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检索条件"机构=C-DAC: Centre for Development of Advanced Computing"

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Impact of Factory Reset on Android Devices

Impact of Factory Reset on Android Devices

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International conference on computing and Networking Technology (IccNT)

作者： Suman Kumar chaudhry Jinu Kumar S V Satheesh Kumar S Priyanka V S Centre for Development of Advanced Computing(C-DAC) Cyber Security Group(CSG) Thiruvananthapuram India

The usage of smartphones is increasing day by day compared to other gadgets, and as a result, the devices have become common exhibits in most criminal investigations. Forensic analysis of smart phones is incredibly challenging due to the security features enforced on them. The factory reset facility available in smart phones clears all the user-generated data, including third-party applications, from the internal memory and completely resets the device back to its original state as a new phone. Therefore, evidential artefacts such as contacts, messages, call logs, photos, videos, and documents get deleted from the device memory, and recovery becomes *** acquisition of data remnants from mobile phones after factory reset is of utmost importance to forensic investigators. This paper discusses the impact of factory resets on Android devices and describes in detail the evidential artefacts left behind after the factory reset of Android phones.

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Performance Analysis of Machine Learning classifiers for Metastatic Breast cancer Diagnosis Within 90 Days

Performance Analysis of Machine Learning Classifiers for Met...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Vaishnavi chopade Pritam Tambe Sayali Satokar Anuradha D. Thakare Manisha Mantri Department of Computer Science & Engineering (AI&ML) Pimpri Chinchwad College of Engineering Pune Pune India Artificial Intelligence and Quantum Group at C-DAC Centre for Development of Advanced Computing Pune India HPC-Medical and Bioinformatics Group at C-DAC Centre for Development of Advanced Computing Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Metastatic breast cancer, commonly referred to as stage IV breast cancer or advanced breast cancer, is a severe form of the disease, occurs when breast cancer cells migrate to different parts of the body such as the lungs, bones, liver and brain. This study focuses on predicting the of metastatic breast cancer diagnosis within a 90-day window. The dataset was meticulously processed, addressing missing values and encoding categorical variables to ensure a more structured and interpretable dataset. Visualizations were utilized to explore insights and relationships within the data. The results of different machine learning algorithms and deep learning model were evaluated, including Naive Bayes, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine (SVM), Gradient Boosting Machine (GBM), and TabNet. Our findings reveal that GBM and TabNet outperformed other algorithms with respect to performance, achieving accuracies of 81.68% and 81.64%, respectively. Furthermore, each algorithm's performance was evaluated using the AUc-ROc curve.

关键词： Support vector machines Deep learning Logistic regression Machine learning algorithms Prediction algorithms Boosting Breast cancer classification algorithms Decision trees Random forests

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OcPP-based Electric Vehicle Ac fast charging solutions for Indian framework

OCPP-based Electric Vehicle AC fast charging solutions for I...

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IEEE International conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020)

作者： Sapna Ravindran Amal S Bhavya Y V chandrasekar V Power Electronics Group Centre for Development of Advanced Computing (C-DAC) Thiruvananthapuram Kerala India

The accelerated adoption of electric vehicles (EV) requires a rapid, scalable EV charging network. Bharat EV charger Specifications (BEVc-Ac001) was the foundation for all earlier works on 3.3kW Ac Electric Vehicle Supply Equipment (EVSE). Since the onboard chargers from major EV OEMs are less than 3.3 kW, the EV Ac chargers of the same rating were used to meet it. This supports only overnight charging requirements of the EVs with high-capacity battery packs. Now with advancements in technologies, EVs with higher battery capacity for longer ranges are rolled out to meet the user requirements. The present slow Ac chargers limited to 16A have huge charging periods with no charging controls. So, the next level of EV charging at 32A with a single phase, up to 7kW and three phase, up to 22kW Ac EVSE have been developed based on IS-17017 and AIS-138 standards to hasten the charging process. Although expensive than Ac slow chargers, expanding Ac fast charging infrastructure with smart connectivity complying with Open charge Point Protocol (OcPP) is cost-effective than Dc EVSEs. The suggested charger delivers controlled charging with simple installation tasks and establishes a communication mechanism between EV and EVSE. This paper describes the design and execution of a standalone and networked Ac fast EVSE to accommodate the various user types while taking into account economic Indian needs.

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Secure Firmware based Lightweight Trusted Platform Module (FLTPM) for IoT Devices

Secure Firmware based Lightweight Trusted Platform Module (F...

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International Symposium on advanced Networks and Telecommunication Systems (ANTS)

作者： Afzal Warsi Shubham Malav Vishal J. Rathod Shrikrishna S chippalkatti Hari Babu Pasupuleti S D Sudarsan Centre for Development of Advanced Computing (C-DAC) Internet of Things - Research and Development Group Bengaluru Karnataka India

The Internet of Things (IoT) is a network in which physical things that have Internet access can communicate and share data with other connected objects. IoT is currently gaining popularity due to the large range of applications that it allows. When such applications connect to the Internet, they attempt to send data to other devices across the network. Because certain IoT applications transmit crucial data over the Internet, the data must be protected. However, these IoT devices are limited in terms of power, computation and memory, as well as communication capabilities. IoT devices are being used as a gateway into the larger IoT network. As a result, the security of these devices has become critical in order to protect them from numerous security risks and attacks. There are multiple solutions to mitigate the issue but Trusted Platform Module (TPM) is a prominent one that provides hardware based security but designed primarily for large compute platforms (e.g., desktops, laptops, servers, etc.). Because of the limitations of IoT devices, the traditional method is unsuitable for IoT network & devices and a lightweight and optimized mechanism will be required. Firmware-based Lightweight Trusted Platform Module (FLTPM) is our suggested approach for augmenting hardware security to resource-constrained devices, which includes lightweight cryptography algorithms & services related to providing hardware security, and trusted/safe boot mechanisms. The results showed that the proposed solution was optimized, unique, and lightweight for resource constrained IoT devices, and that it functioned well in appropriate environment conditions.

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Real-Time Webcam-Based Eye Tracking for Gaze Estimation: Applications and Innovations

Real-Time Webcam-Based Eye Tracking for Gaze Estimation: App...

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International conference on computing and Networking Technology (IccNT)

作者： Tanmay Jain Samiksha Bhatia chandan Sarkar Priyanka Jain N. K. Jain Centre for Development of Advanced Computing (C-DAC) Delhi India DeenBandhu Chhotu Ram University Of Science And Technology Murthal Haryana

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Webcam-based eye-tracking systems offer a costeffective and accessible solution for capturing eye movements and gaze direction, facilitating applications in human-computer interaction (HcI), virtual reality (VR), and market research. This paper presents a novel webcam-based eye-tracking system, addressing accessibility, affordability, and usability by leveraging ubiquitous webcam technology, eliminating the need for specialized hardware. The methodology improves gaze estimation accuracy and stability by addressing inherent noise and variability by an average of 79 percent. A Multioutput Regressor with Support Vector Regression (SVR) as the base model, refined through linear regression, aligns predicted gaze points with actual screen coordinates. The Kalman filter stabilizes tracking by recursively predicting and updating state estimates. A responsive web application was developed using Flask, HTML, cSS, and JavaScript. Real time pupil detection was facilitated by MediaPipe, with data transmitted via Web-Sockets using the *** library. Frame-by-frame data collection, processed through stacking models and Kalman filtering, enhances accuracy and precision. Applications include early detection of neurological diseases like Parkinson’s, Alzheimer’s, and autism spectrum disorder (ASD), and assisting individuals with disabilities in controlling devices via eye movements. In education, eye-tracking enhances student engagement and personalized learning. Businesses can optimize product placement and advertisement effectiveness. In gaming and VR, integrating eye-tracking enhances natural interactions and immersive experiences. Future developments focus on enhancing algorithmic performance, integrating with AI, AR, and VR, and providing real-time feedback. Ethical guidelines and privacy safeguards are crucial for data security, democratizing eye-tracking technology for diverse applications.

关键词： Solid modeling Accuracy Webcams Estimation Gaze tracking Real-time systems Hardware Kalman filters Usability Pupils

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Adoption and Assessment of Machine Learning Algorithms in Security Operations centre for critical Infrastructure 6th

Adoption and Assessment of Machine Learning Algorithms in Se...

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6th International conference and Exhibition on Smart Grids and Smart cities, ISUW 2020

作者： Yeshwanth, M.V. Kalluri, Rajesh Rao, M. Siddharth Kumar, R. K. Senthil Bindhumadhava, B.S. Center for Development of Advanced Computing C-DAC Knowledge Park No 1 Old Madras Road Byappanahalli Bangalore India

ISBN: (纸本)9789811690075

Perception of establishing a Security Operations centre (SOc) depends on the criticality of information assurance and security operations in response to the ever-changing security threat landscape and adoption of formal security standards. From a security perspective, both IT and OT systems are vulnerable to similar threats—botnets, script kiddies, viruses and other malware. The key difference between the two environments, however, has to do with human safety. Typically critical infrastructures are provided with security measures like firewall, demilitarized zones, Intrusion prevention systems (IPS), Intrusion detection systems (IDS), anti virus etc. However, with new threats always around the corner, it's important to have a dedicated infrastructure to deal with these problems, like the SOc. SOc is a centralized unit that deals with security issues on an organizational and technical level. It can be used by utilities which form a part of critical infrastructure like power systems for any threat detection and taking timely action. Typically, SOc is divided into four layers i.e. data collection source, data processing, technologies and display platform. This paper gives an in depth review and comparison of the machine learning models that can be used by the SOc at different layers. Different machine learning models are to be applied at different layers, so its required to evaluate which models give better accuracy in detecting threats. For evaluation of ML algorithms cases covered in this paper are event correlation, breach detection, rule based processing and anomaly detection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： critical infrastructures

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Real-Time Robotic Vision Application for Health Monitoring of Paddy Plants

Real-Time Robotic Vision Application for Health Monitoring o...

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conference on Information and communication Technology (cIcT)

作者： Gopinath Bej Abhra Pal Tamal Dey Sabyasachi Majumdar Tapas Sutradhar Hena Ray Amitava Akuli Alokesh Ghosh Centre for Development of Advanced Computing (C-DAC) Kolkata India

ISBN: (数字)9781665473125

ISBN: (纸本)9781665473132

This paper proposes real-time inspection of the paddy field using robotic vision technology. chlorophyll content in the plant is the measure of sufficient or deficit amount of nutrients in the field as well as the health condition of the plant. The proposed work estimates the chlorophyll content of the plant while navigating the robotic vehicle in the field. An image acquisition arrangement has been mounted facing the ground on the robotic vehicle which captures on-field images. color information from these images is compared with the digitized reference leaf color chart. Mahalanobis distance has been calculated from the extracted color with all colors in the reference color chart. Using the minimum Mahalanobis distance the best match with the reference chart has been decided. The robotic vehicle has the provision of spraying fertilizer if the matched color is the last two colors (e.g. having a light green shade) from the reference color chart. The result of this chlorophyll estimation approach is encouraging and the accuracy is to the tune of 92%.

关键词： Image color analysis Navigation Estimation Spraying Inspection Real-time systems Information and communication technology

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“Performance Evaluation of Variant calling Tools for Human and Microbial Genomes”

“Performance Evaluation of Variant Calling Tools for Human ...

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International conference on Emerging Trends in Networks and computer communications (ETNcc)

作者： Kirti Bhadhadhara Muthukumar Balamurugan Neeraj Bharti Ruma Banerjee Sunitha Manjari Kasibhatla Rajendra Joshi HPC-Medical & Bioinformatics Applications Group Centre for Development of Advanced Computing (C-DAC) Pune India

Analyzing the data deluge generated by next generation sequencing technologies is a challenging task. Variant calling aids in characterizing phenotype of an organism. The objective of this work is to compare popular variant calling protocols available in terms of their accuracy for human and microbial genomes. Three popular read aligners viz. BWA-MEM, Bowtie2 and NovoAlign were used for the human genome benchmark dataset of Genome in a Bottle (GIAB). Variant calling was performed using four popular variant callers viz. samtools-bcftools, VarScan2, GATK-Hc and DeepVariant. For microbial genomes, BWA-MEM was used for reference mapping followed by variant calling using above-mentioned tools (excluding DeepVariant) along with FreeBayes and PILON. The predicted variants were assessed in terms of true positives, false positives and false negatives. consensus approach was used for identifying SNPs (predicted by more than one variant calling pipeline) in the false positive dataset that satisfied the threshold criteria to be included as true positives. Precision and Recall were measured for evaluating each of the variant calling pipelines. Variant calling pipeline using BWA-MEM for read mapping along with either VarScan2 or samtools-bcftools predicted optimal number of single nucleotide polymorphisms (SNP) for high coverage human datasets. For Indel calling, GATK-Hc with any aligner was preferred over VarScan2 and samtools-bcftools. Variant calling using bcftools, Pilon and GATK-Hc with default parameters for heterogeneous microbial datasets revealed optimal performance. SNP calling with minimum errors was evident when $\sim\mathbf{95}\%$ of the samples from a population was taken into cognizance for variant prediction. Predicted variants were validated by comparing with gold standard data which aided in improved identification of actionable high confidence variants, reduced prediction of false positives and this approach has proved to be valuable for prioritization of act

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Remote Photoplethysmography (rPPG) for contactless Blood Oxygen Saturation Monitoring

Remote Photoplethysmography (rPPG) for Contactless Blood Oxy...

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IEEE International conference on Electronics, computing and communication Technologies (cONEccT)

作者： Yaythish Kannaa G S Sayantani Bhattacharya N Aishwarya Department of Electronics and Communication Engineering Amrita School of Engineering Chennai India Centre for Development of Advanced Computing (C-DAC) Chennai India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

The increasing demand for healthcare technologies in Ambient Assisted Living, accentuated by recent global health challenges, necessitates advanced solutions for continuous and non-invasive vital sign monitoring. This research addresses this imperative by introducing a novel application utilizing remote photoplethysmography-based techniques. Focused on frail and elderly individuals, the application aims to measure key vital signs, like SpO2. Unlike traditional methods, our approach requires no wearable sensors, providing a comfortable and convenient monitoring solution. The application's development involves a comprehensive pipeline for vital sign estimation, overcoming challenges posed by diverse skin tones and facial characteristics in elderly subjects. The system had achieved notable results in terms of accuracy measuring 1.2 in MSE and 1.09 in RMSE. This innovative application represents a significant step forward in non-invasive vital sign monitoring, offering a promising avenue for enhancing healthcare in Ambient Assisted Living environments.

关键词： Patient monitoring Ambient assisted living Telemedicine Refining Medical services cameras Skin

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Benchmarking Matrix Multiplications For Variable Qubit Size And Depth

Benchmarking Matrix Multiplications For Variable Qubit Size ...

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Telecom World (ITU WT), Technical Symposium at ITU

作者： Md Imam Mazhar Abhishek Tiwari Nasir Ali Britant Akshay Patil Rahul Kumar Neiwal Embedded Systems Division Centre for Development of Advanced Computing (C-DAC) Noida Ministry of Electronics and Information Technology (MeitY) New Delhi

ISBN: (数字)9789261390914

ISBN: (纸本)9798350367799

To emulate a quantum computation on a classical computer i.e. the evolution of the unitary operations on the wave function of the particle in quantum mechanics, we have to perform unitary matrix and normalized vector multiplications in the high-level programming languages of Python, c++, Java, etc. Quantum Libraries already available perform the matrix-vector multiplication in the backend using the numpy libraries of Python like Qiskit or use a c++ wrapper to further optimize the runtime it as in Qiskit-Aer Simulators. Since a fully functioning fault-tolerant computer is decades away, it is in our best interest to design new quantum algorithms and develop accelerator test beds for Quantum Emulations. All the quantum computer operations can be emulated on a classical computer, with the only downside being that the matrix multiplications scale up as O (N 3 ) in runtime. In contrast, the quantum computer scales it up as O (N 2 log 2 N), where N = 2 n , where N is the matrix dimension, where n is the number of qubits, so the runtime for quantum emulations on the classical computer increases exponentially with increase in number of qubits and increases linearly with increase in number of depths, complexity wise. Though it is not possible to change the exponential index, it is possible to reduce the runtime for quantum emulations on classical computers by use of GPU and Alveo Accelerator cards, and also code optimization on the software side like using a c++ wrapper. In this paper, we will benchmark the matrix-matrix multiplications on HPc Accelerator cards varying the qubit size and the depth of the quantum circuit and provide a universal mathematical equation for the runtime on the GPU and Alveo Vector cards for two variables of qubit size and quantum circuit depth. So a theoretical limit on qubit size and qubit depth exactly can be established for quantum emulations on present classical supercomputers.

关键词： Runtime Quantum computing Qubit Emulation Graphics processing units Quantum mechanics c++ languages Mathematical models Vectors Quantum circuit

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