Browsing by Author "Guddeti, G.R.M."

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A comparative performance evaluation of independent component analysis in medical image denoising
(2011) Arakeri, M.P.; Guddeti, G.R.M.
Medical images are often corrupted by noise arising in image acquisition process. Accurate diagnosis of the disease requires that medical images be sharp, clear and free of noise. Thus, image denoising is one of the fundamental tasks required by medical image analysis. There exist several denoising techniques for medical images like Median, Wavelet, Wiener, Average and Independent component analysis (ICA) filters. The independent component analysis is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals. In this paper, ICA has been used to separate out noise from the image to provide important diagnostic information to the physician and its usefulness is demonstrated by comparing its performance with other noise filtering methods. The performance of the ICA and other denoising techniques is evaluated using the metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Absolute Error (MAE) and Mean Structural Similarity Index (MSSIM). The ICA based noise filtering technique gives 25.8245 dB of PSNR, 0.7312 of MAE and 0.9120 of SSIM. The experimental results and the performance comparisons show that ICA proves to be the effective method in eliminating noise from the medical image. Â© 2011 IEEE.
A framework for estimating geometric distortions in video copies based on visual-audio fingerprints
(Springer-Verlag London Ltd, 2015) Roopalakshmi, R.; Guddeti, G.R.M.
Spatio-temporal alignments and estimation of distortion model between pirate and master video contents are prerequisites, in order to approximate the illegal capture location in a theater. State-of-the-art techniques are exploiting only visual features of videos for the alignment and distortion model estimation of watermarked sequences, while few efforts are made toward acoustic features and non-watermarked video contents. To solve this, we propose a distortion model estimation framework based on multimodal signatures, which fully integrates several components: Compact representation of a video using visual-audio fingerprints derived from Speeded Up Robust Features and Mel-Frequency Cepstral Coefficients; Segmentation-based bipartite matching scheme to obtain accurate temporal alignments; Stable frame pairs extraction followed by filtering policies to achieve geometric alignments; and distortion model estimation in terms of homographic matrix. Experiments on camcorded datasets demonstrate the promising results of the proposed framework compared to the reference methods. © 2013, Springer-Verlag London.
A Google Glass Based Real-Time Scene Analysis for the Visually Impaired
(Institute of Electrical and Electronics Engineers Inc., 2021) Ali A, H.; Rao, S.U.; Ranganath, S.; Ashwin, T.S.; Guddeti, G.R.M.
Blind and Visually Impaired People (BVIP) are likely to experience difficulties with tasks that involve scene recognition. Wearable technology has played a significant role in researching and evaluating systems developed for and with the BVIP community. This paper presents a system based on Google Glass designed to assist BVIP with scene recognition tasks, thereby using it as a visual assistant. The camera embedded in the smart glasses is used to capture the image of the surroundings, which is analyzed using the Custom Vision Application Programming Interface (Vision API) from Azure Cognitive Services by Microsoft. The output of the Vision API is converted to speech, which is heard by the BVIP user wearing the Google Glass. A dataset of 5000 newly annotated images is created to improve the performance of the scene description task in Indian scenarios. The Vision API is trained and tested on this dataset, increasing the mean Average Precision (mAP) score from 63% to 84%, with an IoU > 0.5. The overall response time of the proposed application was measured to be less than 1 second, thereby providing accurate results in real-time. A Likert scale analysis was performed with the help of the BVIP teachers and students at the 'Roman Catherine Lobo School for the Visually Impaired' at Mangalore, Karnataka, India. From their response, it can be concluded that the application helps the BVIP better recognize their surrounding environment in real-time, proving the device effective as a potential assistant for the BVIP. © 2013 IEEE.
A GPU framework for sparse matrix vector multiplication
(Institute of Electrical and Electronics Engineers Inc., 2014) Bayyapu, B.; Guddeti, G.R.M.; Raghavendra, P.S.
The hardware and software evolutions related to Graphics Processing Units (GPUs), for general purpose computations, have changed the way the parallel programming issues are addressed. Many applications are being ported onto GPU for achieving performance gain. The GPU execution time is continuously optimized by the GPU programmers while optimizing pre-GPU computation overheads attracted the research community in the recent past. While GPU executes the programs given by a CPU, pre-GPU computation overheads does exists and should be optimized for a better usage of GPUs. The GPU framework proposed in this paper improves the overall performance of the application by optimizing pre-GPU computation overheads along with GPU execution time. This paper proposes a sparse matrix format prediction tool to predict an optimal sparse matrix format to be used for a given input matrix by analyzing the input sparse matrix and considering pre-GPU computation overheads. The sparse matrix format predicted by the proposed method is compared against the best performing sparse matrix formats posted in the literature. The proposed model is based on the static data that is available from the input directly and hence the prediction overhead is very small. Compared to GPU specific sparse format prediction, the proposed model is more inclusive and precious in terms of increasing overall application's performance. Â© 2014 IEEE.
A hybrid algorithm for disparity calculation from sparse disparity estimates based on stereo vision
(Institute of Electrical and Electronics Engineers Inc., 2014) Mukherjee, S.; Guddeti, G.R.M.
In this paper, we have proposed a novel method for stereo disparity estimation by combining the existing methods of block based and region based stereo matching. Our method can generate dense disparity maps from disparity measurements of only 18% pixels of either the left or the right image of a stereo image pair. It works by segmenting the lightness values of image pixels using a fast implementation of K-Means clustering. It then refines those segment boundaries by morphological filtering and connected components analysis, thus removing a lot of redundant boundary pixels. This is followed by determining the boundaries' disparities by the SAD cost function. Lastly, we reconstruct the entire disparity map of the scene from the boundaries' disparities through disparity propagation along the scan lines and disparity prediction of regions of uncertainty by considering disparities of the neighboring regions. Experimental results on the Middlebury stereo vision dataset demonstrate that the proposed method outperforms traditional disparity determination methods like SAD and NCC by up to 30% and achieves an improvement of 2.6% when compared to a recent approach based on absolute difference (AD) cost function for disparity calculations [1]. Â© 2014 IEEE.
A novel approach for multi-dimensional variable sized virtual machine allocation and migration at cloud data center
(Institute of Electrical and Electronics Engineers Inc., 2017) Sharma, N.K.; Guddeti, G.R.M.
In this paper, we propose a branch-and-bound based exact algorithm for allocating multi-dimensional variable sized VMs at the cloud data center. Further, an energy efficient VMs migration technique is proposed to reduce the energy consumption and thus avoids the Service Level Agreement (SLA) violation at the cloud data center. Â© 2017 IEEE.
A novel approach to video copy detection using audio fingerprints and PCA
(Elsevier B.V., 2011) Roopalakshmi, R.; Guddeti, G.R.M.
In Content-Based Copy detection (CBCD) literature, numerous state-of-the-art techniques are primarily focusing on visual content of video. Exploiting audio fingerprints for CBCD problem is necessary, because of following rea-sons: audio content constitutes an indispensable information source; transformations on audio content is limited compared to visual content. In this paper, a novel CBCD approach using audio features and PCA is proposed, which includes two stages: first, multiple feature vectors are computed by utilizing MFCC and four spectral descriptors; second, features are further processed using PCA, to provide compact feature description. The results of experiments tested on TRECVID-2007 dataset, demonstrate the efficiency of proposed method against various transformations. Â© 2011 Published by Elsevier Ltd.
A novel bio-inspired load balancing of virtualmachines in cloud environment
(Institute of Electrical and Electronics Engineers Inc., 2015) Ashwin, T.S.; Domanal, S.G.; Guddeti, G.R.M.
Load Balancing plays an important role in managing the software and the hardware components of cloud. In this present scenario the load balancing algorithm should be efficient in allocating the requested resource and also in the usage of the resources so that the over/underutilization of the resources will not occur in the cloud environment. In the present work, the allocation of all the available Virtual Machines is done in an efficient manner by Particle Swarm Optimization load balancing algorithm. Further, we have used cloudsim simulator to compare and analyze the performance of our algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all the available virtual machines uniformly i.e, without any under/over utilization and also the average response time is better compared to all existing scheduling algorithms. Â© 2014 IEEE.
A real time facial emotion recognition using depth sensor and interfacing with Second Life based Virtual 3D avatar
(Institute of Electrical and Electronics Engineers Inc., 2014) Kakarla, M.; Guddeti, G.R.M.
Facial emotions are the nonverbal form of communication and we find it very useful when we have no words to express our feelings then we use the gestures to express the same. To detect and track emotions dynamically, we have to use Kinect Sensor since it has camera to produce 3D depth maps. The Facial emotions are detected in real-time, by applying the mesh over the tracked face and thereby identifying the desired points for extracting the features. Facial Action Code Systems (FACS) and Facial Animated Parameters (FAP) are the places of interest for depicting the emotions. Second Life is the 3D Virtual world where people are able to create a digital character called 'Avatar' and thereby interacting with the people in the virtual world. The Avatar, which shows some gestures, needs some emotions in a particular scenario. Using the real-time facial emotion recognition based on Kinect depth sensor, avatar emotions are also generated in the 3D Virtual world using Second Life. This idea can be further extended to serve as a communication link so that speech and hearing impaired people can express their emotions. Â© 2014 IEEE.
An android GPS-based navigation application for blind
(Association for Computing Machinery, 2014) Nisha, K.K.; Pruthvi, H.R.; Hadimani, S.N.; Guddeti, G.R.M.; Ashwin, T.S.; Domanal, S.G.
Visual Impairment makes the person depend on another person for all his works and daily chores. Through the application proposed in this paper, we aim to eliminate this dependency of a visually impaired person when travelling from one place to another. The main goal is to provide information regarding the current location, how much distance and time is required to reach the destination as well as provide the user with the directions and turns to be taken while travelling by providing continuous audio feedback in his understandable language. Â© is held by the author/owner(s).
Compact and efficient CBCD scheme based on integrated color features
(2011) Roopalakshmi, R.; Guddeti, G.R.M.
Content-Based Copy Detection (CBCD) is an emergent research topic and has been extensively studied recently. Color is one of the most important and easily recognizable features of visual content. To address efficiency and effectiveness issues of CBCD systems, in this article a compact and computationally inexpensive CBCD scheme using MPEG-7 Dominant Color Descriptor is proposed. This paper proposes a novel dominant color extraction technique, which solves the intrinsic problems of existing color clustering techniques. Experimental results show that the proposed scheme improves detection accuracy up to 38%, and also supports significant reduction in total computational cost up to the extent of 91%, when compared with that of existing schemes against various video transformations. Â© 2011 IEEE.
Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images
(Springer London, 2015) Arakeri, M.P.; Guddeti, G.R.M.
The manual analysis of brain tumor on magnetic resonance (MR) images is time-consuming and subjective. Thus, to avoid human errors in brain tumor diagnosis, this paper presents an automatic and accurate computer-aided diagnosis (CAD) system based on ensemble classifier for the characterization of brain tumors on MR images as benign or malignant. Brain tumor tissue was automatically extracted from MR images by the proposed segmentation technique. A tumor is represented by extracting its texture, shape, and boundary features. The most significant features are selected by using information gain-based feature ranking and independent component analysis techniques. Next, these features are used to train the ensemble classifier consisting of support vector machine, artificial neural network, and k-nearest neighbor classifiers to characterize the tumor. Experiments were carried out on a dataset consisting of T1-weighted post-contrast and T2-weighted MR images of 550 patients. The developed CAD system was tested using the leave-one-out method. The experimental results showed that the proposed segmentation technique achieves good agreement with the gold standard and the ensemble classifier is highly effective in the diagnosis of brain tumor with an accuracy of 99.09 % (sensitivity 100 % and specificity 98.21 %). Thus, the proposed system can assist radiologists in an accurate diagnosis of brain tumors. © 2013, Springer-Verlag London.
Content-Based Video Copy Detection scheme using motion activity and acoustic features
(Springer Verlag service@springer.de, 2014) Roopalakshmi, R.; Guddeti, G.R.M.
This paper proposes a new Content-Based video Copy Detection (CBCD) framework, which employs two distinct features namely, motion activity and audio spectral descriptors for detecting video copies, when compared to the conventional uni-feature oriented methods. This article focuses mainly on the extraction and integration of robust fingerprints due to their critical role in detection performance. To achieve robust detection, the proposed framework integrates four stages: 1) Computing motion activity and spectral descriptive words; 2) Generating compact video fingerprints using clustering technique; 3) Performing pruned similarity search to speed up the matching task; 4) Fusing the resultant similarity scores to obtain the final detection results. Experiments on TRECVID-2009 dataset demonstrate that, the proposed method improves the detection accuracy by 33.79% compared to the referencemethods. The results also prove the robustness of the proposed framework against different transformations such as fast forward, noise, cropping, picture-inpicture and mp3 compression. Â© Springer International Publishing Switzerland 2014.
Effective E-learning using 3D Virtual Tutors and WebRTC Based Multimedia Chat
(Institute of Electrical and Electronics Engineers Inc., 2014) Kokane, A.; Singhal, H.; Mukherjee, S.; Guddeti, G.R.M.
This paper discuss the novel implementation of Learner Centered Design Approach of E-learning System using 3D Virtual Tutors [1] and further enhances this work in facilitating young learners to interact with human tutors using WebRTC Based Multimedia Chat system. The present work adds three main features such as: Firstly, addition of live video lecture session by which students can interact with the tutor just like video call of Skype system. Secondly, the presentation of 3D virtual tutors' narrations of articles in the text form of live transcriptions of avatars' speech. Thirdly, introduction of timed quiz by which a real-life objective examination can be mimicked and thereby evaluating the performance of students. Lastly, we present a comparative evaluation of the original system and its improved version with respect to responses received from a large pool of young learners using the system. Â© 2014 IEEE.
Efficient video copy detection using simple and effective extraction of color features
(2011) Roopalakshmi, R.; Guddeti, G.R.M.
In the present Multimedia era, the exponential growth of illegal videos and huge piracy issues increased the importance of Content Based video Copy Detection (CBCD) techniques. CBCD systems require compact and computationally efficient descriptors for detecting video copies. In this paper, we propose a simple and efficient video signature scheme using Dominant Color Descriptors of MPEG-7 standard in order to implement the proposed CBCD task. Experimental results show that the proposed approach yields better detection rates when compared to that of existing approaches, against common transformations like Contrast change, Noise addition, Rotation, Zooming, Blurring etc. Further, evaluation results also prove that our scheme is computationally efficient by supporting substantial reduction in the total computational cost up to the extent of 65% when compared to that of existing schemes. Â© 2011 Springer-Verlag.
Ember: A smartphone web browser interface for the blind
(Association for Computing Machinery, 2014) Jassi, I.S.; Ruchika, S.; Pulakhandam, S.; Mukherjee, S.; Ashwin, T.S.; Guddeti, G.R.M.
Ember is a smartphone web browser interface designed exclusively for the blind user. The Ember keypad enables blind users to type using their knowledge of Braille. The interface is intuitive to the blind user because the layout consists of a very few large targets and remains consistent throughout the application. The verbal command option provides another dimension for user-interface interaction. Twelve out of thirteen users found that Ember verbal command navigation was easier than using a traditional web browser. Ten out of thirteen users found it faster to use the Ember tactile method of navigation compared to a traditional web browser. The learning rate for both the tactile and verbal command methods was faster compared to the learning rate associated with a traditional web browser layout. Finally it was seen that five out of five users found it significantly faster to use the Ember keypad compared to the QWERTY keypad. Â© 2014 ACM.
Fast Convergence to Near Optimal Solution for Job Shop Scheduling Using Cat Swarm Optimization
(Springer Verlag service@springer.de, 2017) Dani, V.; Sarswat, A.; Swaroop, V.; Domanal, S.; Guddeti, G.R.M.
Job Shop Scheduling problem has wide range of applications. However it being a NP-Hard optimization problem, always finding an optimal solution is not possible in polynomial amount of time. In this paper we propose a heuristic approach to find near optimal solution for Job Shop Scheduling Problem in predetermined amount of time using Cat Swarm Optimization. Novelty in our approach is our non-conventional way of representing position of cat in search space that ensures advantage of spatial locality is taken. Further while exploring the search space using randomization, we never explore an infeasible solution. This reduces search time. Our proposed approach outperforms some of the conventional algorithms and achieves nearly 86% accuracy, while restricting processing time to one second. Â© 2017, Springer International Publishing AG.
Fault Tree Analysis: A Review on Analysis, Simulation Tools and Reliability Dataset for Safety-critical Systems
(World Researchers Associations, 2025) Madhusmita, D.; Mohan, R.; Guddeti, G.R.M.
Risk analysis is a crucial and prominent method to analyze the dependability attributes of safety-critical systems. Risk analysis comprises a wide variety of State-of-the-Art techniques. Out of these, this study only focuses on the Fault Tree Analysis (FTA) technique. Except for the evaluation techniques, we also paid attention to the survey of simulation tools along with the reliability datasets. © 2025, World Researchers Associations. All rights reserved.
Fog-Based Video Surveillance System forÂ Smart City Applications
(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2021) Natesha, B.V.; Guddeti, G.R.M.
With the rapid growth in the use of IoT devices in monitoring and surveillance environment, the amount of data generated by these devices is increased exponentially. There is a need for efficient computing architecture to push the intelligence and data processing close to the data source nodes. Fog computing will help us to process and analyze the video at the edge of the network and thus reduces the service latency and network congestion. In this paper, we develop fog computing infrastructure which uses the deep learning models to process the video feed generated by the surveillance cameras. The preliminary experimental results show that using different deep learning models (DNN and SNN) at the different levels of fog infrastructure helps to process the video and classify the vehicle in real time and thus service the delay-sensitive applications. Â© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
GPU accelerated inexact matching for multiple patterns in DNA sequences
(Institute of Electrical and Electronics Engineers Inc., 2014) Rastogi, P.; Guddeti, G.R.M.
DNA sequencing technology generates millions of patterns on Every run of the machine and it poses a challenge for matching these patterns to the reference genome effectively with high execution speed. The main idea here is inexact matching of patterns with mismatches and gaps (insertions and deletions). In Inexact match up pattern DNA sequence is to be matched with some allowed number of errors. Here we have considered 2 errors. Errors can be mismatches or gaps. Existing algorithm as SOAP3 performs inexact matching on GPU with mismatches only. SOAP3 doesn't consider gaps (insertion and deletion). General Purpose Graphical Processing Unit (GPGPU) is an effective solution in terms of the cost and speed and there by providing a high degree of parallelism. This paper presents a parallel implementation of multiple pattern inexact matching in genome reference using CUDA based on BWT. The algorithm incorporates DFS (Depth First Search) Strategy for For matching multiple patterns, each thread of GPGPU is provided with a different pattern and hence millions of patterns can be matched using only one CUDA kernel. Since the memory of the GPU is limited then memory management should handled carefully. Synchronization of multiple threads is provided in order to prevent illegal access to the shared memory. GPU results are compared with that of CPU execution Experimental results of the proposed methodology achieved an average speedup factor of seven as compared to that of CPU execution. Â© 2014 IEEE.