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Boopathy, S.
- Identification of Diseases in Grapes Using Gray Level Co-Occurrence Matrix & Wavelet Statistical Features
Authors
1 ECE Department, Sona College of Technology, Salem, Tamil Nadu, IN
2 Sona College of Technology, Salem, Tamil Nadu, IN
Source
Digital Image Processing, Vol 4, No 5 (2012), Pagination: 273-278Abstract
Grapes are a crop that is susceptible to many diseases. However, the degree of susceptibility varies depending on the variety. When no pest management is carried out, damage can generally be severe. Downy mildew and powdery mildew are the major grape diseases in India. Evidently, these diseases can be easily predicted based on the climatic conditions determined by agricultural experts. Technological strategies using machine vision and artificial intelligence are being investigated to achieve intelligent farming forbetter yield. As a part of the prediction process in Grapes, this paper initially deals with the identification of type of disease that has occurred in a grape vine, with a special focus on its leaves. The first step in an effective pest management program is correct identification of the disease. This paper uses GLCM (Gray Level Co-occurrence Matrix) and Wavelet statistical Features to determine whether a given grape leaf is affected with Powdery Mildew or Downy Mildew by comparing the statistical features with that of an unaffected leaf. The developed algorithm's efficiency can successfully detect and classify the examined diseases with a precision of 94%.Keywords
Grapes, GLCM (Gray Level Co-Occurrence Matrix), Wavelet Transform, Color Thresholding Powdery Mildew and Downy Mildew and Wavelet Statistical Features.
- Video Distribution with Energy Efficient Statistical QOS Provision over Wireless Networks
Authors
Source
International Journal of Innovative Research and Development, Vol 2, No 4 (2013), Pagination: 628-637Abstract
The resource allocation problem for general multi hop multicast network flows and derives the optimal solution that minimizes the total energy consumption while guaranteeing a statistical end-to-end delay bound on each network path. Existing approaches use negated signal-to-noise ratio as link weights on the complete network graph, finds the minimum spanning tree using those weights to maximize the sum rate, and performs optimal resource allocation on the flow corresponding to the obtained tree structure and maintains a set of dominant flows that are optimal for a potentially large percentage of channel states under a certain network topology and performs flow selection. We propose network flow based algorithm allocates resources in the I th iteration, until all resources are exhausted and the utility is maximized by minimizing the flow cost representing the negative values of 'data rate'. In contrast to maximum-utility resource allocation, the problem of minimum power subject to rate target that we consider does not admit a single-stage multi-commodity flow formulation. In the proposed NFBA, we maximize the 'potential power saving' on the flow instead of minimizing the cost, in a Ft adaptively. We analyze our proposed scheme in terms of complexity, power and cost.Full Text
- VLSI Implementation of Sobel Edge Detection
Authors
1 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, TamilNadu, IN
Source
Digital Image Processing, Vol 11, No 3 (2019), Pagination: 41-43Abstract
Edge detection is the process of identifying and finding sharp discontinuities in an image. Sobel edge detection algorithm is a gradient-based edge detection method, which finds edges using Horizontal Mask (HM) and Vertical Mask (VM). Sobel edge detection algorithm is selected due to its property of less deterioration in high level of noise. The proposed work uses a modified architecture by replacing 10-bit addition with 8-bit addition using shift operator for reducing the time and architecture complexity of Sobel edge detection algorithm. Additionally, low power adder is used for reducing the power consumption when the value of the operand remains constant. The adder is divided into two parts, i.e., the Most Significant Part (MSP) and the Least Significant Part (LSP). The MSP of the original adder is adjusted to include detection logic circuits. When the MSP is required, the input data of MSP remain unchanged. However, when the MSP is not required, the input data of the MSP become zeros to avoid glitching power consumption. The two operands of the MSP enter the detection-logic unit, except the adder, so that the detection-logic unit can decide whether to turn off the MSP or not. The bottleneck of fixed processor speed affects the image-processing algorithms in software implementation. This has been succeeded in dealing with the advancements in VLSI technology. The proposed work presents the design of edge detection using VHDL language.
Keywords
FPGA, Sobel Operator, Low Power Adder, EDGE Detection.References
- Muthukrishnan. R and M. Radha Edge detection techniques for image segmentation (2011).
- Nazma Nausheena, Ayan Seala, Pritee Khannaa, Santanu Halderb., A FPGA based implementation of Sobel edge detection(2017)
- A. Prashanth, R. Paramesh Waran, Sucheta Khandekar and Sarika Pawar, Low Power High Speed based Various Adder Architectures using SPST(2016)
- O. R. Vincent, O. Folorunso., A Descriptive Algorithm for Sobel Image Edge Detection(2009)
- S. D. Brown, R. J. Francis, J. Rose, Z. G. Vranesic, Field-Programmable Gate Arrays, vol. 180, Springer Science & Business Media, 2012.
- Samta Gupta, Susmita Ghosh Mazumdar., Sobel Edge Detection Algorithm(2013)
- R. C. Gonzalez, R. E. Woods, S. L. Eddins, Digital image processing using matlab (2004).
- S. Halder, D. Bhattacharjee, M. Nasipuri, D.K. Basu, A Fast FPGA Based Architecture for Sobel Edge Detection, Progress in VLSI Design and Test, Springer, 2012, pp. 300–306.
- D. L. Perry, VHDL: Programming by Example, vol. 4, McGraw-Hill, 2002.
- J. D. Plummer, Silicon VLSI Technology: Fundamentals, Practice, and Modeling, Pearson Education India, 2009.
- J. C. Russ, The Image Processing Handbook, CRC press, 2015.
- A. G. Vicente, I.B. Munoz, P.J. Molina, J.L.L. Galilea, Embedded vision modules for tracking and counting people, Instrum. Meas. IEEE Trans. 58 (9) (2009) 3004–3011.
- Patient Health Monitoring System-Ingenious Life Saver
Authors
1 Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, TamilNadu, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 11, No 3 (2019), Pagination: 49-52Abstract
Hospital management is the complex environment, in which the multi team of clinicians (nurses, therapists and physicians) continually observed and evaluates patient information. In which, drug delivery is a vital component of patient care. To monitor the pulse rate, body temperature, flow of glucose infusion rate of patients in hospitals we need physicians/nurses. The incorrect infusion of glucose rate may even result in heart arrest. In India, these systems are not automated due to complexity and excessive cost for implementation of the system. This is based up on the communication devices like Mobile phones and sensor networks for the real time analysis of patient health The impact of our solution is to provide consistent flow of glucose infusion rate by automation through the measurement of pulse rate, body temperature of the patients. This product prevents the patients from adverse effect and saves the human life.
Keywords
Component, Patient Care, Mobile Phone, Sensor, Automation.References
- Lei Clifton, David A. Clifton, Marco A. F. Pimentel, Peter J. Watkinson, and Lionel Tarassenko,“Predictive Monitoring of Mobile Patients by Combining Clinical Observations With Data From Wearable Sensors”vol. 18, no. 3 pp. 722-730 may 2014.
- Luisa Flohr,shaylene Beaudry et.al, “Clinician-Driven Design of VITALPAD-An intelligent Monitoring and Communication Device to Improve patient safety in the intensive Care unit” –vol.6 2018.
- Jianqiang Hu et.al, “Cloud Assisted Home Health Monitoring System”-pp.899-903may 2017.
- Lisa Yu et.al, “Personalize Health Monitoring System of Elderly Wellness at the Community Level in Hong Kong”-vol.6 2018.
- M. Omoogun, V. Ramsurrun, S. Guess, P. Seam, X. Bellekens, A. Seeam, "Critical patient eHealth monitoring system using wearable sensors", 2017 1st International Conference on Next Generation Computing Applications (NextComp), pp. 169-174, 2017.
- P. Gope, T. Hwang, "Bsn-care: a secure iot-based modern healthcare system using body sensor network", IEEE Sensors Journal, vol. 16, no. 5, pp. 1368-1376, 2017.
- S. Haynes, K. K. Kim, "A mobile care coordination system for the management of complex chronic disease", Stud. Health Technol. Inform., vol. 225, pp. 505-509, Aug. 2016.
- S. Balambigai, P. Jecvitha, "A survey on investigation of vital human parameters to predict the risk of cardiovascular diseases", 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Jan 2017.
- S. Boopathy, N. Ramkumar “Controlling the Boiler Temperature of Tea Leaves by using Android Application and Arduino UNO”, International Journal of Modern Computer Science, Vol 4, Issue 3, pp 57-60.
- S. Boopathy, P Govindaraju, N. Ramkumar, P. Premkumar, “Implementation of Automatic Fertigation System by Measuring the Plant Parameters”, International Journal of Engineering Research & Technology, vol 3, Issue 10, pp 583-586.
- S. Boopathy M. Jothibasu, “A Fast Cloud Based Pervasive Method of Cart Billing System for Supermarket Using Real Time Technology”, International Journal of Advanced Research in Computer Science, vol 8, issue 3, pp 209-212.