Browsing by Author "Aparna., P."

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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. Â© 2016 IEEE.
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. Â© 2016 IEEE.
A Mixed Parallel and Pipelined Efficient Architecture for Intra Prediction Scheme in HEVC
(Taylor and Francis Ltd., 2022) Poola, L.; Aparna., P.
The complexity of intra prediction in High-Efficiency Video Coding (HEVC) is increased significantly due to the incorporation of inherent features like variable-sized quadtree partitioned coding units and 35 angular modes that help in achieving better compression. This paper presents an efficient hardware architecture for the intra prediction that supports and comprises the above aspects and achieves a higher throughput to support high definition (HD) videos. A compact reusable reference buffer structure is implemented to limit the buffer size to 1 KB. A dedicated arithmetic unit to take advantage of the parallelism present in the prediction algorithm is incorporated, which allows the reuse of multipliers to reduce hardware resources. The loading of reference samples to buffers for prediction causes significant delays which are eliminated in our design. The entire architecture functions as a pipelined unit with no data dependency and generates eight samples/clock cycle in parallel. The design is implemented on a Field Programmable Gate Array (FPGA) platform operating at a frequency of 110 MHz. This makes it possible to support 4 K videos at 30 frames per second, with the resource cost of 16 K logic gates and 122 registers. © 2022 IETE.
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. Â© 2012 IEEE.
A visual attention guided unsupervised feature learning for robust vessel delineation in retinal images
(Elsevier Ltd, 2018) Srinidhi, C.L.; Aparna., P.; Rajan, J.
Background and objective: Accurate segmentation of retinal vessels from color fundus images play a significant role in early diagnosis of various ocular, systemic and neuro-degenerative diseases. Segmenting retinal vessels is challenging due to varying nature of vessel caliber, the proximal presence of pathological lesions, strong central vessel reflex and relatively low contrast images. Most existing methods mainly rely on carefully designed hand-crafted features to model the local geometrical appearance of vasculature structures, which often lacks the discriminative capability in segmenting vessels from a noisy and cluttered background. Methods: We propose a novel visual attention guided unsupervised feature learning (VA-UFL) approach to automatically learn the most discriminative features for segmenting vessels in retinal images. Our VA-UFL approach captures both the knowledge of visual attention mechanism and multi-scale contextual information to selectively visualize the most relevant part of the structure in a given local patch. This allows us to encode a rich hierarchical information into unsupervised filtering learning to generate a set of most discriminative features that aid in the accurate segmentation of vessels, even in the presence of cluttered background. Results: Our proposed method is validated on the five publicly available retinal datasets: DRIVE, STARE, CHASE_DB1, IOSTAR and RC-SLO. The experimental results show that the proposed approach significantly outperformed the state-of-the-art methods in terms of sensitivity, accuracy and area under the receiver operating characteristic curve across all five datasets. Specifically, the method achieved an average sensitivity greater than 0.82, which is 7% higher compared to all existing approaches validated on DRIVE, CHASE_DB1, IOSTAR and RC-SLO datasets, and outperformed even second-human observer. The method is shown to be robust to segmentation of thin vessels, strong central vessel reflex, complex crossover structures and fares well on abnormal cases. Conclusions: The discriminative features learned via visual attention mechanism is superior to hand-crafted features, and it is easily adaptable to various kind of datasets where generous training images are often scarce. Hence, our approach can be easily integrated into large-scale retinal screening programs where the expensive labelled annotation is often unavailable. © 2018 Elsevier Ltd
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. Â© 2006 IEEE.
An Approach for Diagnostically Lossless Coding of Volumetric Medical Data Based on Wavelet and Just-Noticeable-Distortion Model
(Taylor and Francis Ltd., 2023) Chandrika, B.K.; Aparna., P.; Sumam David, S.
This paper explores a technique for visually/diagnostically lossless coding in the wavelet domain to effectively compress the three-dimensional medical image data. The quantisation module based on Just Noticeable Distortion (JND) for wavelets guarantees the visual quality in the reconstructed data. This method has been further extended to present the Volume of Interest (VOI) based technique that enables to preserve the quality of the diagnostically significant VOI region. The proposed method tested on several datasets outperforms the state-of-the-art methods. Apart from the conventional quality metric, Human Visual System (HVS) based quality metrics are also used to evaluate the visual quality of the reconstructed image. A subjective and objective evaluation carried out for VOI based coder shows that the quality-compression needs of the medical community are well addressed. © 2023 IETE.
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. Â© 2014 IEEE.
An efficient framework for segmentation and identification of tumours in brain MR images
(Inderscience Enterprises Ltd., 2016) Parameshwari, D.S.; Aparna., P.
In this research work, two efficient textural feature extraction (TFE) algorithms (TFEA-I and TFEA-II) are proposed for a class of brain magnetic resonance imaging (MRI) applications. TFEA-I employs higher order statistical cumulant, namely, Kurtosis in order to generate a feature set based on the probability density function (PDF) of generalised Gaussian model that represents thewavelet coefficient energies of the sub-bands of decomposed image. TFEA-II derives a feature set employing cooccurrence matrix model for second order statistical characterisation of wavelet coefficients. In conjunction with TFEA-I and TFEA-II, we propose segmentation framework to compute coarse and smooth segmented boundaries for the tumour. When compared with the conventional TFEA methods reported in the literature, the use of proposed TFEA-I and TFEA-II results in two important advantages; considerable reduction in the feature set size and elimination of the need for using specialised feature selection/reduction algorithms thereby making them highly attractive for a class of brain MR imaging application. © © 2016 Inderscience Enterprises Ltd.
An efficient parallel-pipelined intra prediction architecture to support DCT/DST engine of HEVC encoder
(Springer Science and Business Media Deutschland GmbH, 2022) Poola, L.; Aparna., P.
The complexity of intra prediction in high-efficiency video coding (HEVC) is increased due to the addition of five variable sized prediction units (PUs) and 35 directional predictions. In this work, we propose an efficient parallel-pipelined architecture that can process 8 samples in parallel for every clock cycle. The functional units needed to predict the PU samples work in a pipelined fashion. With this balanced combination of parallel-pipelined structure, we are able to achieve higher throughput with limited hardware resources than existing literature works. The samples are processed row-wise, so that they can be directly transform coded, thus eliminating the need for an intermediate memory buffer of 8 K between the two modules. A compact reconfigurable reference buffer of size 0.8 KB is incorporated to reduce the read-write latency associated with reference samples’ fetching. A dedicated module for arithmetic operations is used in the intra engine that ensures the reuse of multipliers to increase the hardware efficiency. The architecture so designed supports all the PU sizes and directional modes. The proposed design is tested and implemented on a field-programmable gate array (FPGA) platform operating at 150 MHz frequency to achieve 8 samples throughput with a hardware cost of 16.2 K Look-Up Tables (LUTs) and 5.7 K registers to support HD 4 K real-time video encoding applications. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Analysis of real-time tracking filters implementation in FPGA
(Institute of Electrical and Electronics Engineers Inc., 2018) Haritha, G.; Aparna., P.; Srihari, P.; Satapathi, G.S.
This paper presents real time tracking hardware based on field programmable gate arrays (FPGA). Traditionally, tracking system is simulated in MATLAB environment and an equivalent hardware description language (HDL) code is written for FPGA. But, in this paper a simulated and verified MATLAB code is directly converted into HDL which is used in FPGA. This method improves the efficiency of the real time tracker and flexibility in usage of FPGA. Moreover, the simulated and hardware outputs will be consistent. Three types tracking algorithm namely Kalman filter (KF), extended Kalman filter (EKF), and Î±Î²Î³ filter are implemented to validate the proposed approach. Â© 2018 IEEE.
Automated Method for Retinal Artery/Vein Separation via Graph Search Metaheuristic Approach
(Institute of Electrical and Electronics Engineers Inc., 2019) Srinidhi, C.L.; Aparna., P.; Rajan, J.
Separation of the vascular tree into arteries and veins is a fundamental prerequisite in the automatic diagnosis of retinal biomarkers associated with systemic and neurodegenerative diseases. In this paper, we present a novel graph search metaheuristic approach for automatic separation of arteries/veins (A/V) from color fundus images. Our method exploits local information to disentangle the complex vascular tree into multiple subtrees, and global information to label these vessel subtrees into arteries and veins. Given a binary vessel map, a graph representation of the vascular network is constructed representing the topological and spatial connectivity of the vascular structures. Based on the anatomical uniqueness at vessel crossing and branching points, the vascular tree is split into multiple subtrees containing arteries and veins. Finally, the identified vessel subtrees are labeled with A/V based on a set of hand-crafted features trained with random forest classifier. The proposed method has been tested on four different publicly available retinal datasets with an average accuracy of 94.7%, 93.2%, 96.8%, and 90.2% across AV-DRIVE, CT-DRIVE, INSPIRE-AVR, and WIDE datasets, respectively. These results demonstrate the superiority of our proposed approach in outperforming the state-of-The-Art methods for A/V separation. © 1992-2012 IEEE.
Automated Summarization ofÂ Gastrointestinal Endoscopy Video
(Springer Science and Business Media Deutschland GmbH, 2023) Sushma, B.; Aparna., P.
Gastrointestinal (GI) endoscopy enables many minimally invasive procedures for diagnosing diseases such as esophagitis, ulcer, polyps and cancers. Guided by the endoscopeâ€™s video sequence, a physician can diagnose the diseases and administer the treatment. Unfortunately, due to the huge amount of data generated, physicians are currently discarding procedural video and rely on a small number of carefully chosen images to record a procedure. In addition, when a patient seeks a second opinion, the assessment of lesions in a huge video stream necessitates a thorough examination, which is a time-consuming process that demands much attention. To reduce the length of the video stream, an automated method to generate the summary of endoscopy video recordings consisting only of abnormal frames by using deep convolutional neural networks trained to classify normal, abnormal and uninformative frames is proposed. Results show that our method can efficiently detect abnormal frames and is robust to the variations in the frames. The proposed CNN architecture outperforms the other classification models with an accuracy of 0.9698 with less number of parameters. Â© IFIP International Federation for Information Processing 2023.
Classification of Capsule Endoscopy Images based on Feature Concatenation of Deep Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2021) Biradher, S.; Aparna., P.
Wireless capsule endoscopy (WCE) is a noninvasive way of detecting abnormalities in digestive tract. These abnormalities need to be detected at the early stages before they turn malignant. The classification of these abnormalities has put many challenges due to the variations in lesion shape and color, lighting conditions, and other factors. Existing methods based on handcrafted features give less accuracy due to the limited capability of feature representation. This study proposes a new approach for classifying wireless capsule endoscopy images using feature concatenation of deep convolutional neural network models. The features of two pre-trained models are concatenated and tested using a newly created dataset. The dataset is created using images taken from the Kvasir capsule endoscopy and Red lesion endoscopy dataset which is publicly available. This system improves diagnostic efficiency and brings great assistance to the doctor. Â© 2021 IEEE.
Classification of Wireless Capsule Endoscopy Bleeding Images using Deep Neural Network
(Institute of Electrical and Electronics Engineers Inc., 2022) Biradher, S.; Aparna., P.
Wireless capsule endoscopy (WCE) is an innovative video technique that allows a non-invasive visual inspection of the whole gastrointestinal system. The digestive tract anomalies must be identified in advance to treat them before they turn into hazardous cancers. Detection and classification of digestive tract associated anomalies have been difficult because of various factors like differences in lesion form and color, level of illumination or lighting, etc. Existing approaches based on non-deep learning methods include a manually designed feature extraction step, which is less efficient since these manually designed features may lose essential information and cannot be optimised because they are not part of an end-to-end learning system. This paper provides a new way of classification between bleeding and non-bleeding classes of wireless capsule endoscopy images. The proposed method makes use of a simple Deep Convolutional Neural Network which consists of six convolutional layers alternated with max-pooling layers and this technique is compared with existing ones in terms of different performance metrics. This approach boosts diagnostic efficiency and gives doctors a huge amount of support. Â© 2022 IEEE.
Deep chroma prediction of Wyner–Ziv frames in distributed video coding of wireless capsule endoscopy video
(Academic Press Inc., 2022) Sushma, B.; Aparna., P.
Compression of captured video frames is crucial for saving the power in wireless capsule endoscopy (WCE). A low complexity encoder is desired to limit the power consumption required for compressing the WCE video. Distributed video coding (DVC) technique is best suitable for designing a low complexity encoder. In this technique, frames captured in RGB colour space are converted into YCbCr colour space. Both Y and CbCr representing luma and chroma components of the Wyner–Ziv (WZ) frames are processed and encoded in existing DVC techniques proposed for WCE video compression. In the WCE video, consecutive frames exhibit more similarity in texture and colour properties. The proposed work uses these properties to present a method for processing and encoding only the luma component of a WZ frame. The chroma components of the WZ frame are predicted by an encoder–decoder based deep chroma prediction model at the decoder by matching luma and texture information of the keyframe and WZ frame. The proposed method reduces the computations required for encoding and transmitting of WZ chroma component. The results show that the proposed DVC with a deep chroma prediction model performs better when compared to motion JPEG and existing DVC systems for WCE at the reduced encoder complexity. © 2022 Elsevier Inc.
Distributed video coding based on classification of frequency bands with block texture conditioned key frame encoder for wireless capsule endoscopy
(Elsevier Ltd, 2020) Sushma, B.; Aparna., P.
Wireless capsule endoscopy (WCE) has provided remarkable improvement in diagnosing gastrointestinal disorders by scanning the entire digestive tract. The system still need refinement, to upgrade the quality of images, frame rate and battery life. The principal component of the system that can address these issues is low complexity video compressor. Motivated by low computational complexity requirements of WCE video encoding, this paper presents a distributed video coding framework based on frequency bands classification. The lower frequency bands are used to generate good quality side information (SI) as they exhibit high temporal correlation. This reduces the complexity of hash generation at the encoder, thus eliminating the latency in SI creation. Apart from this, SI creation involves only a simple block search and doesn't depend on Wyner–Ziv (WZ) bands. Also different approach for distributed coding of sub-sampled chroma components of WZ frame is proposed. Low complexity JPEG based key frame encoding is proposed that take advantage of WCE image textural properties to reduce the complexity of encoding smooth blocks by 81% at the quantization and encoding stage. Other novel features include use of discrete Tchebichef transform (DTT), Golomb–Rice code for entropy coding. Performance evaluation shows that the proposed method achieves 60% improvement in compression over Motion JPEG with low computational complexity. © 2020 Elsevier Ltd
Efficient Distributed Video Coding based on principle of syndrome coding
(Inderscience Publishers, 2011) Aparna., P.; Sumam David, S.
Distributed Video Coding (DVC) is a new video coding paradigm, the main objective of which is to reduce the encoder complexity to support a separate class of uplink-friendly applications like wireless video applications, besides achieving the rate-distortion performance of conventional video coders. In this paper, we describe and present the simulation results of the video coding method based on the principle of distributed source coding using Golay codes and then propose an improvement to it. In this, the side information is improved by performing a very coarse motion search at the encoder and transmitting the position of the side information block as the hash information to the decoder, which will help the decoder to perform motion estimation. Copyright © 2011 Inderscience Enterprises Ltd.
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. Â© 2013 IEEE.
GNSS Intentional Interference Mitigation via Average KF Innovation and Pseudo Track Updation
(Institute of Electrical and Electronics Engineers Inc., 2020) Pardhasaradhi, P.; Srihari, S.; Aparna., P.
GNSS based navigation is vulnerable to intentional interferences like jamming, meaconing, and spoofing. In the jamming attack, there is no position estimate from the navigation filter. Whereas, in the case of meaconing and spoofing attacks, the navigation track is misguided by projecting the false measurements onto the GNSS receiver. In the above two cases, the measurements are either corrupted by jamming or spoofed. Therefore, to address this problem, this paper presents a novel pseudo-state update and pseudo-covariance update equations for the Kalman filter (KF) at a given epoch to improve the performance of GNSS track. Simulation results reveals that proposed method enhanced the navigation accuracy (by including two adders, two flip flops and a control unit) compared to other traditional methods. Further, it is evident from the results that the proposed approach provided improved performance in track continuity and position root mean square error(PRMSE) compared to existing techniques. Â© 2020 IEEE.