Green Communication Infrastructure (GCI) Serving Different Smart City Applications

Aims: The aim of the Green Communication Infrastructure ‎‎(GCI) project is to understand the idea of a self ‎‎"sustainably" controlled correspondence foundation ‎fitting for smart city application fields. ‎ Background: This paper shows the endeavors to understand the idea of a ‎self "sustainably" energized communication foundation ‎fitting for smart city application fields. The recommended ‎Green Communication Infrastructure (CGI) comprises ‎different kinds of remote settled (or even versatile) hubs ‎performing diverse activities as per the application ‎requests. An imperative class of these hubs is the Wireless ‎Solar Router (WSR). Objective: The work in this venture was begun in 2009 with the aim ‎of demonstrating the essential advances that must be taken to ‎accomplish such framework and to proclaim the value of ‎embracing natural vitality assets in building mission ‎basic frameworks. Alternate destinations of this venture ‎are introducing a sensibly cost, solid, verified, and simple ‎to introduce correspondence foundation.‎ Method: The arrangement to actualize the GCI was accomplished ‎subsequent to passing two structure levels: device level and ‎system level. Result: The suggested system is highly applicable and serves a wide ‎range of smart city application fields and hence many ‎people and organizations can utilize this system. ‎ Conclusion: The presence of a reliable, secured, low cost, easy to install ‎and self-powered communication infrastructure is ‎mandatory in our nowadays. The communities in ‎developing countries or in rural areas need such a system ‎highly in order to communicate with other people in the ‎world which will affect positively their social and ‎economic situation.

Hierarchical and Multi-resolution Neuron Network Computing for Complex Inverse Problems-An Approach Based on Brain-inspired Computing and Learning Method

Background: Complex inverse problems such as Radar Imaging and CT/EIT imaging are well investigated in mathematical algorithms with various regularization methods. However it is difficult to obtain stable inverse solutions with fast convergence and high accuracy at the same time due to the ill-posed property and non-linear property. Objective: In this paper, we propose a hierarchical and multi-resolution scalable method from both algorithm perspective and hardware perspective to achieve fast and accurate solu-tions for inverse problems by taking radar and EIT imaging as examples. Method: We present an extension of discussion on neuromorphic computing as brain-inspired computing method and the learning/training algorithm to design a series of problem specific AI “brains” (with different memristive values) to solve a general complex ill-posed inverse problems that are traditionally solved by mathematical regular operators. We design a hierarchical and multi-resolution scalable method and an algorithm framework to train AI deep learning neuron network and map into the memristive circuit so that the memristive val-ues are optimally obtained. We propose FPGA as an emulation implementation for neuro-morphic circuit as well. Result: We compared the methodology between our approach and traditional regulariza-tion method. In particular we use Electrical Impedance Tomography (EIT) and Radar imaging as typical examples to compare how to design an AI deep learning neuron network architec-tures to solve inverse problems. Conclusion: With EIT imaging as a typical example, we show that any moderate complex inverse problem, as long as it can be described as combinational problem, AI deep learning neuron network is a practical alternative approach to try to solve the inverse problems with any given expected resolution accuracy, as long as the neuron network width is large enough and computational power is strong enough for all combination samples training purpose.

A Comprehensive Survey on Current Literature, Standards, ‎Applications and Projects of Self-Organizing Aerial Ad Hoc Network ‎‎(AANET) in Smart Cities

: In the last few decades, Information and Communication Technology (ICT) has been introduced which aims to bring more comfort to human life by integrating smartness into daily objects, yields to the idea of the smart city. Guaranteeing the well-being of residents and assessing industry and urban planning from an ecological and sustainable perspective are the main goals for the smart city. Great potentials are brought to the public and civil areas by the Aerial Ad Hoc Network (AANET) concept, especially in applications that are risky to human lives. AANET, like any emerging technology, comes with many challenges that have to be overcome to be employed efficiently. In this paper, we make a detailed survey on current literature, standards, and projects of self-organizing AANET in smart cities. Also, we intend to present a profound knowledge of this active research area by identifying features, design characteristics, architectures, routing protocols, and security aspects for the design and implementation of self-organizing AANET. Furthermore, we discuss existing solutions, indicate assessment metrics along with current applications, finally we highlight the main research scope for further developments. This article surveys the work done toward AANET-related outstanding issues, intending to encourage further research in this field.

Image Enhancement with Improved Global and Local Visual Effects

Background: In many applications of image processing, the enhancement of images is often a step necessary for their preprocessing. In general, for an enhanced image, the visual contrast as a whole and its refined local details are both crucial for achieving accurate results for subsequent classification or analysis. Objective: This paper proposes a new approach for image enhancement such that the global and local visual effects of an enhanced image can both be significantly improved. Methods: The approach utilizes the normalized incomplete Beta transform to map pixel intensities from an original image to its enhanced one. An objective function that consists of two parts is optimized to determine the parameters in the transform. One part of the objective function reflects the global visual effects in the enhanced image and the other one evaluates the enhanced visual effects on the most important local details in the original image. The optimization of the objective function is performed with an optimization technique based on the particle swarm optimization method. Results: Experimental results show that the approach is suitable for the automatic enhancement of images. Conclusion: The proposed approach can significantly improve both the global and visual contrasts of the image.

Role of Genetic Interactions in Lung Diseases Detection using Computational Approaches: A Review

: Genome-wide Association Studies (GWAS) give special insight into genetic differences and environmental influences that are part of different human disorders and provide prognostic help to increase the survival of patients. Lung diseases such as lung cancer, asthma, and tuberculosis are detected by analyzing Single Nucleotide Polymorphism (SNP) genetic variations. The key causes of lung-related diseases are genetic factors, environmental and social behaviors. The epistasis effects act as a blueprint for the researchers to observe the genetic variation associated with lung diseases. The manual examination of the enormous genetic interactions is complicated to detect the lungs syndromes for diagnosis of acute respiratory. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2006 and 2020. In this critical review, various computational approaches are extensively discussed in detecting respondent Epistasis effects for various lung diseases such as Asthma, Tuberculosis, lung cancer, and Nicotine drug dependence. The analysis shows that different computational models identified candidate genes such as CHRNA4, CHRNB2, BDNF, TAS2R16, TAS2R38, BRCA1, BRCA2, RAD21, IL4Ra, IL-13 and IL-1β, have important causes for genetic variants linked to pulmonary disease. These computational approaches' strengths and limitations are described. The issues behind the computational methods while identifying the lung diseases through epistasis effects and the parameters used by various researchers for their evaluation are presented.

Progressive Image Recognition Method and Its Application in Security In-spection Machines

Background:: Owing to the ability of a deep learning algorithm to identify objects and the related detection technology of security inspection equipment, in this paper, we propose a progressive object recognition method that con-siders local information of objects. Methods:: First, we construct an X-Base model by cascading multiple convolutions and pooling layers to obtain the feature mapping image. Moreover, we provide a “segmented convolution, unified recognition” strategy to detect the size of the objects. Results:: Experimental results show that this method can effectively identify the specifications of bags passing through the security inspection equipment. Compared with the traditional VGG and progressive VGG recognition methods, the pro-posed method achieves advantages in terms of efficiency and concurrency. Conclusion:: This study provides a method to gradually recognize objects and can potentially assist the operators to identify prohibited objects.

Can One Design a Series of Fast, Energy- Efficient “Brains” based on Neuromorphic Computing to Solve Complex Pattern Recognition Problem?

Objectives: In this paper, we present a theoretical discussion on neuromorphic computing circuit dynamic and its relations with AI deep learning neural networks. By elucidating their relations we can focus on the investigations of solving pattern classification and recognition problems in real applications from the perspectives of both logic computation view and physical circuit device views. The investigation is motivated by the design of a feasible fast and energy-efficient circuit device as well as an efficient training computation method to solve complex problems. Thus for this purpose we propose in this paper an approach to solve such problems. First, the neuromorphic computing as a new research area is introduced, including physical circuit properties, memristive device physical properties and the circuit dynamics described by the temporal and spatial (Maxwell) differential equations. Secondly, we show that by using AI deep learning neural networks to train AI neural networks we are able to derive the optimal AI neuron network weights. Last but not the least, we brief a mapping method in [1] and show in general how the neuromorphic circuit will work in practice after mapping the weights from AI deep learning neural networks into the neuromorphic circuit synapses/memristors. Conclusion: We also devote our discussions to the physical device feasibility and related matters. The method proposed in this paper is pragmatic and constructive.

Designing a Chat-Bot for College Information using Information Retrieval and Automatic Text Summarization Techniques

Background:: In the era of information overload it is very difficult for a human reader to make sense of the vast information available in the internet quickly. Even for a specific domain like college or university website it may be difficult for a user to browse through all the links to get the relevant answers quickly. Objective:: In this scenario, design of a chat-bot which can answer questions related to college information and compare between colleges will be very useful and novel. Methods:: In this paper a novel conversational interface chat-bot application with information retrieval and text summariza-tion skill is designed and implemented. Firstly this chat-bot has a simple dialog skill when it can understand the user query intent, it responds from the stored collection of answers. Secondly for unknown queries, this chat-bot can search the internet and then perform text summarization using advanced techniques of natural language processing (NLP) and text mining (TM). Results:: The advancement of NLP capability of information retrieval and text summarization using machine learning tech-niques of Latent Semantic Analysis(LSI), Latent Dirichlet Allocation (LDA), Word2Vec, Global Vector (GloVe) and Tex-tRank are reviewed and compared in this paper first before implementing them for the chat-bot design. This chat-bot im-proves user experience tremendously by getting answers to specific queries concisely which takes less time than to read the entire document. Students, parents and faculty can get the answers for variety of information like admission criteria, fees, course offerings, notice board, attendance, grades, placements, faculty profile, research papers and patents etc. more effi-ciently. Conclusion:: The purpose of this paper was to follow the advancement in NLP technologies and implement them in a novel application.