Rajesh Kumar Tripathy

25 papers Journal 21Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Automated human emotion recognition from EEG signals using chaotic local binary pattern and ensemble learning. Int. J. Mach. Learn. Cybern. Himanshu Chhabra, Raveendrababu Vempati, Urvashi Chauhan, Prince Jain, Rajesh Kumar Tripathy, Lakhan Dev Sharma
2025	J	`jnl`	Cross-Subject Emotion Recognition From Multichannel EEG Signals Using Multivariate Decomposition and Ensemble Learning. IEEE Trans. Cogn. Dev. Syst. Raveendrababu Vempati, Lakhan Dev Sharma, Rajesh Kumar Tripathy
2025	J	`jnl`	Detection of Bundle Branch Block From 12-Lead ECG Using Fifth-Order Tensor-Domain Machine Learning. IEEE Trans. Hum. Mach. Syst. Chhaviraj Chauhan, Rajesh Kumar Tripathy, Monika Agrawal
2025	J	`jnl`	SERN-AwGOP: Squeeze-and-Excitation Residual Network With an Attention-Weighted Generalized Operational Perceptron for Atrial Fibrillation Detection. IEEE Access Soumil Hooda, Rajesh Kumar Tripathy
2025	J	`jnl`	Time-frequency domain machine learning for detection of epilepsy using wearable EEG sensor signals recorded during physical activities. Biomed. Signal Process. Control. Shaswati Dash, Dinesh Kumar Dash, Rajesh Kumar Tripathy, Ram Bilas Pachori
2024	J	`jnl`	A Lightweight Deep Convolutional Neural Network Implemented on FPGA and Android Devices for Detection of Breast Cancer Using Ultrasound Images. IEEE Access Aditya Vinod, Prabhav Guddati, Amit Kumar Panda, Rajesh Kumar Tripathy
2024	J	`jnl`	FPGA Implementation of the Proposed DCNN Model for Detection of Tuberculosis and Pneumonia Using CXR Images. IEEE Embed. Syst. Lett. Prabhav Guddati, Shaswati Dash, Rajesh Kumar Tripathy
2024	—	`conf`	Multi-Frequency-Aware Patch Adversarial Learning for Neural Point Cloud Rendering. AIM Jay Karhade, Haiyue Zhu, Ka-Shing Chung, Rajesh Kumar Tripathy, Wei Lin, Marcelo H. Ang
2023	J	`jnl`	Detection of Myocardial Infarction From 12-Lead ECG Trace Images Using Eigendomain Deep Representation Learning. IEEE Trans. Instrum. Meas. Sathvik Bhaskarpandit, Anurag Gade, Shaswati Dash, Dinesh Kumar Dash, Rajesh Kumar Tripathy, Ram Bilas Pachori
2023	J	`jnl`	Dyadic boundary points based empirical wavelet transform for the elimination of eye movement and eye blink-based ocular artifacts from EEG signals. Biomed. Signal Process. Control. Shaswati Dash, Pranjali Gajbhiye, Phattarapong Sawangjai, Guntitat Sawadwuthikul, Thapanun Sudhawiyangkul, Rajesh Kumar Tripathy, Ram Bilas Pachori
2023	J	`jnl`	Exploiting Tunable Q-Factor Wavelet Transform Domain Sparsity to Denoise Wrist PPG Signals. IEEE Trans. Instrum. Meas. Shresth Gupta, Anurag Singh, Abhishek Sharma, Rajesh Kumar Tripathy
2023	J	`jnl`	Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series. IEEE Access Rakesh Reddy Yakkati, Sreenivasa Reddy Yeduri, Rajesh Kumar Tripathy, Linga Reddy Cenkeramaddi
2023	J	`jnl`	Multiscale Analysis Domain Interpretable Deep Neural Network for Detection of Breast Cancer Using Thermogram Images. IEEE Trans. Instrum. Meas. Anurag Gade, Dinesh Kumar Dash, T. Mita Kumari, Samit Kumar Ghosh, Rajesh Kumar Tripathy, Ram Bilas Pachori
2023	J	`jnl`	Patient specific higher order tensor based approach for the detection and localization of myocardial infarction using 12-lead ECG. Biomed. Signal Process. Control. Chhaviraj Chauhan, Rajesh Kumar Tripathy, Monika Agrawal
2023	—	`conf`	Tensor-Domain Machine Learning Based Cardiac Diseases Detection Using 12-lead ECG. NCC Chhaviraj Chauhan, Rajesh Kumar Tripathy, Monika Agrawal
2023	J	`jnl`	Time frequency domain deep CNN for automatic background classification in speech signals. Int. J. Speech Technol. Rakesh Reddy Yakkati, Sreenivasa Reddy Yeduri, Rajesh Kumar Tripathy, Linga Reddy Cenkeramaddi
2023	—	`conf`	Time-Frequency Domain Modified Vision Transformer Model for Detection of Atrial Fibrillation using Multi-lead ECG Signals. NCC Haarika Manda, Shaswati Dash, Rajesh Kumar Tripathy
2022	J	`jnl`	Automated Heart Sound Activity Detection From PCG Signal Using Time-Frequency-Domain Deep Neural Network. IEEE Trans. Instrum. Meas. Samit Kumar Ghosh, R. N. Ponnalagu, Rajesh Kumar Tripathy, Ganapati Panda, Ram Bilas Pachori
2022	J	`jnl`	Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network. Appl. Soft Comput. Neha Muralidharan, Shaurya Gupta, Manas Ranjan Prusty, Rajesh Kumar Tripathy
2022	J	`jnl`	ECG beat classification based on discriminative multilevel feature analysis and deep learning approach. Biomed. Signal Process. Control. Nabanita Sinha, Rajesh Kumar Tripathy, Arpita Das
2022	J	`jnl`	EEGANet: Removal of Ocular Artifacts From the EEG Signal Using Generative Adversarial Networks. IEEE J. Biomed. Health Informatics Phattarapong Sawangjai, Manatsanan Trakulruangroj, Chiraphat Boonnag, Maytus Piriyajitakonkij, Rajesh Kumar Tripathy, Thapanun Sudhawiyangkul, Theerawit Wilaiprasitporn
2022	J	`jnl`	Time-Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals. IEEE Trans. Instrum. Meas. Jay Karhade, Shaswati Dash, Samit Kumar Ghosh, Dinesh Kumar Dash, Rajesh Kumar Tripathy
2021	—	`conf`	Classification of PCG Signals using Fourier-based Synchrosqueezing Transform and Support Vector Machine. IEEE SENSORS Samit Kumar Ghosh, Rajesh Kumar Tripathy, R. N. Ponnalagu
2021	J	`jnl`	Wavelet Domain Optimized Savitzky-Golay Filter for the Removal of Motion Artifacts From EEG Recordings. IEEE Trans. Instrum. Meas. Pranjali Gajbhiye, Nopparada Mingchinda, Wei Chen, Subhas Chandra Mukhopadhyay, Theerawit Wilaiprasitporn, Rajesh Kumar Tripathy
2020	J	`jnl`	Development of Automated Sleep Stage Classification System Using Multivariate Projection-Based Fixed Boundary Empirical Wavelet Transform and Entropy Features Extracted from Multichannel EEG Signals. Entropy Rajesh Kumar Tripathy, Samit Kumar Ghosh, Pranjali Gajbhiye, U. Rajendra Acharya

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.