefficients specified with add and shift operation. FIR filter has been designed on MATLAB using equiripple technique and further the filter is synthesized on XilinxISE9.2 for Spartan 3EXC3S500EFG320-4, VIRTEX2PXC2VP30FF1152-5 FPGA devices. .The FIR filter is simulated I/O of 16 and 32 bit precision on MATLAB platform and FPGA structure. The behavioral simulation is proposed in VHDL model. Using an equiripple method with fixed point data type offers reduction in cost and minimum power consumption. Keywords: FIR, Equiripple, FCSD, Sparten3E XC3S500E, Virtex2P.

Abstract: Cloud computing is an emerging technology which has business centric attitude as it is a pay by use model. There is an increasing pace in cloud computi ng business model usage. But a lot of issues are still yet to be solved such as reliability, tolerance, load balancing, security, resource allocation etc. Among them security is the major concern pointed by NIST survey [2]. In cloud environment, user�s data has to be released to the cloud and leaves protection sphere of software owner. There are various public key infrastructure based protection schemes available. But these schemes lack trust due to public and private key distribution threats [2]. This paper presents the systematic way of protecting copy right of relational database with a secure design of Software as a Service in cloud environment. Firstly paper explains how public key infrastructure based secure protocol design has to be done with a sample application e-election system. Preliminary research has been done on this problem [1], but there is no comprehensive solution proposed. Hence this paper proposes Monte Carlo estimation and evolutionary based cloud drop watermark approach for database copy right protection. Using secure keys in encrypted form, cloud drops are generated with statistical probabilities for embedding .Monte Carlo estimation on normal probabilities is used for better prediction on mean and standard deviation. Thus this approach eliminates the threat of key distribution. Also this approach maintains the usability of data even after watermarking. Finally, this paper provides comprehensive solution for copy right protection using the above approach, bringing the better randomness and fuzziness through statistical probabilities. Keywords: Cloud Computing, CRDaaS, Monte Carlo, Public Key, Private Key.

Abstract: Robotic systems have made their impact on human life in unexpected ways. Earlier robotics systems were thought as units which work independently of each other then came the idea of connecting them through a network. Later it has been identified that rather than connecting the robots through a network it will be more powerful when they are connected through the Cloud. With this the learning from one robot can be processed remotely and mixed with information from other robots. Since the cloud can have all the computation and memory stored remotely, all of the endpoints can be lightweight, and there is a huge collective benefit. These robots can address billions of behaviors and learn how to do important things quickly. This study aims at learning the architecture of cloud robotics and the potential benefits of the Cloud offered to the cloud robotics. The paper also discusses the challenges and direction of future work in the field of cloud robotics. Keywords: Robotics, network, Cloud, automation.

Abstract: Fractal image compression (FIC) is very useful in situation, where we have to deal with Chunk of data with limited storage space and low band width. This lossy compression technique gives a high compression ratio by representing the image in a vector form representation. Main drawback of standard image compression techniques of JPEG and JPEG 2000 are resolution dependent and poor PSNR value at high compression ratio. This lossy compression technique provides good trade off between PSNR value and compression ratio. In this survey paper different image compression techniques are compared. Keywords: Image Compression, wavelet transform, Disctete cosine transform, Fractal.

Abstract: In this paper we have made an attempt to automate an industrial process using LabVIEW software. The Synthesis of Hydrogen chloride (HCL) has great importance in industrial, chemical, and commercial fields. This process involves a number of discrete operations which are to be performed in correct sequence to get the final product. Automation of this process reduces human intervention and results in increased productivity. This paper deals with automation using LABVIEW which is a simple and powerful tool for automation. Keywords: HCL synthesis, Flat sequence, VI, LABVIEW.

Abstract: The term Sparsity refered to as the number of non zero elements in sparse approximation and it can be measured by L_0 Norm. Sparse dictionary learning algorithms used to find sparse representation of the input data in the form of linear combination of basis elements. This elements are called atoms and it compose dictionary. Dictionary learning methods have been successfully used in a number of signal and image processing applications and this includes image denoising, face recognition, compression analysis. Dictionary learning algorithms consist of two stages: a sparse coding stage and a dictionary update stage. In the first stage the dictionary is kept constant and the sparsity assumption is used to produce sparse linear approximations of the observed data. In the second stage, the coefficients of the linear combination are kept constant and the dictionary is updated to minimize a certain cost function. The performance of these methods strongly depends on the dictionary update stage since most of these methods share a similar sparse coding stage. In previous dictionary learning algorithms sparsity constraint is only used in sparse coding stage but in proposed method the sparsity constraint is used both in sparse coding and dictionary update stage. Keywords: Sparsity, Dictionary learning, SVD.

Abstract: In these prevailing globalized times; we are more connected with the plastic money where our economic transactions are carrying out through debit or credit cards. The prevailing ATM (Automatic Teller Machine) systems founded on magnetic card & a static PIN. The breaching into the existing system by skimming devices, card trapping, etc. revealing the vulnerability of existing siting creates a hostile atmosphere for the transactions. The proposed system introduces an alternative for the magnetic card and PIN. The proposed system doesn't need any other complex component other than fingerprint sensor additional to existing traditional ATM. Instead of magnetic ATM debit/credit card use chip enabled card which contain only Account number, Name of the customer, Fingerprint and PIN. A fingerprint sensor is an electronic device used to capture a digital image of the fingerprint pattern. The captured image is called a live scan. This live scan is digitally processed to create a biometric template (a collection of extracted features) which is stored and used for matching. The input fingerprint retrieved from the sensor is directly compared with template stored on the card issued by the bank after this authentication, the embedded code initiate the communication with the data server to complete the financial transaction. Keywords: Fingerprint, Authentication, live scan, template

Abstract: The Discrete Wavelet Transform (DWT) is well known for its application in image and video compression. Due to its remarkable advantage over the discrete cosine transform (DCT) in image compression, 2-D DWT has been accepted for the JPEG-2000 compression standard. The implementation of 2-D DWT, however, is highly computation-intensive. The lifting scheme is an efficient implementation of wavelet transform. Compared with convolution based DWT, lifting-based architectures not only have lower computation complexity but also require less memory. Modifications are made to lifting scheme in order to reduce critical path delay and size of temporal buffer. Keywords: Lifting scheme, Discrete Wavelet Transform, Discrete Cosine Transform, VLSI architecture

Abstract: After lung cancer, breast cancer is the leading cause of cancer death in women [3]. Prominent in women above 40 years, breast cancer is also seen in young women and men. The only way to diagnose breast cancer is to classify tumors into malignant and benign tumors. There should be a reliable diagnosis procedure to distinguish between these tumors. But even experts find it very difficult to distinguish tumors. This paper presents a review on the breast image and feature acquisition techniques as well as the algorithms used for breast cancer classification into benign and malignant tumors. Keywords: Sonography, Scintimammography, Ductal Carcinoma, Microcalcifications

Abstract: Heart rate monitoring for fitness applications has gained new aspects with the emerging of smart watches or wrist bands. Estimating heart rate from motion corrupted PhotoPlethysmoGraphic (PPG) signals is extremely difficult under intensive physical exercise.Removing motion artifacts (MA) from the PPG signal is one of the most challenging issues.In this paper, a survey of methodologies for heart rate estimation from motion corrupted PPG signals is presented. Various MA removals, heart rate spectrum estimation from cleansed PPG signal and spectral peak tracking methods are discussed. Challenges associated with heart rate estimation from motion corrupted PPG signalsfor each method are studied and enabling solutions are reviewed. Keywords: Photoplethysmography (PPG), Heart Rate Monitoring, Motion artifact (MA),

Abstract: All of the electronics devices needs power and the power given wirelessly to devices like embedded or real time systems, gets more advantages. The major issue of the consumer products is the short life of battery even in high technology support systems. Many researches are going on with wireless power transfer technology via magnetism, microwaves, resonance etc... One method resonance is covered in this paper. Keywords: Wireless power transfer technology (WPTT), Resonance, Resonance frequency, Magnetic Coupling.

Abstract: The digital watermarking is a process of information hiding. Digital watermarks allow users to legally use content, while adding security to the content to prevent illegal usage. Digital image watermarking algorithms based on the discrete wavelet transform have been widely recognized to be more powerful than others. This is due to the wavelets' excellent spatial localization, frequency spread, and multi-resolution characteristics, which are similar to the theoretical models of the human visual system. This paper presents a review on different digital image watermarking techniques based on discrete wavelet transform. Keywords: Information hiding, discrete wavelet Transform, spatial localisation, multi-resolution, human visual system.

Abstract: Cloud computing is a model for enabling a convenient, on demand network access to a common shared pool of configurable computing resources that can be immediately provisioned and released with least effort . The technologies used in cloud have provided ample opportunities for scalability, cost efficiency, reliability and high resource utilization. Many applications deployed on clouds are real time in nature .Performance of real time cloud applications depends on the generated results and time at which result becomes available .Scheduling has essential role in mapping real time tasks to machines such that deadlines and response time requirements are satisfied. Agent based scheduling technology is flexible to meet different requirements while scheduling. The agent based systems facilitate the interaction between processes by cooperating, coordinating and negotiating with each other. Based on this approach scheduling mechanisms are implemented and corresponding dynamic scheduling algorithms for real time tasks are executed in cloud. In this paper an approach for real time scheduling is implemented for virtualized clouds. This concept is based on agent based scheduling with deadline and cost constraints. Based on these constraints a bidirectional announcement bidding mechanism and probability strategy are developed that will give an improved scheduling of tasks. Investigated the problem of agent-based scheduling for independent real-time tasks in virtualized cloud environments and proposed mechanism for improved scheduling based on deadlines and cost constraints. Keywords: Real time scheduling, cloud, virtual machines, bidding, cost effective ratio, market approach.