Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

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

Using channel pruning-based YOLO v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments

This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD), the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer (denote as “fc”). The experimental results show that the proposed technique is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases.

/pdf/can-deep-learning-identify-tomato-leaf-disease-4ljwwpncgz.pdf

Can Deep Learning Identify Tomato Leaf Disease

Nowadays, the smart grid has demonstrated a great ability to make life easier and more comfortable given recent advances. This paper studies the above issue from the perspective of two important and very useful smart grid applications, i.e., the advanced metering infrastructure and demand response using the instrumentality of a set of well-known scheduling algorithms, e.g., best-channel quality indicator, log rule, round robin, and exponential–proportional fairness to validate the performance. To increase the data transmission bandwidth, a new concept of optical wireless communication known as free-space optical communication (FSO) system based on microring resonator (MRR) with the ability to deliver up to gigabit (line of sight) transmission per second is proposed for the two studied smart grid applications. The range between 374.7 and 374.79 THz frequency band was chosen for the generation of 10 successive-carriers with a free spectral range of 8.87 GHz. The ten multi-carriers were produced through drop port of the MRR. The results show up to 10 times bandwidth improvement over the radius as large as 600 m and maintain receive power higher than the minimum threshold (− 20 dBm) at the controller/users, so the overall system is still able to detect the FSO signal and extract the original data without detection.

A novel free space communication system using nonlinear InGaAsP microsystem resonators for enabling power-control toward smart cities

In an agriculture field paddy is one of the major staple foods in the Asian countries like China, India, Indonesia, and Bangladesh etc. But paddy disease likes a Blast; Bacterial Leaf Blight; Rice tungro etc. stops the growth of the paddy trees. If the diseases are not detected at an early possible stage then there is a decrease in the production of paddy. In this paper a noble methodology i.e. image processing of the paddy leaf by histogram is proposed, to avoid large scale effect of these diseases. By this approach one can detect the disease at a very primary stage and thus can take necessary steps in time to minimize the loss of production. At first, an image of a paddy leaf is captured, and then processed for enhancement. Later, the image is converted from RGB colour image to gray image and then extracted to a histogram using MATLAB functions. The resultants of images are given as input to identify disease from classification of diseases and grading the diseases. After completing disease identification, and stage detection, a consultative treatment module of the disease was prepared with the help of agricultural experts.

/pdf/damaged-paddy-leaf-detection-using-image-processing-240aum4jrz.pdf

Damaged paddy leaf detection using image processing

Edge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities. In this paper a simple edge detection technique is performed by convoluting the image using a five order mask and then Selecting a threshold from taking the sum of mean value and the standard deviation of the gradient image within a 3 X 3 window. In Canny edge detection a gauss kernel is used for smoothing the image in order to eliminate the noise. But in our algorithm a single mask perform the noise smoothing as well as finding the edge strength.

Development of Edge Detection Technique for Images Using Adaptive Thresholding

Panic is an impetuous occurrence that can emerge on the human face based on any abrupt situation or atypical circumference. The facial expression of human being is nothing but resembles a reflection of the circumstances or consequences those he or she is facing or yet to face. The happening of panic has become an alarming phenomenon in the present era. Isolation of a people enriched with abnormal facial expression is one of the tremendous difficult tasks. In this paper we have proposed a new methodology based on statistical approach with respect to time span series. Firstly, we are to capture facial expressions with the help of high resolution camera and stored them in a database. After that we followed transformation for image enhancement. We have considered Wiener filter for Image Restoration. Then we are to identify the span spaces to collect the span points of the facial image with respect to nose position and we are to calculate the serial correlation for Ranking Expression. Finally, the geometric value of time span series represents the data of panic expression.

Statistical Approach for Extraction of Panic Expression

A filter is a appliance or progression that confiscates various redundant components or aspects from a signal. Discrete Fourier Transform (DFT) is utilized for completing frequency psychotherapy of discrete time signals. DFT gives a discrete in frequency domain representation whereas the other transforms produced continuous in frequency domain. Other hands Finite Impulse Response (FIR) filter is a filter whose impulse rejoinder or response to any input (finite length), is of finite duration, because it settles to zero in finite time. In this research articles we are trying to combine the discrete Fourier transform and finite impulse response filter for improve of impulse response.

Debabrata Samanta

Papers

A novel free space communication system using nonlinear InGaAsP microsystem resonators for enabling power-control toward smart cities

Damaged paddy leaf detection using image processing

Development of Edge Detection Technique for Images Using Adaptive Thresholding

Statistical Approach for Extraction of Panic Expression

A combining approach using DFT and FIR filter to enhance impulse response