A novel cryptographic approach to evaluate the security of federated learning frameworks V. Dankan Gowda, Sadashiva V. Chakrasali, Pullela SVVSR Kumar, Sampada Abhijit Dhole, Jayamala Kumar Patil, Shivoham Singh Journal of Discrete Mathematical Sciences and Cryptography, 2025 Federated Learning (FL) has turned out to be one of the most useful techniques for enabling distributed machine learning while protecting the data. Thirdly, because FL is distributed, the introduced feature is susceptible to several attacks associated with security. The current paper develops a new cryptographic solution that can enhance the security of FL frameworks. Thus, applying the cryptographic technologies, the employ of the proposed method ensures reliable data protection against the losses and illicit interventions. Experimental evaluations for this strategy are numerous and illustrate how, at the same time, our approach defends FL operations on the practical level while providing performance. It should also be noted how the results can provide a clear depiction to prove how beneficial our cryptographic solution will be in the context of improving the security of federated learning and paving the way for safer and more reliable FL implementations.
Green Routes Building the Backbone for Electric Vehicle Charging V. Dankan Gowda, Sadashiva V. Chakrasali, Ved Srinivas, K.D.V. Prasad, Saptarshi Mukherjee Digital Convergence in Intelligent Mobility Systems, 2025 This chapter delves into an intricate aspect of infrastructure development viz., the influence of EVs that keeps accelerating. The ensuing impending switch towards electric vehicles will greatly make charging services very demanding. This section tackles the present developing infrastructure of EV charging, spotting new innovative technologies to meet the rising needs of EV users while sustainably protecting the environment. The talk then carries on to the design of sustainable charging networks and their merger with renewable energy sources, which supports the notion that eco-friendly initiatives are the key to it. On top of this, it is assessing the economic impacts and business models of setting up and managing EV charging stations as well as their susceptibility to policies and public opinion which could further grow the charging infrastructure. By means of a combination of technical analysis, real case studies, and visionary views, this chapter proposes the strategies and teamwork that are required for building a green backbone for electric vehicle charging—a key component of the agendas for sustainable urban transportation.
Mathematical analysis of wavelet-based multi-image compression in medical diagnostics V. Dankan Gowda, Y. N. Sunitha, Sadashiva V. Chakrasali, K. D. V. Prasad, Parismita Sarma, Mirzanur Rahman Journal of Discrete Mathematical Sciences and Cryptography, 2024 Accurate diagnosis and well-informed treatment choices are only possible with the help of sophisticated medical imaging technology. The introduction of digital imaging methods has led to a dramatic rise in the volume of data associated with medical images, which in turn has led to difficulties in their storage, transmission, and administration. It is crucial to utilize effective image compression techniques to address these challenges without compromising the diagnostic integrity of the pictures. The wavelet transform has matured into a potent method for striking a good compromise between picture quality and file size reduction while compressing. The safe and efficient transfer of medical image data is a major concern in today’s healthcare settings. In this academic investigation, we investigate how wavelet transform-based techniques may be used to enhance medical picture compression. The proposed techniques optimize compression ratios while maintaining diagnostic picture quality by making use of the wavelet transform’s multi-resolution and frequency localization features. To address the unique challenges given by medical image collections, various iterations of the wavelet transform and compression techniques are investigated. Through a series of detailed tests involving several medical picture modalities, the effectiveness of these technologies is thoroughly evaluated, demonstrating their effectiveness in achieving significant data reduction without sacrificing clinical information.
Design and Implementation of Delta-Sigma Modulator using Simulink Rajendra Prasad P, Sadashiva V Chakrasali, M. Nagabushanam, V Nuthan Prasad, Shashank H N 2024 Global Conference on Communications and Information Technologies Gccit 2024, 2024 Delta sigma modulation (DSM) is a prominent technique in both analog-to-digital conversion (ADC) and digital-to-analog conversion (DAC), known for its ability to achieve high resolution and dynamic range while maintaining low power consumption. This paper introduces a novel implementation of delta sigma ADC and DAC converters aimed at enhancing high-resolution analog signal processing applications. Leveraging advanced delta sigma modulation techniques, the design and implementation of the converters are outlined, emphasizing oversampling, noise shaping, and decimation strategies to achieve superior performance. The ADC stage employs delta sigma modulation to encode analog signals into high-resolution digital format while mitigating quantization noise through noise shaping techniques. Following the ADC stage, the paper explores the reconstruction process in the DAC stage, focusing on inverse delta sigma modulation techniques to faithfully reconstruct the analog signal from digital data. Design considerations such as digital filter design, quantization error compensation, and signal-to-noise ratio optimization are addressed to ensure accurate signal reconstruction. Results highlight significant improvements in signal fidelity and noise reduction compared to conventional ADC-DAC systems. This work represents significant advancements in high-resolution analog signal processing systems, promising enhanced performance and reliability in various real-world applications including telecommunications, audio processing, and sensor interfaces.
Computer vision based healthcare system for identification of diabetes & its types using AI Avinash Sharma, K.D.V. Prasad, Sadashiva V. Chakrasali, Dankan Gowda V, Chanakya Kumar, Abhay Chaturvedi, A. Azhagu Jaisudhan Pazhani Measurement Sensors, 2023 Diabetes mellitus, often known as diabetes, is an endocrine disorder that has a wide global impact today. Here is a requirement for an effective model that able prognoses diabetes and its types with more accurateness as early. Given the breadth and depth of existing studies, there is a pressing need for accurate and timely illness forecasting in the healthcare sector. Current circumstances need the creation and design of systems that are quicker to respond, more accurate, more durable, and more generalizable. For increasing the accurateness of prediction with best effectiveness innovative Artificial Intelligence and Machine Learning Model is proposed. This model predicts the diabetes class using the symptoms located into the data-set which is having the row as one rule of the system & this rule are need to understand and compile using feature.
Performance analysis of different intonation models in Kannada speech synthesis Sadashiva Veerappa Chakrasali, Krishnappa Indira, Sunitha Yariyur Narasimhaiah, Shadaksharaiah Chandraiah Indonesian Journal of Electrical Engineering and Computer Science, 2022 Text <span lang="EN-US">to speech (TTS) is a system that generates artificial speech from text input. The prosodic models used improve the quality of the synthesized speech especially naturalness and intelligibility. The prosody involves intonation, intonation refers to the variations in the pitch frequency (F0) with respect to time in an utterance. This work mainly concentrates on building feedback neural network model to predict F0 contour in the utterances using Fujisaki intonation model parameters as the input features to the network since the Fujisaki intonation model is data driven and not a rule based one. In this work we have built 4-layer feedback neural network in the festival framework. Finally, the synthetically generated Kannada speech using the neural network model, is compared for its performance with the classification and regression tree (CART) model and Tilt model. Database of simple declarative Kannada sentences created by Carnegie Mellon University have been deployed in this work. From the study it is very clear that F0 contours can be accurately predicted using CART and neural network models, whereas naturalness and intelligibility is high in CART model rather than neural network model.</span>
Optimal path discovery for two moving sinks with a common junction in a wireless sensor network Satish Tunga, Sadashiva V. Chakrasali, N. Shylashree, Latha B. N., Mamatha A. S. Indonesian Journal of Electrical Engineering and Computer Science, 2021 A new algorithm is described for determining the optimal round-trip paths for two moving sinks in a wireless sensor network. The algorithm uses binary integer programming to select two non-overlapping shortest paths except having a common junction node to cover all the sensor nodes. The two paths are balanced as nearly equal as possible. That is the sensor nodes along each path are equal or differ by just one depending on whether the total number of sensor nodes excluding the junction node is even or odd. In this method, both the path lengths are made equal or very nearly equal while the total length is minimized. This integrated approach is a novel and unique solution to solve the dual moving sink path problem in a wireless sensor network.
HMM based Kannada speech synthesis using festvox Sadashiva V Chakrasali*, , K Indira, Shashank B Sharma, Srinivas N M, Varun S S, , , , and International Journal of Recent Technology and Engineering, 2019 The process which involves generation of human like voice by a machine is called speech synthe- sis. The developments in the fteld of speech synthesis is vast in international languages, but it is limited in Indian languages like Kannada. This work aims at de- velopment of such a system for Kannada language using Festival and Festvox. It is based on parametric analysis and models of speech features, particular to a language and speaker. The system is memoryless and dynamic, wherein only extracted features are stored but not recorded audio. The training process involves speech data acquisition, pre-processing, labelling using Baum- Welch Iteration, whereas testing process involves text analysis, text segmentation, speech synthesis and qual- ity enhancement using acoustic HMM model develop- ment. The quality of synthesis is 3.52 dB to 5.02 dB as measured by Mel-Cepstral Distortion (MCD) score.
Formants and LPC analysis of kannada vowel speech signals Sadashiva Chakrasali, Umesh Bilembagi, K. Indira 2018 3rd IEEE International Conference on Recent Trends in Electronics Information and Communication Technology Rteict 2018 Proceedings, 2018
Optimized face detection on FPGA Sadashiva V Chakrasali, Sanmati Kuthale 2016 International Conference on Circuits Controls Communications and Computing I4c 2016, 2017
