“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

The Internet of Things (IoT) is set to become one of the key technological developments of our times provided we are able to realize its full potential. The number of objects connected to IoT is expected to reach 50 billion by 2020 due to the massive influx of diverse objects emerging progressively. IoT, hence, is expected to be a major producer of big data. Sharing and collaboration of data and other resources would be the key for enabling sustainable ubiquitous environments, such as smart cities and societies. A timely fusion and analysis of big data, acquired from IoT and other sources, to enable highly efficient, reliable, and accurate decision making and management of ubiquitous environments would be a grand future challenge. Computational intelligence would play a key role in this challenge. A number of surveys exist on data fusion. However, these are mainly focused on specific application areas or classifications. The aim of this paper is to review literature on data fusion for IoT with a particular focus on mathematical methods (including probabilistic methods, artificial intelligence, and theory of belief) and specific IoT environments (distributed, heterogeneous, nonlinear, and object tracking environments). The opportunities and challenges for each of the mathematical methods and environments are given. Future developments, including emerging areas that would intrinsically benefit from data fusion and IoT, autonomous vehicles, deep learning for data fusion, and smart cities, are discussed.

Data Fusion and IoT for Smart Ubiquitous Environments: A Survey

This article consists of a collection of slides from the author's conference presentation. Presents an overview of Mataio, a company that provides products and services in the field of augmented reality and computer vision. Some of the specific topics discussed include: corporate structure, product launchings, markets served, and major areas of technnological innovation in the field.

Augmented reality

The mobile Nodes in wireless Ad hoc networks are fed by batteries,so the energy limitation has become a performance bottleneck for mobile ad hoc networks.IEEE 802.11 has been used as the current standard MAC protocol for ad hoc networks.However,it has not the ability of adjusting power level dynamically,which reduces the performance of network.Power control can enhance the power efficiency of nodes,reduce the interferer of neighbor nodes and improve the performance of network.A novel power controlled MAC protocol based on SNR in mobile Ad hoc networks is presented in this paper.Simulation results demonstrate that compared to the IEEE 802.11 MAC protocol,the proposed protocol can decrease the power consumption greatly,and improve the energy utilization of mobile terminals while maintaining the throughput performance.

A Power Control MAC Protocol for Ad hoc Networks

In this paper we describe the mobile health (m-health) system in the context of Internet of Things (IoT). We describe the fundamental characteristics of m-health devices such as compactness, IP connectivity, low-power consumption and security. We discuss acquisition of mobile health data via medical gadgets and wearables and application of this data in monitoring various health conditions such as blood sugar level, ECG, blood-pressure, asthma, etc. Security is very critical for IoT based m-health system. We address the issues of confidentiality, privacy, and security in the context of secure m-health system. We listed several measures to protect the information of patients and m-health system. The m-health system will benefit the patients in many ways such as quick diagnosis, remote monitoring and home rehabilitation. Overall m-health system will significantly reduces healthcare cost and unnecessary hospitalizations.

Mobile Health (m-Health) System in the Context of IoT

Safe From Fire (SFF) is an intelligent self controlled smart fire extinguisher system assembled with multiple sensors, actuators and operated by micro-controller unit (MCU). It takes input signals from various sensors placed in different position of the monitored area, and combines integrated fuzzy logic to identify fire breakout locations and severity. Data fusion algorithm facilitates the system to discard deceptive fire situations such as: cigarette smoke, welding etc. During the fire hazard SFF notifies the fire service and others by text messages and telephone calls. Along with ringing fire alarm it announces the fire affected locations and severity. To prevent fire from spreading it breaks electric circuits of the affected area, releases the extinguishing gas pointing to the exact fire locations. This paper presents how this system is built, components, and connection diagram and implementation logic. Overall performance is evaluated through experimental tests by creating real time fire hazard prototype scenarios to investigate reliability. It is observed that SFF system demonstrated its efficiency most of the cases perfectly.

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An Intelligent Fire Detection and Mitigation System Safe from Fire (SFF)

A method and apparatus for monitoring heart rate of the heart using a wearable system is designed and implemented in this paper A heart rate receives from heart beat signals and stores the data to a database and after a time period this method can determine an idle heart rate of the monitoring body This idle heart rate is compared with the stored data and can determine the normal and abnormal heart rate variability After the certain time period this system can detect the heart rate and also can send a signal to the user in time of abnormalities Consequent estimations of heart rate variability are contrasted with this

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An Intelligent Sensor Based System for Real Time Heart Rate Monitoring (HRM)

In this paper, the performance parameters of a small printed quasi-self-complementary ultra wideband (UWB) antenna in close proximity to the human body is investigated. The return loss response, impedance, radiation pattern and gain are investigated. The antenna shows good on-body performance, and therefore it will be suitable for on-body applications in UWB wireless body area networks.

Study of a small printed quasi-self-complementary ultra wideband antenna for on-body applications

Generative Adversarial Networks (GAN) have been remarkable at generating artificial data, especially realistic looking images. This learning framework has proven itself to be effective in synthetic image generation, semantic image hole filling, semantic image editing, style transfer and many more. On the other hand, variational auto-encoders (VAE) have also been quite effective, so much so that mathematically it is often more accurate at generating images resembling to its original dataset. Nevertheless, images generated by VAE suffer from blurriness and are generally less realistic looking from human perception. In this paper we take a broad view on both systems and propose a theoretical approach to combine them and bring out the best of both.

A Comparative Study on Variational Autoencoders and Generative Adversarial Networks

In these days people tend to check reviews and ratings of video games before spending money and time for a game. This paper proposes a new model of sentiment analysis for video game’s reviews. In the proposed model, video game’s rating will be generated by doing sentiment analysis on public opinion data from Micro-blog site, Twitter. To segregate users’ sentiment Naive Bayes, Support Vector Machine, Logistic Regression and Stochastic Gradient Descent machine learning algorithms were used. This algorithms themselves were trained and tested on the Amazon game review dataset before doing sentiment analysis on Twitter Data. Furthermore, customized classifiers model was implemented which acted as voting classifiers to determine contextual polarity. This voting classifier takes results of other algorithms into account and select the best one which can obtain the most number of votes. Before implementing the classifiers, data pre pro-processing had been performed for accurate sentiment analysis. This process ensures higher accuracy for generating review ratings and distinguishing users’ sentiment. Finally, algorithm’s accuracy results are analyzed in vivid details. Analysis showed that our proposed technique outperforms others.

Iftekharul Mobin

Papers

An Intelligent Fire Detection and Mitigation System Safe from Fire (SFF)

An Intelligent Sensor Based System for Real Time Heart Rate Monitoring (HRM)

Study of a small printed quasi-self-complementary ultra wideband antenna for on-body applications

A Comparative Study on Variational Autoencoders and Generative Adversarial Networks

Rating Generation of Video Games using Sentiment Analysis and Contextual Polarity from Microblog