K JAYARAM KUMAR

@acet.ac.in

Senior Assistant Professor, Department of ECE
Aditya College of Engineering & Technology

K JAYARAM KUMAR


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https://researchid.co/jayaram

EDUCATION

(VLSI Based Signal Processing)
M.Tech (VLSI & Embedded Systems)
B.Tech (Electronics and Communication Engineering

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering
15

Scopus Publications

62

Scholar Citations

6

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Hybrid Deep Learning for Kidney Stone Detection in CT Scans With Noise Reduction and Feature Enhancement
    U. M. Fernandes Dimlo, Sreenu Banoth, Priti Bihade, Yahia Mjery, K. Jayaram Kumar, Umanesan R, A. Saran Kumar, V. Bhoopathy
    Journal of Computer Science, 2026
    The identification of kidney stones using CT scans is an essential but difficult effort in medical diagnostics, frequently obstructed by imaging noise and the complexity of manual interpretation. Although conventional methods are widely used, they suffer from inaccuracies and inefficiencies, necessitating the development of automated diagnostic solutions. This study tackles these issues by presenting HDCNRNet, a hybrid deep learning network explicitly developed for automated kidney stone identification. The suggested approach incorporates Convolutional Neural Networks (CNNs) with sophisticated noise reduction methodologies and improved feature extraction modules to boost the diagnostic precision and dependability of kidney stone identification. HDCNRNet surpasses current models by attaining exceptional performance measures, including an accuracy of 97.8±0.3, sensitivity of 97.8, specificity of 99.2 precision of 98.0, and an F1-score of 97.6% ± 0.4. These findings demonstrate a significant improvement over baseline models such as ResNet-50 and VGG16, highlighting the model's superior ability to identify kidney stones while minimizing false positives and negatives. The use of excellent noise reduction techniques and feature enhancement components guarantees the model's efficacy despite fluctuating and noisy CT scan data. This research advances medical imaging by providing a scalable, efficient, and highly accurate AI-based method for kidney stone identification, readily integrable into clinical workflows. The results indicate that HDCNRNet can substantially boost diagnostic outcomes, lower the workload of radiologists, and improve patient care by providing more reliable and quicker diagnoses.
  • Blockchain-Enabled Collaborative Threat Intelligence in IoT Security Using a Hybrid Neural Network Model
    Prasanna Simhadati, C Hrishikesava Reddy, R Gomathi, Supriya Telsang, K Jayaram Kumar, A Barkathulla, V Bhoopathy
    International Research Journal of Multidisciplinary Scope, 2025
    The fast spread of Internet of Things (IoT) devices over many different fields has made network security even more crucial. Conventional security systems can fail to handle the dynamic and complex character of contemporary cyber threats aiming at IoT systems. This paper suggests a novel security framework combining blockchain technology, machine learning (ML), and a centralized iOS application to get past these constraints. The suggested approach guarantees privacy, integrity, and immutability of shared Cyber Threat Intelligence (CTI) data by using smart contracts and the Ethereum blockchain. Fundamentally, a hybrid deep learning model CNNTransLSTM is used to highly precisely detect and categorize threats in real-time. Combining Transformer encoders, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNN), this model efficiently records spatial and temporal aspects of IoT network data. By allowing users to report hazards and get alerts, the iOS app serves as an interactive hub improving human-machine cooperation. CNNTransLSTM model beats conventional approaches in terms of accuracy, sensitivity, and loss rate according to experimental evaluations. Moreover, the distributed blockchain architecture enables among stakeholders safe, open, and cooperative threat intelligence sharing. This all-encompassing strategy enables users and cloud providers to make quick, well-informed decisions to reduce risks, hence greatly improving the resilience of IoT ecosystems.
  • Optimized Task Offloading in D2D-Assisted Cloud-Edge Networks Using Hybrid Deep Reinforcement Learning
    Navya Kailasam, Srilatha Yalamati, V. S. N. Murthy, Venkateswara Rao P, R. Anil Kumar, K. Jayaram Kumar
    International Journal of Basic and Applied Sciences, 2025
    The modern communication network depends highly on Device-to-Device (D2D) technology as an essential foundation. Direct communication allows devices to exchange information among themselves. Cloud-edge-device networks enable tasks to execute through several operational procedures. A device working at capacity executes local tasks or transfers them directly to an inactive device by means of D2D technology. The device has two options for delivering workloads, namely an edge-server transfer or a direct cloud-server transfer. Existing methods ‎do not fully exploit D2D-assisted offloading. Such systems fail to maximize the benefits that stem from combining cloud-edge-device op-‎operations. This makes resource distribution a complex challenge that needs an optimized solution. Traditional solutions find it difficult to ‎produce efficient system solutions. The presented work describes an approach for task offloading mechanisms. The technique determines ‎overall system expenses through optimized management of time, together with energy usage. The method operates to optimize all four critical ‎system factors: task selection and transmission power, with rate and computational resource distribution. The proposal utilizes a combina-‎tion of deep reinforcement learning methods through SD3. The proposed method merges Softmax Deep Double Deterministic Policy Gradients (SD3) with numerical techniques to achieve its operations. The proposed method operates on multiple smaller components of the ‎primary issue. The SD3-based DRL method controls offloading decisions throughout the system, and the numerical techniques manage ‎power and resource allocation. Extensive simulations were conducted. Seven different scenarios were tested. Research compared the pro-‎posed method against four traditional solution approaches. Research findings demonstrate the superiority of the proposed solution. The ‎technique both lessens system expenses and optimizes resource usage while generating better operational efficiency. A novel hybrid DRL-based approach for task offloading constitutes the main contribution of this work. The system improves cloud-edge-device partnerships by ‎enabling D2D communication. Machine learning unions with numerical methods create an effective strategy to solve complex optimization ‎tasks‎.
  • Optimizing FIR Filter Efficiency with Advanced Hybrid Multiplier Techniques
    E. Jagadeeswara Rao, M. Grace Mercy, K. Jayaram Kumar, M. Rajanbabu, K. Sudha Ramya
    Journal of Circuits Systems and Computers, 2025
    In modern signal processing applications, Finite Impulse Response (FIR) filters are extensively used in various domains such as communications, consumer electronics and audio systems. Multiplication plays a pivotal role in implementing numerous algorithms, making the selection of a fast and efficient multiplier critical for FIR filters. With the growing demand for power-efficient algorithms, the need for advanced multipliers capable of handling large numerical inputs without compromising the performance has become evident. Therefore, this paper introduces a novel approach to tackle the challenges associated with large-scale multiplication in FIR filters by drawing inspiration from ancient Indian mathematical techniques known as Vedas. Specifically, a Vedic Multiplier (VM) is presented that harnesses the power of distinct Vedic sutras, enabling efficient multiplication operations. To further enhance performance, the Karatsuba Algorithm (KA) and two additional sutras are integrated, such as Urdhva Tiryagbhyam (UT) and Nikhilam Sutra (NS), to form a hybrid multiplier. Furthermore, all proposed multipliers are implemented, along with existing designs, in Verilog code using Xilinx Vivado and Cadence Genus. In addition, the experimental results show that the proposed designs outperform the existing designs in terms of area, power consumption and delay. By significantly reducing the multiplier delay and power consumption, the proposed designs offer a promising solution for addressing the challenges of large-scale multiplication in FIR filter designs.
  • SPECIFICATIONS OF STRUCTURAL DEVELOPMENT AND CHARACTERISTICS OF Al–Si–Cu EXCEPTIONAL HIGH-ALLOY FORMULATIONS
    Oxidation Communications, 2025
  • Advanced Facial Emotion Recognition Using DCNN-ELM: A Comprehensive Approach to Preprocessing, Feature Extraction and Performance Evaluation
    K. Boopalan, Satyajee Srivastava, K R Kavitha, D. Usha Rani, K. Jayaram Kumar, et al.
    Journal of Computer Science, 2025
    As a subfield of affective computing, Facial Emotion Recognition (FER) teaches computers to read people's facial expressions to determine their emotional state. Because facial expressions convey 55% of an individual's emotional and mental state in the whole range of face-to-face communication, Facial Emotion Recognition is crucial for connecting humans and computers. Improvements in the way computer systems (robotic systems) interact with or assist humans are another benefit of advancements in this area. Deep learning is key to the highly advanced research being conducted in this area. Recently, FER research has made use of Ekman's list of fundamental emotions as one of these models. Anger, Disgust, Fear, Happy, Sad, Surprise, and Neutral are the seven main emotions mapped out on Robert Plutchik's wheel. Opposite to each of the main emotions is its polar opposite. There are four steps to the suggested method: Preprocessing, feature extraction, model performance evaluation, and finalization. The preprocessing step makes use of the kernel filter. The proposed approach uses SWLDA for feature extraction. Facial Emotion Recognition (FER) is critical for improving human-computer interactions, particularly in educational settings. This study presents a novel hybrid approach combining Deep Convolutional Neural Networks (DCNN) with Extreme Learning Machines (ELM) to enhance emotion recognition accuracy. The proposed model demonstrates superior performance compared to traditional DCNN and standalone ELM approaches, offering real-time emotion detection in online learning environments. The effectiveness of the model is validated using publicly available datasets, setting a new benchmark for FER. This study makes major contributions to the field of Facial Emotion Recognition (FER) by offering a robust architecture that combines Deep Convolutional Neural Networks (DCNN) with Extreme Learning Machines (ELM). The methodology's efficacy is proven with publicly available datasets, establishing a new standard in FER, particularly in educational settings.
  • A Novel Integrated System for Forest Fire Detection using Multiple Adaptive Reduced KELM Models
    Deepak Kholiya, Mummidi Rachel, Arivarasan S, P. Sukumar, K Jayaram Kumar, T. Aswini Devi
    2nd IEEE International Conference on Data Science and Network Security Icdsns 2024, 2024
    Forest fires are a leading source of ecological destruction. Damage from fires, particularly in their early stages when the system are difficult to see, can be mitigated by a faster and more accurate detection system. Forest fires are a leading source of ecological destruction. Damage from fires, particularly in their early stages when the system are difficult to see, can be mitigated by a faster and more accurate detection system. According to the proposed method, there are three phases, which include model preparation, feature extraction, and training. Unsharp filtering and a CIEXYZ color space conversion are performed on the input image during the preprocessing phase. The boundary chain code, sphericity, and contour line of the fire utilized for feature extraction could constitute the threshold. It utilized a MARK-ELM to train the model. Compared to MARK and ELM, the proposed technique typically obtains a better accuracy of 93.35 percent.
  • Low Power FPGA Implementation of ECG monitoring in WBAN
    Swathi Dasi, Koppada Vanaja, Bandi Vaisalini, Nagavarapu Sowmya, K Jayaram Kumar, M V Pathi Amudalapalli
    2024 International Conference on Integration of Emerging Technologies for the Digital World Icietdw 2024, 2024
    In smart healthcare, independent ECG devices with an Internet of Things foundation are becoming commonplace for identifying and averting cardiovascular disorders. Nevertheless, since these systems frequently run on batteries, transmission connections with high power consumption might drastically shorten their total operating life. This study suggests a power management approach and related VLSI design to improve the longevity of battery-operated Internet of Things ECG monitoring devices in order to solve this problem. Depending on the battery’s energy level, the suggested approach dynamically switches between high-power and low-power transmission modes. For on-node localized processing, a lightweight technique with a runtime adaptive thresholding focus on slope improvement is presented. This algorithm allows for real-time QRS complex recognition and heart rate evaluation while optimizing power consumption in low battery conditions. By effectively gathering enough ECG data from a single patient and prolonging system lifespan, the suggested system satisfies the objective of Wireless Body Area Network (WBAN) systems by intelligently choosing the suitable transmission mode depending on battery level and heart rate stability. Using the MIT-BIH arrhythmia database, the algorithm’s sensitivity and predictivity were evaluated; 99.35% sensitivity and 99.38% predictivity were obtained. With a maximum clock frequency of 269 MHz and a power consumption of only 0.7 mW, the Spartan-6 FPGA was used to build the design, which made it appropriate for real-time ECG monitoring. IoT-based ECG monitoring systems may now operate more efficiently and last longer thanks to the suggested power management approach and VLSI design, which increases their suitability for real-time healthcare applications.
  • Design of area-efficient high speed 4 × 4 Wallace tree multiplier using quantum-dot cellular automata
    A. Arunkumar Gudivada, K. Jayaram Kumar, Srinivasa Rao Jajula, Durga Prasad Siddani, Praveen Kumar Poola, Varun Vourganti, Asisa Kumar Panigrahy
    Materials Today Proceedings, 2021
  • A systematic journal of multipliers accuracy and performance
    E. Rao, Durgesh Nandan, R. Rajath Krishna, K. J. Kumar
    International Journal of Engineering and Advanced Technology, 2019
    Low power and efficient architecture of computer arithmetic is demanded of real time Digital signal processing. Out of all arithmetic units, the multiplier is most important and frequently used arithmetic component in literature. As we know that there are many multipliers exist in the literature and everyone has his own proc-corns. But there is a gap in literature, no one gets compared all popular multiplier technique at same platform and discuss their advantages and limitations at one place. This research work outlines the most popular five multiplier techniques (like Wallace, modified, Vedic, Russian Peasant and Logarithm) and compares them, highlights merits, demerit for further improvements. This comprehensive study includes the systematic development, compares the latest design of every multiplier and justified that which one is better over other reported multiplier is also highlighted.
  • Quantitative analysis of drinking water quality for long term water borne diseases
    Kamidi Prasanth, Sabbi Vamshi Krishna, Sanniti Rama Krishna, Kondapalli Jayaram Kumar
    Communications in Computer and Information Science, 2019
  • Implementation of low power LFSR’s design through the use of GDI method
    P Soundarya Mala, Ch Srigiri, R Jayaram Kumar, Srivani Vaddi
    Indian Journal of Public Health Research and Development, 2018
  • Advanced multiplier design and implementation using Hancarlson adder
    E. Jagadeeswara Rao, T. Ramanjaneyulu, K. Jayaram Kumar
    2018 International Conference on Intelligent and Innovative Computing Applications Iconic 2018, 2018
  • Design of high speed Wallace tree multiplier using 8-2 and 4-2 adder compressors
    E Jagadeeswara Rao, K Jayaram Kumar, Dr. T. V. Prasad
    International Journal of Engineering and Technology Uae, 2018
  • Square root operation of 64 bit floating point numerical data using verilog coding
    S. Krishna, R. Kumar, M Sai, Preethi Sudha Gollamudi, M Kamaraju, et al.
    International Journal of Advanced Trends in Computer Science and Engineering, 2018

RECENT SCHOLAR PUBLICATIONS

  • Optimizing FIR Filter Efficiency with Advanced Hybrid Multiplier Techniques
    EJ Rao, MG Mercy, KJ Kumar, M Rajanbabu, KS Ramya
    Journal of Circuits, Systems and Computers 34 (06), 2550144 , 2025
    2025.0
  • ADVANCED FACIAL EMOTION RECOGNITION USING DCNN-ELM: A COMPREHENSIVE APPROACH TO PREPROCESSING, FEATURE EXTRACTION AND PERFORMANCE EVALUATION
    S SRIVASTAVA, K KAVITHA, DU RANI, K KUMAR, MV JAGANNATHA, ...
    JOURNAL OF COMPUTER SCIENCE 21 (1), 13-24 , 2025
    2025.0
  • Blockchain-Enabled Collaborative Threat Intelligence in IoT Security Using a Hybrid Neural Network Model
    P Simhadati, CH Reddy, R Gomathi, S Telsang, KJ Kumar, A Barkathulla, ...
    Int. Res. J. Multidiscip. Scope 6, 889-901 , 2025
    2025.0
    Citations: 1
  • SPECIFICATIONS OF STRUCTURAL DEVELOPMENT AND CHARACTERISTICS OF Al–Si–Cu EXCEPTIONAL HIGHALLOY FORMULATIONS.
    R BHOOPATHI, SJ SULTANUDDIN, C RAO, KJ KUMAR
    Oxidation Communications 48 (1) , 2025
    2025.0
  • 5G Resource Allocation Enhancement Via Resnet-InceptionV2 With Non-Linear Analysis.
    TS Karthik, M Elangovan, AR Prasath, KJ Kumar, AK Shrivastav
    Journal of Computational Analysis & Applications 33 (2) , 2024
    2024.0
  • Low Power FPGA Implementation of ECG monitoring in WBAN
    S Dasi, K Vanaja, B Vaisalini, N Sowmya, KJ Kumar, MVP Amudalapalli
    2024 International Conference on Integration of Emerging Technologies for … , 2024
    2024.0
  • A Novel Integrated System for Forest Fire Detection using Multiple Adaptive Reduced KELM Models
    D Kholiya, M Rachel, P Sukumar, KJ Kumar, TA Devi
    2024 International Conference on Data Science and Network Security (ICDSNS), 1-6 , 2024
    2024.0
    Citations: 1
  • Optimizing Pest Detection And Management In Precision Agriculture Through Deep Learning Approaches.
    RVS Praveen, A Shrivastava, R Prasanthi, KH Bindu, KJ Kumar, K Yadav
    Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024
    2024.0
  • WITHDRAWN: Sentiment analysis of product feedback using natural language processing
    P Chitra, TS Karthik, S Nithya, JJ Poornima, JS Rao, M Upadhyaya, ...
    Materials Today: Proceedings , 2021
    2021.0
    Citations: 17
  • Design of area-efficient high speed 4× 4 Wallace tree multiplier using quantum-dot cellular automata
    AA Gudivada, KJ Kumar, SR Jajula, DP Siddani, PK Poola, V Vourganti, ...
    Materials Today: Proceedings 45, 1514-1523 , 2021
    2021.0
    Citations: 10
  • Quantitative analysis of drinking water quality for long term water borne diseases
    K Prasanth, SV Krishna, SR Krishna, KJ Kumar
    International Conference on Advances in Computing and Data Sciences, 500-508 , 2019
    2019.0
    Citations: 2
  • Automatic pet feeder using internet of things
    JK Kondapalli, VR Sanepu, BS Kothapalli, SPR Peketi, VDN Kukatla
    JETIR 6 (4), 360-367 , 2019
    2019.0
    Citations: 6
  • A systematic review of multipliers: accuracy and performance analysis
    EJ Rao, VKRD Nandan, KJ Kumar
    Int J Eng Adv Technol (IJEAT) 8 (6S), 965-969 , 2019
    2019.0
    Citations: 7
  • Advanced multiplier design and implementation using Hancarlson adder
    EJ Rao, T Ramanjaneyulu, KJ Kumar
    2018 International Conference on Intelligent and Innovative Computing … , 2018
    2018.0
    Citations: 9
  • Implementation of Low Power LFSR’s Design through the use of GDI Method
    PS Mala, C Srigiri, RJ Kumar, S Vaddi
    Indian Journal of Public Health 9 (12), 1487 , 2018
    2018.0
    Citations: 1
  • Design of high speed Wallace tree multiplier using 8-2 and 4-2 adder compressors
    EJ Rao, KJ Kumar, TV Prasad
    International Journal of Engineering & Technology 7 (4), 2386-2390 , 2018
    2018.0
    Citations: 7
  • A Novel 2X2 Vedic multiplier architecture based on reversible logic
    K Kumar, MR Nagabhushana, SG Kedlaya
    International Journal of Electrical Electronics and Computer Science … , 2016
    2016.0
    Citations: 1
  • High Speed and Reliable Gray Mapped Polar Codes
    G HANEESHA, KJ KUMAR
  • HIGH-SPEED SUPERIOR BI-ROTATIONAL CORDIC USING QUADRANT AMENDMENT WITH PRE-SCALED STRUCTURAL DESIGN
    KJ KUMAR, PS MALA

MOST CITED SCHOLAR PUBLICATIONS

  • WITHDRAWN: Sentiment analysis of product feedback using natural language processing
    P Chitra, TS Karthik, S Nithya, JJ Poornima, JS Rao, M Upadhyaya, ...
    Materials Today: Proceedings , 2021
    2021.0
    Citations: 17
  • Design of area-efficient high speed 4× 4 Wallace tree multiplier using quantum-dot cellular automata
    AA Gudivada, KJ Kumar, SR Jajula, DP Siddani, PK Poola, V Vourganti, ...
    Materials Today: Proceedings 45, 1514-1523 , 2021
    2021.0
    Citations: 10
  • Advanced multiplier design and implementation using Hancarlson adder
    EJ Rao, T Ramanjaneyulu, KJ Kumar
    2018 International Conference on Intelligent and Innovative Computing … , 2018
    2018.0
    Citations: 9
  • A systematic review of multipliers: accuracy and performance analysis
    EJ Rao, VKRD Nandan, KJ Kumar
    Int J Eng Adv Technol (IJEAT) 8 (6S), 965-969 , 2019
    2019.0
    Citations: 7
  • Design of high speed Wallace tree multiplier using 8-2 and 4-2 adder compressors
    EJ Rao, KJ Kumar, TV Prasad
    International Journal of Engineering & Technology 7 (4), 2386-2390 , 2018
    2018.0
    Citations: 7
  • Automatic pet feeder using internet of things
    JK Kondapalli, VR Sanepu, BS Kothapalli, SPR Peketi, VDN Kukatla
    JETIR 6 (4), 360-367 , 2019
    2019.0
    Citations: 6
  • Quantitative analysis of drinking water quality for long term water borne diseases
    K Prasanth, SV Krishna, SR Krishna, KJ Kumar
    International Conference on Advances in Computing and Data Sciences, 500-508 , 2019
    2019.0
    Citations: 2
  • Blockchain-Enabled Collaborative Threat Intelligence in IoT Security Using a Hybrid Neural Network Model
    P Simhadati, CH Reddy, R Gomathi, S Telsang, KJ Kumar, A Barkathulla, ...
    Int. Res. J. Multidiscip. Scope 6, 889-901 , 2025
    2025.0
    Citations: 1
  • A Novel Integrated System for Forest Fire Detection using Multiple Adaptive Reduced KELM Models
    D Kholiya, M Rachel, P Sukumar, KJ Kumar, TA Devi
    2024 International Conference on Data Science and Network Security (ICDSNS), 1-6 , 2024
    2024.0
    Citations: 1
  • Implementation of Low Power LFSR’s Design through the use of GDI Method
    PS Mala, C Srigiri, RJ Kumar, S Vaddi
    Indian Journal of Public Health 9 (12), 1487 , 2018
    2018.0
    Citations: 1
  • A Novel 2X2 Vedic multiplier architecture based on reversible logic
    K Kumar, MR Nagabhushana, SG Kedlaya
    International Journal of Electrical Electronics and Computer Science … , 2016
    2016.0
    Citations: 1
  • Optimizing FIR Filter Efficiency with Advanced Hybrid Multiplier Techniques
    EJ Rao, MG Mercy, KJ Kumar, M Rajanbabu, KS Ramya
    Journal of Circuits, Systems and Computers 34 (06), 2550144 , 2025
    2025.0
  • ADVANCED FACIAL EMOTION RECOGNITION USING DCNN-ELM: A COMPREHENSIVE APPROACH TO PREPROCESSING, FEATURE EXTRACTION AND PERFORMANCE EVALUATION
    S SRIVASTAVA, K KAVITHA, DU RANI, K KUMAR, MV JAGANNATHA, ...
    JOURNAL OF COMPUTER SCIENCE 21 (1), 13-24 , 2025
    2025.0
  • SPECIFICATIONS OF STRUCTURAL DEVELOPMENT AND CHARACTERISTICS OF Al–Si–Cu EXCEPTIONAL HIGHALLOY FORMULATIONS.
    R BHOOPATHI, SJ SULTANUDDIN, C RAO, KJ KUMAR
    Oxidation Communications 48 (1) , 2025
    2025.0
  • 5G Resource Allocation Enhancement Via Resnet-InceptionV2 With Non-Linear Analysis.
    TS Karthik, M Elangovan, AR Prasath, KJ Kumar, AK Shrivastav
    Journal of Computational Analysis & Applications 33 (2) , 2024
    2024.0
  • Low Power FPGA Implementation of ECG monitoring in WBAN
    S Dasi, K Vanaja, B Vaisalini, N Sowmya, KJ Kumar, MVP Amudalapalli
    2024 International Conference on Integration of Emerging Technologies for … , 2024
    2024.0
  • Optimizing Pest Detection And Management In Precision Agriculture Through Deep Learning Approaches.
    RVS Praveen, A Shrivastava, R Prasanthi, KH Bindu, KJ Kumar, K Yadav
    Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024
    2024.0
  • High Speed and Reliable Gray Mapped Polar Codes
    G HANEESHA, KJ KUMAR
  • HIGH-SPEED SUPERIOR BI-ROTATIONAL CORDIC USING QUADRANT AMENDMENT WITH PRE-SCALED STRUCTURAL DESIGN
    KJ KUMAR, PS MALA