Bio: R. Maheswari is an academic researcher from VIT University. The author has contributed to research in topics: Central processing unit & Multi-core processor. The author has an hindex of 2, co-authored 10 publications receiving 11 citations.
TL;DR: The machine learning hybrid techniques were performed over the collected data to validate the smart distribution and the result obtained ensures a substantial gain which leads to conservation of generated energy through smart energy management.
TL;DR: This work aims to design a nanobot (nano robot) which can effectively identify the cancer growth using the concept called Positron Emission Tomography that comes under the branch of Acoustic Bio-Signalling.
•22 Feb 2012
TL;DR: This paper presents a new technique of embedding multigrain parallel processing HPRC using FPGA in the CPU/DSP unit of OR1200 a soft-core RISC processor.
Abstract: In the embedded era, reconfigurable components comes in three forms of IP Intellectual Property cores i) Soft core ii) Firm core and iii) Hard Core. This paper presents a new technique of embedding multigrain parallel processing HPRC using FPGA in the CPU/DSP unit of OR1200 a soft-core RISC processor. The core performance is increased by placing a multigrain parallel processing HPRC internally in the Integer Execution Pipeline unit of the CPU/DSP core. Depending on the complexity/depth of the code, the dependency level of vertices DL were created and numbers of threads N were created to run the code parallel in HPRC. Multigrain parallel processing HPRC is achieved by two function i) HPRC_Parallel_Start to trigger the parallel thread ii) HPRC_Parallel_End to stop the thread. In the first phase of this paper a Verilog HDL functional code is developed and synthesised using XIINX ISE and in the second phase a CoreMark processor core benchmark is used to test the performance of the reconfigured IP soft core.
TL;DR: The objective of the research paper is to capture the design a Vehicle Number Plate Identification System which can be used to identify and read the license number of any vehicle.
Abstract: The objective of the research paper is to capture the design a Vehicle Number Plate Identification System which can be used to identify and read the license number of any vehicle. The basic process involves taking the image of the front/rear of the vehicle which then gets processed and ultimately the number gets displayed on the LCD. This system can be used for a wide variety of installations and establishments such as entry points of schools, colleges, offices and parking spaces. The camera takes an image which is then processed in PC. The result achieved from this is the license number of the vehicle which is then transmitted via the Wi-Fi module and ultimately displayed on the LCD. The hardware especially can be made more rugged and compact so as to handle all the elements of nature and various environments that it can be used in.
••29 Aug 2019
TL;DR: An external hardware-independent system which can control electrical appliances using hand gesture and voice control and uses a distance-measuring technique to provide certain functionalities to external hardware.
Abstract: Hand gesture recognition is one of the significant research domains in the computer science field to which people have paid attention in the past decade. It has been considered as a highly successful technology, where it saves time to unlock any device and provide high stability with an increased accuracy rate. It provides a simple interface for humans to communicate with machines. This paper describes an external hardware-independent system which can control electrical appliances using hand gesture and voice control. We will be using a subpart of this field which is measuring the distance of relative hand movement with respect to the sensors used. The system uses a distance-measuring technique to provide certain functionalities to external hardware. The relative hand movement is recognized using ultrasonic sensors and voice pattern is recognized using analog to digital converter. The hardware part of the ultrasonic sensor is interfaced with Arduino. After getting the sound speed (sonic speed) and time between emission and reception, the distance can be calculated and provide necessary functionalities. In Voice Control Google Speech Recognition is used to convert speech to text and provide the desired functionalities. The workflow of the entire system is controlled using Arduino and Python code.
TL;DR: In this paper , a study on data-driven probabilistic machine learning (ML) techniques and their real-time applications to smart energy systems and networks was conducted to highlight the urgency of this area of research.
Abstract: The current trend indicates that energy demand and supply will eventually be controlled by autonomous software that optimizes decision-making and energy distribution operations. New state-of-the-art machine learning (ML) technologies are integral in optimizing decision-making in energy distribution networks and systems. This study was conducted on data-driven probabilistic ML techniques and their real-time applications to smart energy systems and networks to highlight the urgency of this area of research. This study focused on two key areas: i) the use of ML in core energy technologies and ii) the use cases of ML for energy distribution utilities. The core energy technologies include the use of ML in advanced energy materials, energy systems and storage devices, energy efficiency, smart energy material manufacturing in the smart grid paradigm, strategic energy planning, integration of renewable energy, and big data analytics in the smart grid environment. The investigated ML area in energy distribution systems includes energy consumption and price forecasting, the merit order of energy price forecasting, and the consumer lifetime value. Cybersecurity topics for power delivery and utilization, grid edge systems and distributed energy resources, power transmission, and distribution systems are also briefly studied. The primary goal of this work was to identify common issues useful in future studies on ML for smooth energy distribution operations. This study was concluded with many energy perspectives on significant opportunities and challenges. It is noted that if the smart ML automation is used in its targeting energy systems, the utility sector and energy industry could potentially save from $237 billion up to $813 billion. • A study on data-driven probabilistic machine learning (ML) in sustainable smart energy/smart energy systems is conducted. • The use of probabilistic ML in core energy technologies are briefly studied. • The ML techniques play a key role in integrating thermal, electric, large-scale renewable energy resources and fuel gird.A variety of tools for implementing ML in energy systems control, efficient management, and operations are discussed. • Recent key developments of ML, its challenges, and state-of-art future research opportunities are briefly described.
TL;DR: The Approximated Collaborative Sensor Fault Detection (ACSFD) scheme and its VLSI architecture are proposed and different sorting algorithm is implemented to evaluate the efficiency of the sorting network.
Abstract: A fault-tolerant distributed decision fusion in the presence of sensor faults via Collaborative Sensor Fault Detection (CSFD) was used traditionally. CSFD scheme is proposed in which the results of a homogeneity test are used to identify the faulty nodes within the network such that their quantized messages can be filtered out when estimating the parameter of interest. The scheme can identify the faulty nodes efficiently and improve the performance of the decision fusion significantly. It achieves very good performance at the expense of such extensive computations as exponent and multiplication/division in the detecting process. In many real-time WSN applications, the fusion center might be implemented in an ASIC and included in a stand-alone device. Therefore, a simple and efficient decision fusion scheme requiring lower hardware cost and power consumption is extremely desired. In this paper, we propose the Approximated Collaborative Sensor Fault Detection (ACSFD) scheme and its VLSI architecture. Sorting operation are required in ACSFD to find out four biggest faulty node indexes for subsequent usage for this purpose .we have implemented different sorting algorithm to evaluate the efficiency of the sorting network
••23 Jun 2020
TL;DR: A biosensor platform based on magnetized phage-based nanobots, when combined with standard, portable field equipment, allow for detection of <10 cfu/100 mL of viable E. coli within 7 h, faster than any methods published to date.
Abstract: Advances in synthetic biology, nanotechnology, and genetic engineering are allowing parallel advances in areas such as drug delivery and rapid diagnostics Although our current visions of nanobots may be far off, a generation of nanobots synthesized by engineering viruses is approaching Such tools can be used to solve complex problems where current methods do not meet current demands Assuring safe drinking water is crucial for minimizing the spread of waterborne illnesses Although extremely low levels of fecal contamination in drinking water are sufficient to cause a public health risk, it remains challenging to rapidly detect Escherichia coli, the standard fecal indicator organism Current methods sensitive enough to meet regulatory standards suffer from either prohibitively long incubation times or requirement of expensive, impractical equipment Bacteriophages, tuned by billions of years of evolution to bind viable bacteria and readily engineered to produce custom proteins, are uniquely suited to bacterial detection We have developed a biosensor platform based on magnetized phages encoding luminescent reporter enzymes This system utilizes bio-orthogonally functionalized phages to enable site-specific conjugation to magnetic nanoparticles The resulting phage-based nanobots, when combined with standard, portable field equipment, allow for detection of <10 cfu/100 mL of viable E coli within 7 h, faster than any methods published to date
TL;DR: An adaptive neuro-fuzzy inference system (ANFIS) estimation algorithm is developed in order to estimate a database of instantaneous photovoltaic power and a deep learning forecasting algorithm is realized to estimate the smart grid parameters so as to optimize the consumption energy.
Abstract: Renewable energy plays a very important role in solving energy problems, and solar energy is one of the most important renewable sources, especially in sunny countries.This paper deals with two pro...
TL;DR: A novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend.
Abstract: The literature discussing the concepts, technologies, and ICT-based urban innovation approaches of smart cities has been growing, along with initiatives from cities all over the world that are competing to improve their services and become smart and sustainable. However, current studies that provide a comprehensive understanding and reveal smart and sustainable city research trends and characteristics are still lacking. Meanwhile, policymakers and practitioners alike need to pursue progressive development. In response to this shortcoming, this research offers content analysis studies based on topic modeling approaches to capture the evolution and characteristics of topics in the scientific literature on smart and sustainable city research. More importantly, a novel topic-detecting algorithm based on the deep learning and clustering techniques, namely deep autoencoders-based fuzzy C-means (DFCM), is introduced for analyzing the research topic trend. The topics generated by this proposed algorithm have relatively higher coherence values than those generated by previously used topic detection methods, namely non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), and eigenspace-based fuzzy C-means (EFCM). The 30 main topics that appeared in topic modeling with the DFCM algorithm were classified into six groups (technology, energy, environment, transportation, e-governance, and human capital and welfare) that characterize the six dimensions of smart, sustainable city research.