College of Engineering, Pune

About: College of Engineering, Pune is a based out in . It is known for research contribution in the topics: Computer science & Sliding mode control. The organization has 4264 authors who have published 3492 publications receiving 19371 citations. The organization is also known as: COEP.

SVM classifier based grape leaf disease detection

Abstract: Grape constitutes one of the most widely grown fruit crops in the India. Productivity of grape decreases due to infections caused by various types of diseases on its fruit, stem and leaf. Leaf diseases are mainly caused by bacteria, fungi, virus etc. Diseases are a major factor limiting fruit production and diseases are often difficult to control. Without accurate disease diagnosis, proper control actions cannot be used at the appropriate time. Image Processing is one of the widely used technique is adopted for the plant leaf diseases detection and classification. This paper is intended to aid in the detection and classification leaf diseases of grape using SVM classification technique. First the diseased region is found using segmentation by K-means clustering, then both color and texture features are extracted. Finally classification technique is used to detect the type of leaf disease. The proposed system can successfully detect and classify the examined disease with accuracy of 88.89%.

Performance Analysis of MUSIC and ESPRIT DOA Estimation Algorithms for Adaptive Array Smart Antenna in Mobile Communication

Abstract: Adaptive array smart antenna involves the array processing to manipulate the signals induced on various antenna elements in such way that the main beam directing towards the desired signal and forming the nulls towards the interferers. Such smart antennas are widely used in wireless mobile communications as they can increase the channel capacity and coverage range. In adaptive array smart antenna, to locate the desired signal, various direction of arrival (DOA) estimation algorithms are used. This paper investigates and compares MUSIC and ESPRIT, DOA estimation algorithms which are widely used in the design of smart antenna system. MUSIC and ESPRIT algorithms provide high angular resolution and hence they are explored much in detail by varying various parameters of smart antenna system. However simulation in this paper shows that MUSIC algorithm is highly accurate and stable and provides high angular resolution compared to ESPRIT and hence MUSIC algorithm can be widely used in mobile communication to estimate the DOA of the arriving signals.

Machine learning for predictive maintenance of industrial machines using IoT sensor data

Abstract: The industrial Internet of Things (IIoT) is the use of Internet of Things (IoT) technologies in manufacturing which harnesses the machine data generated by various sensors and applies various analytics on it to gain useful information. The data captured by the machines is usually accompanied by a date time component which proves vital for predictive modelling. This paper explores the use of AutoRegressive Integrated Moving Average (ARIMA) forecasting on the time series data collected from various sensors from a Slitting Machine, to predict the possible failures and quality defects, thus improving the overall manufacturing process. The use of Machine Learning thus proves a vital component in IIoT having use cases in quality management and quality control, lowering the cost of maintenance and improving the overall manufacturing process.

Femto-photography: capturing and visualizing the propagation of light

Abstract: We present femto-photography, a novel imaging technique to capture and visualize the propagation of light. With an effective exposure time of 1.85 picoseconds (ps) per frame, we reconstruct movies of ultrafast events at an equivalent resolution of about one half trillion frames per second. Because cameras with this shutter speed do not exist, we re-purpose modern imaging hardware to record an ensemble average of repeatable events that are synchronized to a streak sensor, in which the time of arrival of light from the scene is coded in one of the sensor's spatial dimensions. We introduce reconstruction methods that allow us to visualize the propagation of femtosecond light pulses through macroscopic scenes; at such fast resolution, we must consider the notion of time-unwarping between the camera's and the world's space-time coordinate systems to take into account effects associated with the finite speed of light. We apply our femto-photography technique to visualizations of very different scenes, which allow us to observe the rich dynamics of time-resolved light transport effects, including scattering, specular reflections, diffuse interreflections, diffraction, caustics, and subsurface scattering. Our work has potential applications in artistic, educational, and scientific visualizations; industrial imaging to analyze material properties; and medical imaging to reconstruct subsurface elements. In addition, our time-resolved technique may motivate new forms of computational photography.