Faculty Publications
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Publications by NITK Faculty
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Item Adaptive local cosine transform for seismic image compression(2006) Aparna., P.; Sumam David, S.A typical seismic analysis involves collection of data by an array of seismometers, transmission over a narrow-band channel, and storage of data for analysis. Transmission and archiving of large volumes of data involves great cost. Hence there is a need to devise suitable methods for compressing the seismic data without compromising on the quality of the reconstructed signal. This paper presents our work on the seismic data compression based on adaptive local cosine transform and its associated multi-resolution and best-basis methodology and compares the results with wavelet based implementation. © 2006 IEEE.Item Syndrome coding of video with LDPC codes(2008) Reddy, S.; Aparna., P.; Sumam David, S.In this paper we present the simulation results of the video coding system based on the principle of distributed source coding. Unlike conventional video coding system, this system exploits source statistics at the decoder, thus reversing the complexity model. Current implementation uses LDPC codes for syndrome coding. © 2008 IEEE.Item Multilevel coset coding of video with Golay codes(2011) Aparna., P.; Sumam David, S.This paper presents a video coding method based on the principle of distributed source coding. This work aims in shifting the encoder complexity to the decoder to support uplink friendly video applications, simultaneously achieving the rate-distortion performance of the conventional predictive coding system. In this work concept of syndrome coding with Golay codes is adopted for compression. The simulation results presented in this paper reveals the superior performance of this distributed video coder over the Intraframe coders and predictive coders for video data with less correlation between frames. © 2011 IEEE.Item A nearest neighbor based approach for classifying epileptiform EEG using nonlinear DWT features(2012) Holla, A.V.R.; Aparna., P.Epilepsy is a pathological condition characterized by spontaneous, unforeseeable occurrence of seizures, during which the perception or behaviour of a person is altered, if not disturbed. In prediction of occurance of seizures, better classification accuracies have been reported with the use of non linear features and hence they have been estimated from wavelet transformed Electro Encephalo Graph (EEG) data and used to train k Nearest Neighbour (kNN) classifier to classify the EEG into normal, background and epileptic classes. Very good accuracy performance of nearly 100% has been reported from the current work. © 2012 IEEE.Item Fast response search and rescue robot, assisted low power WSN net for navigation and detection(IEEE Computer Society, 2013) Kumar, S.; Reddy, V.; Prakash, P.; Aparna., P.Our project is a remotely controlled robot capable of surveying disaster situations or act as a security countermeasure. It utilizes a stationary network of passive infrared sensor nodes interconnected through a multi-hop Zig bee network. The sensors are motion sensitive and using regional localization can be used for identifying the location of survivors or intruders based on the situation. The robot is controlled via a Wi-fi link which streams real time video back to the base station. The main processor is a TI Sitara AM335x ARM processor. It also acts as relay for the sensor data. Each node consists of three passive infrared detection circuits each covering a sector of 120 degrees and connects via the TI CC2530 ZNP chip. The raw PIR data is signal conditioned using an LM324 Op-amp. The nodes can be deployed easily due to their compact size. Their low power consumption and low cost makes them ideal for remote areas and can be deployed in large numbers. © 2013 IEEE.Item An efficient algorithm for textural feature extraction and detection of tumors for a class of brain MR imaging applications(Institute of Electrical and Electronics Engineers Inc., 2014) Parameshwari, D.S.; Aparna., P.In this paper, we propose an efficient textural feature extraction algorithm (TFEA) based on higher order statistical cumulant namely Kurtosis for a class of brain MR imaging applications. Using a model that represents the wavelet coefficient energies of the sub-bands of multi-level decomposition of the image as a basis, a feature set involving three parameters for each band corresponding to probability density function (PDF) of generalized Gaussian type is derived. The logical correctness and working of the proposed TFEA are first verified based on MATLAB ver.2010a tool. The algorithm is applied in conjunction with one of the popularly used canny edge detection algorithm for segmenting a class of real and synthetic magnetic resonance (MR) images to detect the region of a tumor if present. The use of the proposed approach results in reduced feature set size thus obviating the need for employing specialized feature selection/ reduction algorithms. A detailed look at the experimental results clearly show an improvement in the segmentation quality compared with conventional method. © 2014 IEEE.Item Symmetry based perceptually lossless compression of 3D medical images in spatial domain(Institute of Electrical and Electronics Engineers Inc., 2014) Chandrika, B.K.; Aparna., P.; Sumam David, S.A perceptual model developed in spatial domain based on background luminance is combined with lossless compression frame work to remove visually redundant information. Block matching is applied on anatomical symmetry present in medical images to remove intra slice and inter slice correlations. The obtained results show better compression gains against lossless compression techniques, without any degradation in visual quality. © 2014 IEEE.Item A comprehensive solution to road traffic accident detection and ambulance management(Institute of Electrical and Electronics Engineers Inc., 2016) Hari Sankar, S.; Jayadev, K.; Suraj, B.; Aparna., P.Delay in providing Emergency Medical Services (EMS) is the cause of the high mortality rate in road traffic accidents in countries like India. There is delay involved in each and every stage of the process, right from reporting an accident to dispatching an ambulance, till the patient is safely handed over to the casualty. Minimizing this delay can help save lives. We propose a comprehensive solution to both accident detection and ambulance management. When the in-vehicle accident detection module reports an accident, the main server automatically dispatches the nearest ambulance to the accident spot. The android application used by the ambulance driver assists the driver to reach the location quickly and safely. Automation of accident detection and ambulance dispatch, along with providing guidance to the ambulance driver, is achieved here. This can save precious time and help standardize the whole process. © 2016 IEEE.Item A feasible QRS detection algorithm for arrhythmia diagnosis(Institute of Electrical and Electronics Engineers Inc., 2016) Khadirnaikar, S.; Aparna., P.This paper presents a reliable QRS detection algorithm to detect and classify Electrocardiogram (ECG) waveform abnormalities by extracting features such as heart rate and duration of QRS complex. As R peak detection is the pivotal step in automatic electrocardiogram analysis, various mathematical operations like clipping, differentiation and squaring are carried out in the preprocessing stage to enhance the section containing the QRS complex. Thresholding is performed to detect the R peaks. In order to improve the accuracy of the algorithm search back technique is implemented to determine the missing R peaks and heart beat. Once the position of R peak is detected, positions of Q and S are determined using two different search intervals to take into account the anomalous conditions as well. The heart rate and QRS width are then computed and compared with the normal values to determine the degree and type of abnormality. MIT-BIH database is used to evaluate this algorithm. The algorithm gives sensitivity of 99.34% and positive predictivity of 96.79%. In the proposed algorithm complicated mathematical operations like Fourier Transform, Hilbert Transform or crosscorrelation are not computed, hence is convenient to realize. © 2016 IEEE.Item Irreversible wavelet compression of radiological images based on visual threshold(Institute of Electrical and Electronics Engineers Inc., 2016) Chandrika, B.K.; Aparna., P.; Sumam David, S.Visually lossless irreversible coding permit compression algorithms to improve the compression gain without disturbing the visual image quality. This paper proposes a novel coding scheme in which wavelet based visual model is embedded into lossless compression algorithm to compress the volumetric medical image data. Obtained experimental results are compared with numerically lossless compression schemes such as Differential Pulse Code Modulation (DPCM), Joint Photographic Experts Group-Lossless (JPEG-LS), JPEG-2000 and High Efficiency Video Coding (HEVC). The proposed method achieves reduced bit rate without deteriorating the visual quality of the resulting images. © 2015 IEEE.