DR. LAKSHMINARAYANA. M
@sjbit.edu.in
Assistant Professor, Electronics and Communication Engineering
SJB Institute of Technology, Bangalore
EDUCATION
B.E, M.Tech, Ph.D.
RESEARCH INTERESTS
Compressive Sensing, Medical Image Processing, Signal Processing, Audio and Video processing.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
M. Lakshminarayana, P. Venkata Prasad, and E. Vidya Sagar
IEEE
Hybrid electric cars are a viable alternative to traditional gasoline-powered vehicles, and so efficient and intelligent energy management is critical to the hybrid electric vehicle market’s worldwide growth. Recent advancements in the realm of intelligent approaches, as well as the demand for intelligent energy systems, have paved the way for the creation of energy-efficient hybrid electric cars. The topic of energy management becomes critical in order to improve hybrid electric vehicle autonomy while also lowering expenses. As a result, a unique strategy based on intelligent approaches will be offered to operate plug-in hybrid electric cars in front of various client profiles. This research paper presents the Conceptual review on Intelligent controller and the Intelligent controller using battery state of charge as a decisive criterion in improving the battery performance of a plug-in hybrid electric car. The advanced controller uses the battery state of charge and engine speed as inputs to determine the precise torque that must be converted to energy that may be utilised to charge the battery. This is accomplished by adjusting the forward gain value. The value of forward gain is determined using an intelligent controller and an advanced intelligent controller.
T. V. V. Pavan Kumar, Moazzam Haidari, R. Vimala, Raghavendra Subramanya, Lakshminarayana. M, and Hemavathi
IEEE
Lithium-based rechargeable battery modules are widely used in Electric vehicles (EV), which are likely to become the primary method of transportation in the coming years. EVs are major actors due to their zero emissions of dangerous gases and increased energy efficiency. Lithium batteries should be examined and adjusted regularly to maintain the EV system's safety, productivity, and reliability. Due to a large number of battery cells in EVs, a battery management system (BMS) is required. An EV's battery should be ready to provide both long-lasting power and high energy. The BMS makes decisions based on measurable parameters. Battery resistivity, volume, the status of charge estimation (SoC), state of health (SoH), power declining, and remaining useful life can all be predicted by employing three non-invasive battery measurements like the voltage, current, and temperature. This article is about a low-cost, high-performance BMS. The paper aims to design a cost-effective BMS without compromising its performance. MATLAB is used to simulate and test the suggested design.
Arun Biradar, Nimmagadda Sathyanarayana Murthy, Parul Awasthi, Ajeet Kumar Srivastava, Patan Saleem Akram, M. Lakshminarayana, Shafiqul Abidin, Vikas Rao Vadi, and Asefa Sisay
Hindawi Limited
In general, the MIMO technology is used to transfer data from a protocol such as Wi-Fi in 5G networks. This is due to the increased bandwidth and capacity. The 802.11n protocol, which, using the technology described in it, allows you to reach speeds of up to 350 megabits/second. The quality of data transmission has improved even in areas where the reception signal is low. An external access point with a MIMO antenna is a well-known one. The WiMAX network can now transmit information at speeds of up to 40 megabits/second, using MIMO. It uses MIMO technology up to 8 × 8 . Thanks to this, a higher transfer rate is achieved—over 35 megabits/second. In addition, reliable and high quality connection of the best quality is guaranteed. In this paper, we continue to work and enhance technical configurations of MIMO in 5G networks. The proposed model will improve spectrum performance, improve network capability, and speed up data rates. In a saturation point, the proposed method achieves that the signal to noise interference ratio is just 42.4%, the receiver signal strength is 92.94%, the downstream traffic is 48.76%, the upstream traffic is 45.62%, the bandwidth utilization is 97.43%, the speed is 95.79%, the connectivity between the access point is 90.6%, and the network security is 96.42%.
Shoaib Kamal, P. S. Ramapraba, Avinash Kumar, Bikash Chandra Saha, M. Lakshminarayana, S. Sanal Kumar, Anitha Gopalan, and Kuma Gowwomsa Erko
Hindawi Limited
In this work, we proposed a mechanism for topology reconfiguration or optimization of photovoltaic (PV) arrays using machine learning-assisted techniques. The study takes into concern several topologies that includes series parallel topology, parallel topology, bridge link topology, honeycomb topology, and total cross tied. The artificial neural network-based topology reconfiguration strategy allows for optimal working conditions for PV arrays. With this, machine learning-assisted topology reconfiguration or optimal solar panel deployment enables the proposed mechanism to achieve higher degree of testing accuracy precision, recall, and f-measure under standard ideal condition.
Shoaib Kamal, K. R. Shobha, Flory Francis, Rashmita Khilar, Vikas Tripathi, M. Lakshminarayana, B. Kannadasan, and Kibebe Sahile
Hindawi Limited
The present work proposes to evaluate, compare, and determine software alternatives that present good detection performance and low computational cost for the plant segmentation operation in computer vision systems. In practical aspects, it aims to enable low-cost and accessible hardware to be used efficiently in real-time embedded systems for detecting seedlings in the agricultural environment. The analyses carried out in the study show that the process of separating and classifying plant seedlings is complex and depends on the capture scene, which becomes a real challenge when exposed to unstable conditions of the external environment without the use of light control or more specific hardware. These restrictions are driven by functionality and market perspective, aimed at low-cost and access to technology, resulting in limitations in processing, hardware, operating practices, and consequently possible solutions. Despite the difficulties and precautions, the experiments showed the most promising solutions for separation, even in situations such as noise and lack of visibility.
Priyanka Chattoraj, Parimala Prabhakar, Vishwanath Koti, N. Suganya Natarajan, M. Lakshminarayana, K. Arul, M. Venkatachalapathy, Maqusood Ahamed, M. Karnan, and S. Praveen Kumar
Hindawi Limited
This article evaluates the effect of wear parameters on composite materials. Aluminium alloy 7178 alloys with various nano titanium diboride weight percentages were prepared using stir casting. A pin-and-disc test rig was utilized to carry out the dry sliding wear test. Using Taguchi’s experiments for optimization, the L27 orthogonal array was designed (D.O.E.). The SNR and ANOVA techniques were used to identify the percentage of responses attributable to the input parameters. Both the wear rate and coefficient of friction increased with higher loading levels. This parameter, load intensity, had the most significant influence on wear rate and C.O.F. and sliding distance and velocity. Material removal was prevented at all times by nano titanium diboride particles embedded in the matrix alloy. AA7178 was treated with nano titanium diboride particles to improve its wear resistance.
Lakshminarayana M and Mrinal Sarvagya
Institute of Advanced Engineering and Science
The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-of-Interest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio.
Lakshminarayana M and Mrinal Sarvagya
Institute of Advanced Engineering and Science
Compressive sensing is one of teh cost effective solution towards performing compression of heavier form of signals. We reviewed the existing research contribution towards compressive sensing to find that existing system doesnt offer any form of optimization for which reason the signal are superiorly compressed but at the cost of enough resources. Therefore, we introduce a framework that optimizes the performance of the compressive sensing by introducing 4 sequential algorithms for performing Random Sampling, Lossless Compression for region-of-interest, Compressive Sensing using transform-based scheme, and optimization. The contribution of proposed paper is a good balance between computational efficiency and quality of reconstructed medical image when transmitted over network with low channel capacity. The study outcome shows that proposed system offers maximum signal quality and lower algorithm processing time in contrast to existing compression techniuqes on medical images.
M. Lakshminarayana and Mrinal Sarvagya
Springer International Publishing
The contribution of several compression algorithms plays a significant role in minimizing the size of multiple radiological images from last decade. However, a closer look into existing work will show that there is a big trade-off between compression performance and data quality during the reconstruction process. We review the existing research work being carried out and briefs such problems and trade-off. This paper presents a framework called as CARIC (Combinatorial Approach for Radiological Image Compression) that uses a combinatorial approach of both lossy and lossless compression schemes unique in any radiological image. Using maximum numbers and modalities of different radiological images, we also compare CARIC with some recent and relevant work of compression to find that CARIC offers better image compression ratio along with a great balance among quality of the reconstructed image and faster response time.
M Lakshminarayana and Mrinal Sarvagya
IEEE
There is an enhancement of network and communication and other technologies, which leads to visualize various applications for common users. Medical systems are one of the very important aspects of the better Healthcare and services. In this regards, Extreme Telesurgery (ETS) is conceptualized that performs compression and transmission of the medical image on real time basis. The success of such remote surgery depends upon the optimal use of compression technique. Traditional compression techniques use various transformations schemes such as Discrete Cosine Transformation (DCT), Fast Fourier Transformation (FFT) and Discrete Wavelet Transformation (DWT), where the core objective is to get more and more zero values. So that encoding becomes more light weighted but these methods are sensitive to noises residing in the channel. Which makes it unsuitable for critical Region of Interest (ROI) compression to get higher visual perception, low Bits Per Pixel (BPP) and Error resilient. This paper illustrates a novel method of image signal measurement and reconstruction using Compressive Sensing (CS), which can be used for such real-time image compression requirements in specific applications.
M. Lakshminarayana and Mrinal Sarvagya
Springer International Publishing
Usage of compressive sensing plays a highly contributory role in compression, storage, and transmission in medical images even in presence of inherent complexities associated with radiological images. After reviewing the existing system, we found that existing techniques are less focused on medical images ignoring the complexities associated with it. Hence, this paper presents a very simple and novel transform-based technique where the performance of compressive sensing is enhanced using novel parameters of linear approximation, index ordering, along with number of low pass coefficient, and auxiliary measurement. The algorithm formulated by the proposed system is purely capable of minimizing L1-minimization. The outcome of the proposed system shows well balance between the compression ratio and signal quality in contrast to the existing technique of compressive sensing in medical images.