A communication infrastructure is an essential part to the success of the emerging smart grid. A scalable and pervasive communication infrastructure is crucial in both construction and operation of a smart grid. In this paper, we present the background and motivation of communication infrastructures in smart grid systems. We also summarize major requirements that smart grid communications must meet. From the experience of several industrial trials on smart grid with communication infrastructures, we expect that the traditional carbon fuel based power plants can cooperate with emerging distributed renewable energy such as wind, solar, etc, to reduce the carbon fuel consumption and consequent green house gas such as carbon dioxide emission. The consumers can minimize their expense on energy by adjusting their intelligent home appliance operations to avoid the peak hours and utilize the renewable energy instead. We further explore the challenges for a communication infrastructure as the part of a complex smart grid system. Since a smart grid system might have over millions of consumers and devices, the demand of its reliability and security is extremely critical. Through a communication infrastructure, a smart grid can improve power reliability and quality to eliminate electricity blackout. Security is a challenging issue since the on-going smart grid systems facing increasing vulnerabilities as more and more automation, remote monitoring/controlling and supervision entities are interconnected.

https://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1315&context=electricalengineeringfacpub

A Survey on Smart Grid CommunicationInfrastructures: Motivations, Requirements andChallenges

Face verification systems are prone to spoofing attacks on photos, videos, and 3D masks. Face spoofing detection, i.e., face anti-spoofing, face liveness detection, or face presentation attack detection, is an important task for securing face verification systems in practice and presents many challenges. In this paper, a state-of-the-art face spoofing detection method based on a depth-based Fully Convolutional Network (FCN) is revisited. Different supervision schemes, including global and local label supervisions, are comprehensively investigated. A generic theoretical analysis and associated simulation are provided to demonstrate that local label supervision is more suitable than global label supervision for local tasks with insufficient training samples, such as the face spoofing detection task. Based on the analysis, the Spatial Aggregation of Pixel-level Local Classifiers (SAPLC), which is composed of an FCN part and an aggregation part, is proposed. The FCN part predicts the pixel-level ternary labels, which include the genuine foreground, the spoofed foreground, and the undetermined background. Then, these labels are aggregated together to yield an accurate image-level decision. Furthermore, to quantitatively evaluate the proposed SAPLC, experiments are carried out on the CASIA-FASD, Replay-Attack, OULU-NPU, and SiW datasets. The experiments show that the proposed SAPLC outperforms the representative deep networks, including two globally supervised CNNs, one depth-based FCN, two FCNs with binary labels, and two FCNs with ternary labels, and achieves competitive performances close to some state-of-the-art method performances under various common protocols. Overall, the results empirically verify the advantage of the proposed pixel-level local label supervision scheme.

Face Spoofing Detection Based on Local Ternary Label Supervision in Fully Convolutional Networks

Day by day, all the research communities have been focusing on digital image retrieval due to more internet and social media uses. In this paper, a U-Net-based neural network is proposed for the segmentation process and Haar DWT and lifting wavelet schemes are used for feature extraction in content-based image retrieval (CBIR). Haar wavelet is preferred as it is easy to understand, very simple to compute, and the fastest. The U-Net-based neural network (CNN) gives more accurate results than the existing methodology because deep learning techniques extract low-level and high-level features from the input image. For the evaluation process, two benchmark datasets are used, and the accuracy of the proposed method is 93.01% and 88.39% on Corel 1K and Corel 5K. U-Net is used for the segmentation purpose, and it reduces the dimension of the feature vector and feature extraction time by 5 seconds compared to the existing methods. According to the performance analysis, the proposed work has proven that U-Net improves image retrieval performance in terms of accuracy, precision, and recall on both the benchmark datasets.

/pdf/object-based-image-retrieval-using-the-u-net-based-neural-15q3r5uaov.pdf

Object-Based Image Retrieval Using the U-Net-Based Neural Network.

Cotton is the natural fiber produced, and the commercial crop grown in monoculture on 2.5% of total agricultural land. Cotton is a drought-resistant crop that provides a reliable income to the farmers that grow under the area with a threat from climatic change. These cotton crops are being affected by bacterial, fungal, viral, and other parasitic diseases that may vary due to the climatic conditions resulting in the crop’s low productivity. The most prone to diseases is the leaf that results in the damage of the plant and sometimes the whole crop. Most of the diseases occur only on leaf parts of the cotton plant. The primary purpose of disease detection has always been to identify the diseases affecting the plant in the early stages using traditional techniques for better production. To detect these cotton leaf diseases appropriately, the prior knowledge and utilization of several image processing methods and machine learning techniques are helpful.

/pdf/a-comparative-analysis-of-machine-learning-algorithms-for-ppoz2w6ifl.pdf

A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

Agriculture provides food to all the human beings even in case of rapid increase in the population. It is recommended to predict the plant diseases at their early stage in the field of agriculture is essential to cater the food to the overall population. But it unfortunate to predict the diseases at the early stage of the crops. The idea behind the paper is to bring awareness amongst the farmers about the cutting-edge technologies to reduces diseases in plant leaf. Since tomato is merely available vegetable, the approaches of machine learning and image processing with an accurate algorithm is identified to detect the leaf diseases in the tomato plant. In this investigation, the samples of tomato leaves having disorders are considered. With these disorder samples of tomato leaves, the farmers will easily find the diseases based on the early symptoms. Firstly, the samples of tomato leaves are resized to 256 × 256 pixels and then Histogram Equalization is used to improve the quality of tomato samples. The K-means clustering is introduced for partitioning of dataspace into Voronoi cells. The boundary of leaf samples is extracted using contour tracing. The multiple descriptors viz., Discrete Wavelet Transform, Principal Component Analysis and Grey Level Co-occurrence Matrix are used to extract the informative features of the leaf samples. Finally, the extracted features are classified using machine learning approaches such as Support Vector Machine (SVM), Convolutional Neural Network (CNN) and K-Nearest Neighbor (K-NN). The accuracy of the proposed model is tested using SVM (88%), K-NN (97%) and CNN (99.6%) on tomato disordered samples. 

Plant Leaf Disease Detection using Computer Vision and Machine Learning Algorithms

In the last 57 years, face biometrie researchers have achieved many successes. Face recognition systems have been extensively used in government as well as commercial applications such as mobile, banking and surveillance systems etc. In the last 10 years, the whole biometric community such as researchers, developers, and retailers have worked on challenging tasks to develop a more accurate protection method against spoofing threats. The face spoofing attacks affect high-security field in the companies, government sectors, rising small and medium sized endeavors. Although several face antispoofing or liveness detection methods have been proposed, the issue is still unresolved due to difficulty in finding the features and methods for spoof attacks. Recently it has been shown that the traditional face biometric techniques are more vulnerable to spoofing attacks, so entire research community required to concentrate more to resolve solutions against spoofing attacks. The goal of this paper is to provide a detailed study of antispoofing methodologies and evaluation of databases. The study concluded that there is a need to provide more generalized algorithms for detection of unpredictable spoofing attacks in order to make the system more secure, computationally efficient and reliable.

A comparative study on face spoofing attacks

Nowadays content-based image retrieval (CBIR) framework is drawing in consideration of numerous analysts because of far-reaching applications found in numerous territories. In this paper, a new CBIR methodology is proposed and adequacy of any CBIR framework relies upon the features extracted from a color picture. In this work, firstly find the region of interest of the image using Sobel and Canny method and later on output is applied on HSV color space, it is clear to human vision eye. For classification, neural network is used and categorized the data with class labels. The similarity distance is estimated between the query image and stored image with different similarity metrics like Manhattan distance, Euclidean distance, Chebyshev, Hamming distance and Jaccard distance. The experimental result is estimated on accuracy, precision. The experiment performed on two well-known databases i.e.: Corel-1k and Corel-5k dataset and new methodology proves the better accuracy results up to 87.33% and 68.93% respectively and improves the precision results also up to 86.36% and 68.47% respectively. In this paper, results are also extended up to 80%.

Color Object Detection Based Image Retrieval Using ROI Segmentation with Multi-Feature Method

Detection of diseases in plants is a significant task that has to be done in agriculture. This is something on which the economy profoundly depends. Infection discovery in plants is a significant job in the agribusiness field, as having diseases in plants is very common. To recognize the diseases in leaves, a continuous observation of the plants is required. This observation or continuous monitoring of the plants takes a lot of human effort and it is time-consuming too. To make it simply some sort of programmed strategy is required to observe the plants. Program based identification of diseases in plants makes easier to detect the damaged leaves and reduces human efforts and time-saving. The proposed algorithm distinguishing sickness in plants and classify them more accurately as compared to existing techniques.

Leaf Disease Detection and Classification based on Machine Learning

Nowadays gender classification is a very challenging task in a real-time application based on face recognition. The demand for real-time application based on gender classification will increase in the future. Bag of Words (Bow), Scale Invariant Fourier Transform (SIFT) and K-means clustering are used for feature extractors and classification. This state of art methodology gives more efficient result on different standard datasets. This research proposed a new algorithm for automatic live Gender Recognition (GR) using Support Vector Machine (SVM) is used for classification. The implementation of result work tested on FEI, Live Images and SCIEN database for GR. The detection rate has reached up to 98% in FEI dataset, 94% in Live/Own dataset and 91% SCIEN dataset respectively. This proposed state of art methodology is compared with the previous techniques and achieved better results which will help in the development of real-time identification systems.

Sandeep Kumar

Papers

A Comparative Analysis of Machine Learning Algorithms for Detection of Organic and Nonorganic Cotton Diseases

A comparative study on face spoofing attacks

Color Object Detection Based Image Retrieval Using ROI Segmentation with Multi-Feature Method

Leaf Disease Detection and Classification based on Machine Learning

Gender Classification Using Machine Learning with Multi-Feature Method