From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

In this paper an extensive literature review on load–frequency control (LFC) problem in power system has been highlighted. The various configuration of power system models and control techniques/strategies that concerns to LFC issues have been addressed in conventional as well as distribution generation-based power systems. Further, investigations on LFC challenges incorporating storage devices BESS/SMES, FACTS devices, wind–diesel and PV systems etc have been discussed too.

A literature survey on load–frequency control for conventional and distribution generation power systems

Emotion recognition in human-computer interaction

Bacteria foraging optimization algorithm based load frequency controller for interconnected power system

Taber's Cyclopedic Medical Dictionary

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

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Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network.

Statistics show that the risk of autism spectrum disorder (ASD) is increasing in the world. Early diagnosis is most important factor in treatment of ASD. Thus far, the childhood diagnosis of ASD has been done based on clinical interviews and behavioral observations. There is a significant need to reduce the use of traditional diagnostic techniques and to diagnose this disorder in the right time and before the manifestation of behavioral symptoms. The purpose of this study is to present the intelligent model to diagnose ASD in young children based on resting-state functional magnetic resonance imaging (rs-fMRI) data using convolutional neural networks (CNNs). CNNs, which are by far one of the most powerful deep learning algorithms, are mainly trained using datasets with large numbers of samples. However, obtaining comprehensive datasets such as ImageNet and achieving acceptable results in medical imaging domain have become challenges. In order to overcome these two challenges, the two methods of “combining classifiers,” both dynamic (mixture of experts) and static (simple ‌Bayes) approaches, and “transfer learning” were used in this analysis. In addition, since diagnosis of ASD will be much more effective at an early age, samples ranging in age from 5 to 10 years from global Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets were used in this research. The accuracy, sensitivity, and specificity of presented model outperform the results of previous studies conducted on ABIDE I dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7273, sensitivity = 0.712, specificity = 0.7348). Furthermore, acceptable classification results were obtained from ABIDE II dataset (the best results obtained from Adamax optimization technique: accuracy = 0.7, sensitivity = 0.582, specificity = 0.804) and the combination of ABIDE I and ABIDE II datasets (the best results obtained from Adam optimization technique: accuracy = 0.7045, sensitivity = 0.679, specificity = 0.7421). We can conclude that the proposed architecture can be considered as an efficient tool for diagnosis of ASD in young children. From another perspective, this proposed method can be applied to analyzing rs-fMRI data related to brain dysfunctions.

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks

In this paper designing of multi-objective PID controller for load frequency control (LFC) based on adaptive weighted particle swarm optimization (AWPSO) has been proposed. Conventional methods such as Ziegler-Nichols and Cohen-Coon are based on trial-and-error and their best performances are achieved for first-order process. Single-objective population based methods such as genetic algorithm (GA) and particle swarm optimization (PSO) have only one solution in a single run. Unlike single objective methods, multi-objective optimization can find different solutions in a single run. In the proposed method, overshoot/undershoot and settling time are used as objective functions for multi-objective optimization. The proposed method is used for designing of PID parameters for two area interconnected power system.

Load Frequency Control in Interconnected Power System Using Multi-Objective PID Controller

Internet of Things (IoT) has the idea of receiving data and sending them for each object within the communication network. One of the main issues in this type of networks is handling a growing volume of data with various data sources and data types to satisfy the performance requirements of applications. In this regard, data management in the IoT plays an important role in its efficient operations and has become a major research topic. However, the data management has a crucial role in the IoT, there is not any comprehensive and systematic work to analyze its approaches. Thus, main propose of this paper is to systematically review and study the existing data management approaches in the IoT. The data management approaches are classified into three main classes, including SQL database, NoSQL database, and graph database. In addition, the detailed comparison of the important mechanisms in each category brings a recommendation for further works.

Arash Sharifi

Papers

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network.

Diagnosis of Autism Spectrum Disorders in Young Children Based on Resting-State Functional Magnetic Resonance Imaging Data Using Convolutional Neural Networks

Load Frequency Control in Interconnected Power System Using Multi-Objective PID Controller

The role of structured and unstructured data managing mechanisms in the Internet of things

Load frequency control in interconnected power system using multi-objective PID controller