College of Engineering, Pune

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

Appearance and characterization of fruit image textures for quality sorting using wavelet transform and genetic algorithms.

Abstract: Images of four qualities of mangoes and guavas are evaluated for color and textural features to characterize and classify them, and to model the fruit appearance grading. The paper discusses three approaches to identify most discriminating texture features of both the fruits. In the first approach, fruit's color and texture features are selected using Mahalanobis distance. A total of 20 color features and 40 textural features are extracted for analysis. Using Mahalanobis distance and feature intercorrelation analyses, one best color feature (mean of a* [L*a*b* color space]) and two textural features (energy a*, contrast of H*) are selected as features for Guava while two best color features (R std, H std) and one textural features (energy b*) are selected as features for mangoes with the highest discriminate power. The second approach studies some common wavelet families for searching the best classification model for fruit quality grading. The wavelet features extracted from five basic mother wavelets (db, bior, rbior, Coif, Sym) are explored to characterize fruits texture appearance. In third approach, genetic algorithm is used to select only those color and wavelet texture features that are relevant to the separation of the class, from a large universe of features. The study shows that image color and texture features which were identified using a genetic algorithm can distinguish between various qualities classes of fruits. The experimental results showed that support vector machine classifier is elected for Guava grading with an accuracy of 97.61% and artificial neural network is elected from Mango grading with an accuracy of 95.65%. Practical applications The proposed method is nondestructive fruit quality assessment method. The experimental results has proven that Genetic algorithm along with wavelet textures feature has potential to discriminate fruit quality. Finally, it can be concluded that discussed method is an accurate, reliable, and objective tool to determine fruit quality namely Mango and Guava, and might be applicable to in-line sorting systems

Land Usage Identification with Fusion of Thepade SBTC and Sauvola Thresholding Features of Aerial Images Using Ensemble of Machine Learning Algorithms

Abstract: Detecting the appropriate usage of a piece of land is known as Land Usage Mining. The key resource to detect the Land Usages is aerial Images. The advancement in technology in form of satellites, d...

Fingerprint matching using transform features

Abstract: In the fingerprint recognition application utilizing more information other than minutiae is much helpful. We present here a fingerprint matching scheme based on transform features and their comparison. The technique described here obviates the need for extracting minutiae points to match fingerprint images. The proposed scheme uses Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) to create feature vector for fingerprints. After finding out the core point, fingerprint image of size 64times64 is cropped around the core point. The transform is applied on the cropped image without any pre-processing. The transform coefficients are arranged in specific manner and are used to obtain the feature vector in terms of standard deviation. The fingerprint matching is based on the minimum Euclidean distance between two feature vectors. Here database is formed by capturing 8 images per person using 500 dpi optical scanner. Training images used to form feature vector are 2, 4 or 6 per person. In the matching phase either all or remaining images are checked in identification mode to find out the percentage recognition rate. Comparison for all the three transform is presented here and it is observed that DCT and DFT gives better result as compared DWT.

Role of nano TiO2 and nano ZnO particles on enhancing the electrochemical and mechanical properties of electrochemically deposited phosphate coatings

Abstract: In this work, nanocrystalline phosphate coatings are systematically produced on low carbon steel with different preparation modalities using nano TiO2 and nano ZnO particles. For the sake of comparison, normal zinc phosphate coatings are also produced. All the phosphate coated samples are subjected to the thermal annealing at 100, 200, 300 and 400 °C for 1 h in argon atmosphere. Our results through combined electrochemical characterization and nanoindentation experiments revealed that nano TiO2 and nano ZnO particles act as a sealing agent, and therefore prevent crack propagation which enhances the electrochemical and mechanical properties of these coatings. Annealing treatment, along with the recrystallization of metallic matrix, promotes the corrosion resistance of all the phosphate coatings. In fact, among all the classes of coatings, annealed samples at 400 °C exhibit higher nanomechanical properties and lower corrosion rates. The results also invoked that nano TiO2 phosphate behaviour prevails over other two. Therefore, this dominant behaviour is mostly due to the accumulation of hard Zn-P precipitates upon annealing which exfoliates the outermost layer and therefore exposing the underneath layer to corrosive media, eventually delaying the onsets of corrosion attack. Our results highlight, for the first time, the ample avenues of enhancement in the electrochemical performance as well as nanomechanical properties of electrochemically deposited phosphate coatings using the incorporation of nano TiO2 and nano ZnO particles in water based phosphate bath followed by subsequent cost effective annealing treatment.

Characterization of Wireless Sensor Networks for Traffic a Delay

Abstract: The sensor network is a collection of different sensor nodes. At a moments of information transfer in the sensor network. Data packets should be transmitted via different nodes to the sink node. Wireless sensor network have a Throughput, energy, Reliability, congestion and delay are the different quality of services or parameters. For the reliable data packets transmission in the network require reliability as well as less traffic or congestion in the network. To manage E to E delay or latency in the network requires control the transfer of data packet, path and traffic in the network. For reliable communication, packets will be sending from redundant path if there exists traffic problem. If in the network given huge amount of data packet to node then at that instance created many times traffic problem or congestion. so here improve all these problems. Delay problem solve the transmission problem. In this paper analyse & characterize the traffic and delay. This paper shows the better performance of routing protocols with comparisons & which protocol take a minimum delay with better Average Packet delivery Ratio(PDR) for reliable communication in wireless sensor network.