Browsing by Author "Ravi Shankar Singh"

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A Parallel Algorithm for Wavelet Transform-Based Color Image Compression
(Walter de Gruyter GmbH, 2018) Piyush Kumar Singh; Ravi Shankar Singh; Kabindra Nath Rai
Wavelet transforms emerge as one of the popular techniques in image compression. This technique is accepted by the JPEG Committee for the next-generation image compression standard JPEG-2000. Convolution-based strategy is widely used in calculating the wavelet transform of the image. A convolution-based wavelet transform consists of a large number of multiplications and additions. A color image consists of a two-dimensional matrix each for red, green, and blue colors. An ordinary way to calculate the wavelet transform of a color image includes calculating the transform of the intensity matrix of the red, green, and blue components. In this article, we present a parallel algorithm for calculating the convolution-based wavelet transform of the red, green, and blue intensity components simultaneously in color images, which can run on commonly used processors. This means that it needs no extra hardware. The results are also compared to the nonparallel algorithm based on compression time, mean square error, compression ratio, and peak signal-to-noise ratio. Complexity analysis and comparative complexity analysis with some other papers are also shown here. © 2018 Walter de Gruyter GmbH, Berlin/Boston.
An image encryption algorithm based on XOR operation with approximation component in wavelet transform
(Institute of Electrical and Electronics Engineers Inc., 2016) Piyush Kumar Singh; Ravi Shankar Singh; Kabindra Nath Rai
Wavelet transform of the image generates the different components basically classified in the approximation and detail components. The approximation component has major information. In this paper a partial encryption technique is used, using only approximation component. In this paper a random array is generated which is XORed with the approximation component. The inverse transform of the matrix generated by this operation generates the encrypted image. The random array, the wavelet used and level of wavelet transform jointly serves as the key for the decryption process. In decryption is just reverse of the encryption steps. © 2015 IEEE.
Characteristics of IoT health data
(Elsevier, 2020) Ritesh Sharma; Sandeep S. Udmale; Anil Kumar Pandey; Ravi Shankar Singh
Among the variety of applications enabled by the Internet of Things (IoT), healthcare is the most attractive and vital. A lot (volume) of different types (variety) of health data are captured in real time (velocity) by the network of sensors either put on the body or placed in living environments while maintaining the correctness (veracity) of data. These captured health data are then aggregated and analyzed either locally or in the cloud, which is bringing a positive transformation in healthcare. These four V’s, namely, volume, variety, velocity, and veracity are known as the characteristics of IoT health data. As a result, this work mainly focuses on these characteristics. © 2021 Elsevier Inc. All rights reserved.
Evidence of asymmetric change in diurnal temperature range in recent decades over different agro-climatic zones of India
(John Wiley and Sons Ltd, 2021) Rajesh Kumar Mall; Manisha Chaturvedi; Nidhi Singh; Rajeev Bhatla; Ravi Shankar Singh; Akhilesh Gupta; Dev Niyogi
Diurnal temperature range (DTR) is an important indicator of climatic change and a critical thermal metric to assess the impact on agriculture and human health. This study investigates the seasonal, annual and decadal changes in the spatio-temporal trend in DTR and air temperatures (maximum: Tmax and minimum: Tmin) during 1951–2016 and solar radiation (Srad) during 1984–2016 over 14 different agro-climatic zones (ACZs) in India. The changes in the DTR trend between two time periods:1951–2016 and 1991–2016 (recent period) are also assessed. The results indicate an overall increasing trend in DTR (0.038°C/decade), Tmax (0.078°C/decade, significant), Tmin (0.049°C/decade) during 1951–2016 and Srad (0.10 MJ/m2/day/decade) during 1984–2016. However, a decreasing trend in DTR (−0.02°C/decade) and a significant increasing trend in Tmin (0.210°C/decade) was noted during 1991–2016. The decadal changes showed an evident decline in DTR during the recent period since 1991. The relative increase in Tmin (0.21°C/decade, significant) compared to Tmax (0.18°C/decade) resulted in a decreasing DTR trend. This was evident across the 5 out of the 14 agro-climatic zones for the 1991–2016 period. The seasonal analysis showed a significant (95%) increasing trend in DTR during pre-monsoon and monsoon (1951–2016), and a negative trend for the post-monsoon and monsoon since 1991. There were also interesting spatial differences found with the ACZs in the north-west, parts of Gangetic plain, north-east, and central India exhibiting negative DTR trends. The effect of Srad is larger on Tmax than Tmin; therefore, the decrease in Srad in parts of Gangetic plain likely contributed to a smaller increase in Tmax relative to Tmin and led to a decreasing trend in DTR. At the same time, the west coast, east coast, and southern region show positive trends. The observational analysis finds a distinct increase in the Tmin and also highlights the need for future assessments to continue investigate the causes of these spatio-temporal changes found in this study. © 2020 Royal Meteorological Society
Internet of things in the healthcare industry
(Elsevier, 2020) Sandeep S. Udmale; Anil Kumar Pandey; Ravi Shankar Singh; Sanjay Kumar Singh
Extensive research has helped to make the extraordinary progress in multiple technologies and as an effect it has strengthened the existing medical services. Specifically, the introduction of the Internet of things (IoT) in the healthcare industry has shown promising results by connecting the various medical resources for efficient utilization. Thus, an IoT-based healthcare system has been broadly accepted for trustworthy, useful, and smart healthcare services. This chapter aims to provide an overview of IoT and its importance in the healthcare industry. The business opportunity and quality of services requirements for the design and development of IoT-based systems are discussed. © 2021 Elsevier Inc. All rights reserved.
IoT healthcare architecture
(Elsevier, 2020) M. Jayashankara; Sandeep S. Udmale; Anil Kumar Pandey; Ravi Shankar Singh
Health is a major concern for everyone in today’s world. The number of health problems faced by people today is increasing, and largely regardless of their age. Identification of health-related issues can assist in finding a cure as early as possible and can also lead to a better quality of life. The traditional healthcare system is comparatively more time-consuming. Hence, an Internet of Things (IoT)-based healthcare system is a possible solution, because it provides more flexibility and takes less time as compared to the older system. A healthcare system based on the IoT can incorporate a variety of IoT healthcare architectures. Generally, the architecture consists of several components that interact with each other to perform data collection and analysis tasks to present vital information to the end user, physician, or caretaker. To provide such an important service to the different users, effective and adequate healthcare architectures based on the IoT using multiple sensors are the requirements of modern healthcare. Therefore, this work mainly focuses on IoT healthcare architectures using sensor nodes. © 2021 Elsevier Inc. All rights reserved.
IoT-Based Data Analytics for the Healthcare Industry: Techniques and Applications: A volume in Intelligent Data-Centric Systems
(Elsevier, 2020) Sanjay Kumar Singh; Anil Kumar Pandey; Ankit Chaudhary; Ravi Shankar Singh; Sandeep S. Udmale
IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization. The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand. © 2021 Elsevier Inc. All rights reserved.
Rodent Stroke Model Guidelines: An Update
(Springer Singapore, 2021) Amit Kumar Tripathi; Ravi Shankar Singh; Awakash Soni; Rajavashisth Tripathi; Ranjana Patnaik
According to a recent statistical report from the American Heart Association (AHA), stroke is the fifth major reason for mortality and morbidity. Stroke is a devastating clinical condition for which efficient interventions are not currently available. The rodent stroke model has critical significance in a series of preclinical tests for neuroprotection, neurotransmitters, and neuroplasticity studies. Despite using animal modeling in neuroprotective research, their modeling, development, and preclinical evaluation of animals have been rarely and consistently demonstrated. Comorbidities like aging, diabetes, hypertension, and smoking are major contributors to stroke and the use of comorbid rodent models of stroke is clinically relevant to the human disease condition. Animals, like hypertensive rats and diabetic mice, these comorbid models, are used to explore the pathophysiology of cerebral stroke. Regional cerebral blood flow (rCBF) real-time tracking of arterial blood flow by using laser Doppler flowmetry (LDF) provide evidence for the success of middle cerebral artery (MCA) occlusion. Several factors play an important role in neurological outcomes, while have not happened before effectively addressed in Stroke Therapy Academic Industry Roundtable (STAIR) recommendations. Here, we describe the general guiding principle for a preclinical stroke model, STAIR committee recommendation, the procedure to develop intraluminal Middle Cerebral Artery Occlusion (MCAO) in rodent, surgical procedure optimization, MCA occluders like silicone-rubber coated, nylon coated, nail-polish coated, methyl methacrylate coated, poly-L-Lysine (PLL) coated and resin-coated and their physical properties. Besides, we will provide information on neurobehavior assessment, neuroprotection by volatile anesthesia molecule (halothane, isoflurane, desflurane, sevoflurane, and ketamine/xylazine cocktail), hypothermia induced neuroprotection, and hyperthermia damaging effect on ischemic injury. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.
Wavelets with application in image compression
(IGI Global, 2016) Piyush Kumar Singh; Ravi Shankar Singh; Kabindra Nath Rai
This chapter focus mainly on different wavelet transform algorithms as Burt's Pyramid, Mallat's Pyramidal Algorithm, Feauveau's non dyadic structure and its application in Image compression. This chapter focus on mathematical concepts involved in wavelet transform like convolution, scaling function, wavelet function, Multiresolution analysis, inner product etc., and how these mathematical concepts are liked to image transform application. This chapter gives an idea towards wavelets and wavelet transforms. Image compression based on wavelet transform consists of transform, quantization and encoding. Basic focus is not only on transform step, selection of particular wavelet, wavelets involved in new standard of image compression but also on quantization and encoding, Huffman code, run length code. Difference in between JPEG and JPEG2000, Quantization and sampling, wavelet function and wavelet transform are also given. This chapter is also giving some basic idea of MATLAB to assist readers in understanding MATLAB Programming in terms of image processing. © 2017, IGI Global. All rights reserved.