M Tech Dissertations

Permanent URI for this collectionhttp://drsr.daiict.ac.in/handle/123456789/3

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    ItemOpen Access
    Performance Assessment of Edge Traffic Distribution Routing Algorithm for Graphene Based Network-on-Chip
    (2021) Gupta, Yatin Kumar; Gohel, Bakul; Agrawal, Yash
    Network-on-chip (NoC) has evolved as new paradigm for high-dense interconnect configurations in advanced integrated circuit designs. The increasing numbers of transistor cores with decrease in chip area is the leading motivation behind employment of NoC over SoC architectures. NoC can be addressed a move ahead from computation-centric to communication-centric design and the implementation of scalable communication structures. NoC provides re-configurable interconnections between the different cores in SoC design. It maximizes data transfer speed and reduction in wiring congestion. For further effective enhancing performance of NoCs, it is investigated that incorporation of graphene material can be good for realizing interconnects. As the graphene has the remarkable physical properties it is one of the most important emerging research material for not only the front-end but also for the back-end devices. In this work, edge traffic distribution (ETD) algorithm is explored along with magnificent graphene based interconnects for NoC design. Performance parameters considered are delay, power, energy, and throughput. It is investigated that the ETD routing algorithm leads to reduced delay, higher throughput, and smaller packet loss. Further, it is also analyzed that if the copper based router-to-router link of a mesh based NoC is replaced by a grahene based link then it leads to smaller energy consumption whenever there is a flit transfer from one router to the other. The assessment of NoC structures has been performed using Noxim and SPICE electronic design automation tools.
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    ItemOpen Access
    Adversarial Defense Using Partial Pseudorandom Encryption
    (2021) Kalgutkar, Amruta; Joshi, M. V.
    Machine Learning models like Deep neural networks are vulnerable to adversarial attacks. Carefully crafted adversarial examples force a learned classifier to misclassify the input which can be correctly classified by a human observer. In this thesis, we present a novel approach for defense against such Adversarial attacks. We train and test the model on transformed images in black-box and gray-box scenarios. Here, we propose a transformation technique that partially encrypts every image before training and testing using the Rivest–Shamir–Adleman (RSA) , an asymmetric-key encryption algorithm for visual encryption. The internal structure of the system and the keys generated by RSA are secret. We encrypt only those pixels which are generated by a pseudorandom number generator with a pre-decided secret seed. The images encrypted with such transformation are extremely difficult to decrypt and to launch adaptive adversarial attacks or transferability attacks which makes this visual defense technique against adversarial attack robust. As the field of Adversarial machine learning (AML) is still under study, researchers have not attempted such an approach of training the model on encrypted images for robust learning. State-of-the-art defense techniques are effective but they are computationally expensive and still will not guarantee total security. This idea of partial encryption maintains features and asymmetric key encryption makes it difficult for adversary to guess encryption parameters. This makes the technique novel and hence out-performs state-of-the-art defense techniques.
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    ItemOpen Access
    Analysing User Reviews for Evaluating Game Playability of Mobile Gaming Apps
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Thakar, Swapnil; Tiwari, Saurabh
    The playability of a game depends on the players� experience in terms of functionality,usability, and satisfaction. Mobile gaming has recently evolved because ofthe availability of suitable hardware, configurable mobile devices, and the abilityto download games from the Android and iOS platforms. Most online gamingstores allow customers to submit their reviews about gameplay, issues, and functionalitiespublicly. Game developers can better grasp such consumer issues byexamining player feedback and increasing how well-liked a game is among players.We have mapped the playability of S�nchez�s model with Schwartz�s theoryof human values and analyzed 20,346 user/player reviews from the top 15 gameapps in the Google Play Store. We have also created a labelled dataset of eachplayability category of S�nchez�s model. Finally, we applied a machine learningmodel to support the automatic classification of a review to a specific playabilitycategory violation. Our analysis shows that 30% of the reviews show human valuesviolations, consequently affecting game playability. We found that Socialism isthe most violated and Emotion is the least violated value category. We also foundthat only 18% of the user reviews received responses from the game app developersfor the value violations. Using fine-grained feature extraction, we found thetop 42 functionalities, issues, and concerns for the violations. The analysis resultsof our study give developers a foundation for creating apps that consider users�values for ensuring better playability of mobile game apps.
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    ItemOpen Access
    Privacy-Preserving Iris Based Authentication System
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Agrawal, Radha; Singh, Priyanka; Joshi, Manjunath V.
    Biometric authentication systems have gained immense popularity due to theirability to provide secure and convenient authentication. However, the leakageof sensitive biometric data can compromise an individual�s privacy and security.To address this issue, a privacy-preserving biometric authentication system basedon iris data is proposed in this paper. The framework exploits the homomorphicproperties to process encrypted data, thereby ensuring the privacy of sensitivedata, even while using the services of third-party cloud service providers (CSPs).In the initial stage of the experiment, we encrypt the data, and comparison wasdone by using hamming distance, but after completion of the first experiment,we realized that data can be morphed through an insecure channel by using multipleattacks to overcome this we have proposed framework were morphing isperformed on the iris data by using a man-in-the-middle attack. Two iris identificationAlgorithms are proposed, with a success rate of over 60% and a false matchrate of 5%, and are vulnerable to morph attacks. We also examine how comparablethe original and morphed iris images must be. Using original images, we presentour findings for morphing iris detection. The proposed privacy-preserving biometricauthentication system offers a robust framework that minimizes time complexitycompared to other state-of-the-art approaches. This framework ensuresthe privacy of sensitive data and provides a secure biometric authentication system.
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    ItemOpen Access
    Shadow Detection and Removal from video using Deep Learning
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Dodiya, Krutika; Khare, Manish; Gohel, Bakul
    The removal of shadow from images is crucial in computer vision as it can enhancethe interpretability and visual quality of images. This research work proposesa cascade U-Net architecture for the shadow removal, consisting of twostages of U-Net Architecture. In the first stage, a U-Net is trained using theshadow images and their corresponding ground truth to predict the shadow freeimages. The second stage uses the predicted shadow free images and groundtruth as input to another U-Net, which further refines the shadow removal results.This cascade U-Net architecture enables the model to learn and refine theshadow removal progressively, leveraging both the initial predictions and groundtruth.Experimental evaluations on benchmark datasets demonstrate that our approachachieves notably good performance in both qualitative and quantitative evaluations.By using both objective metrics such as Structural Similarity Index(SSIM),and Root mean Square Error (RMSE), and subjective evaluations where humanobservers rate the quality of the shadow removal results, our approach was foundto outperform other state-of-the-art methods. Overall, our proposed cascade UNetarchitecture offers a promising solution for the shadow removal that canimprove image quality and interpretability
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    ItemOpen Access
    Image Processing Using Digital Programming on FPGA
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Kachchhi, Hardi; Agrawal, Yash; Khare, Manish
    Image processing is a way to transform an image into digital form and after thatperform some operations on it that helps to improve images for human interpretationand extract useful information from it. It is essential for a wide range ofapplications. It allows for enhancing and restoring images, extracting featuresfor object recognition, compressing images for efficient storage and transmission,analyzing images for computer vision tasks, enabling medical diagnostics andtreatment, and interpreting data from remote sensing.Field Programmable Gate Array (FPGA) is preferred for image processing dueto their parallel processing capabilities, reconfigurability, low latency, energy efficiency,pipelining support, customization options, real-time processing capabilities,and ease of integration. These advantages make FPGAs a powerful tool forimplementing high-performance and efficient image processing solutions acrossvarious applications.To implement various filters in Image processing, we have developed a methodthat performs various edge detection techniques using FPGAs and displaying theimage on the monitor through Video Graphics Array (VGA)Controller. Edge detectionfilters and blurring filters are an indispensable part of Image processing invarious fields due to their ability to extract information, enhance visual quality,and enable decision-making based on visual data .
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    On the Robustness of Federated Learning towards Various Attacks
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Yagnik, Shrey Devenkumar; Singh, Priyanka; Joshi, Manjunath V.
    A study based on Federated Learning (FL), i.e., a kind of decentralized learningthat consists of local training among the clients, and the central server returnsthe federated average. Deep learning models have been used in numeroussecurity-critical settings since they have performed well on various tasks. Here,we study different kinds of attacks on FL. FL has become a popular distributedtraining method because it enables users to work with large datasets without sharingthem. Once the model has been trained using data on local devices, only theupdated model parameters are sent to the central server. The FL approach is distributed.Thus, someone could launch an attack to influence the model�s behavior.In this work, we conducted the study for a Backdoor attack, a black-box attackwhere we added a few poisonous instances to check the model�s behavior duringtest time. Also, we conducted three types of White-Box attacks, i.e., Fast GradientSign Method (FGSM), Carlini-Wagner (CW), and DeepFool. We conductedvarious experiments using the standard CIFAR10 dataset to alter the model�s behavior.We used ResNet20 and DenseNet as the Deep Neural Networks. Wefound some adversarial samples upon which the required perturbation is addedto fool the model upon giving the misclassifications. This decentralized approachto training can make it more difficult for attackers to access the training data, butit can also introduce new vulnerabilities that attackers can exploit. We found outthat the expected behavior of the model could be compromised without havingmuch difference in the training accuracy.
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    Semantic Segmentation Based Object Detection for Autonomous Driving
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Prajapati, Harsh; Maiti, Tapas Kumar
    This research focuses on solving the autonomous driving problem which is necessaryto fulfill the increasing demand of autonomous systems in today�s world.The key aspect in addressing this challenge is the real-time identification andrecognition of objects within the driving environment. To accomplish this, weemploy the semantic segmentation technique, integrating computer vision, machinelearning, deep learning, the PyTorch framework, image processing, and therobot operating system (ROS). Our approach involves creating an experimentalsetup using an edge device, specifically a Raspberry Pi, in conjunction with theROS framework. By deploying a deep learning model on the edge device, we aimto build a robust and efficient autonomous system that can accurately identifyand recognize objects in real time.
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    Features for Speech Emotion Recognition
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Uthiraa, S.; Patil, Hemant A.
    The easiest and most effective or natural way of communication is through speech;the emotional aspect of speech leads to effective interpersonal communication.As technological advancements continue to proliferate, the dependence of humanson machines is also increasing, thereby making it imperative to establish efficientmethods for Speech Emotion Recognition (SER) to ensure effective humanmachineinteraction. This thesis focuses on understanding acoustic characteristicsof various emotions and their dependence on the culture and languageused. It then proposes a new feature set, namely, Constant Q Pitch Coefficients(CQPC) and Constant Q Harmonic Coefficients (CQHC) from Constant Q Transform,which captures high resolution pitch and harmonic information, respectively.Further, this thesis focuses on less explored excitation source-based featuresand proposes a novel Linear Frequency Residual Cepstral Coefficients (LFRCC)feature set for the same. Phase-based features, namely Modified Group DelayCepstral Coefficients (MGDCC), is proposed to capture vocal tract and vocal foldinformation well for emotion classification. The recently developed AutomaticSpeech Recognition (ASR) model, Whisper, is used to analyze cross-database SER.This thesis extends the LFRCC idea on the infant cry classification problem. Lastly,a local API is developed for SER.
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    Model Based Testing and Model Checking : An Efficient Combination
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2023) Mishra, Rohit Ajaykumar; Tiwari, Saurabh
    This thesis aims to combine MBT with model analysis to provide an overall frame-work for feedback-based model analysis. We have used an MBT tool, Graph-Walker, and a model checker, UPPAAL, for transformation, feedback, and analy-sis. GW2UPPAAL1is an existing tool that transforms the GraphWalker model intoUPPAAL timed automata and supports a combined analysis and testing process.The tool enables the automatic verification of reachability and deadlocks freedomproperties to exploit the results obtained from this analysis step to improve thetest model before generating and executing test cases on the system under test.However, based on model analysis results, the test engineer must manually cre-ate the new GraphWalker model, which may be time-consuming and error-prone.We have developed a hybrid approach (a.k.a. UPPAAL2GW) to transform theUPPAAL-derived model to GraphWalker to provide automated feedback of themodel checker to the MBT model, which helps the test engineer to use the modi-fied GraphWalker model for test case generation. We have evaluated the overallapproach of the toolchain by seeding mutations into the models created by indus-trial practitioners and verifying whether the tool provides automated feedbackto the test engineer. We have also used Graphviz to reflect changes in the MBTmodels before and after the modifications. Furthermore, we have integrated bothtools for automated analysis and feedback. The integration of GW2UPPAAL andUPPAAL2GW tools bridges the gap between MBT and model checking and en-sures the overall analysis and feedback for MBT test case generation.