The immense increase in multimedia-on-demand traffic that refers to audio, video, and images, has drastically shifted the vision of the Internet of Things (IoT) from scalar to Multimedia Internet of Things (M-IoT). IoT devices are constrained in terms of energy, computing, size, and storage memory. Delay-sensitive and bandwidth-hungry multimedia applications over constrained IoT networks require revision of IoT architecture for M-IoT. This paper provides a comprehensive survey of M-IoT with an emphasis on architecture, protocols, and applications. This article starts by providing a horizontal overview of the IoT. Then, we discuss the issues considering the characteristics of multimedia and provide a summary of related M-IoT architectures. Various multimedia applications supported by IoT are surveyed, and numerous use cases related to road traffic management, security, industry, and health are illustrated to show how different M-IoT applications are revolutionizing human life. We explore the importance of Quality-of-Experience (QoE) and Quality-of-Service (QoS) for multimedia transmission over IoT. Moreover, we explore the limitations of IoT for multimedia computing and present the relationship between the M-IoT and emerging technologies including event processing, feature extraction, cloud computing, Fog/Edge computing and Software-Defined-Networks (SDNs). We also present the need for better routing and Physical-Medium Access Control (PHY-MAC) protocols for M-IoT. Finally, we present a detailed discussion on the open research issues and several potential research areas related to emerging multimedia communication in IoT.

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Multimedia Internet of Things: A Comprehensive Survey

A person's behavior significantly influences their health and well-being. It also contributes to the social environment in which humans interact, with cascading impacts to the health and behaviors of others. During social interactions, our understanding and awareness of vital nonverbal messages expressing beliefs, emotions, and intentions can be obstructed by a variety of factors including greatly flawed self-awareness. For these reasons, human behavior is a very important topic to study using the most advanced technology. Moreover, technology offers a breakthrough opportunity to improve people's social awareness and self-awareness through machine-enhanced recognition and interpretation of human behaviors. This paper reviews (1) the social psychology theories that have established the framework to study human behaviors and their manifestations during social interactions and (2) the technologies that have contributed to the monitoring of human behaviors. State-of-the-art in sensors, signal features, and computational models are categorized, summarized, and evaluated from a comprehensive transdisciplinary perspective. This review focuses on assessing technologies most suitable for real-time monitoring while highlighting their challenges and opportunities in near-future applications. Although social behavior monitoring has been highly reported in psychology and engineering literature, this paper uniquely aims to serve as a disciplinary convergence bridge and a guide for engineers capable of bringing new technologies to bear against the current challenges in real-time human behavior monitoring.

Review and Challenges of Technologies for Real-Time Human Behavior Monitoring

Existing methods for learning-style recognition are highly subjective and difficult to implement. Therefore, the present study aimed to develop a learning-style recognition mechanism based on EEG features. The process for the mechanism included labeling learners’ actual learning styles, designing a method to effectively stimulate different learners’ internal state differences regarding learning styles, designing the data-collection method, designing the preprocessing procedure, and constructing the recognition model. In this way, we designed and verified an experimental method that can effectively stimulate learning-style differences in the information-processing dimension. In addition, we verified the effectiveness of using EEG signals to recognize learning style. The recognition accuracy of the learning-style processing dimension was 71.2%. This result is highly significant for the further exploration of using EEG signals for effective learning-style recognition.

https://www.mdpi.com/2076-3425/11/5/613/pdf

Design and Implementation of an EEG-Based Learning-Style Recognition Mechanism

In this article,the support vector machine(SVM) is applied to face recognition is discussedThis paper uses SVM method to study face recognition,which is a typical problem of multi-classificationOnly one SVM is built to solve this problem,thus overcomes some flaws of several traditional multi-classification methods of SVMSVM can solve the over-study problem effectively,and it has the good promoted performance and better classified accuracyThis article also discusses the primary processing which the SVM is applied to face recognition

Face Recognition Based on Support Vector Machines

PLTGU is a gas and steam power plant, producing electrical energy of 3x526 MW. The PLTGU generation process has several supporting components, one of which is a three-phase induction motor. It requires a balanced three-phase supply voltage in its operation. The problem that frequently occurs is unbalanced voltage supply, causing an imbalance of current going to the motor stator, if not directly corrected. It causes a high temperature on the motor, possible to cause a fire. Based on the problems, a system for monitoring the protection of steam pump motorcycles due to voltage unbalance based on the Internet of Things is designed.  It used a 1-phase AC Adapter voltage sensor, a current sensor SCT-013, an Arduino Uno as the processor, and a web to display the system. The test results showed that the sensor could work well. It could read voltage with an error percentage not exceeding 1%. The percentage error of the current sensor SCT-013 was 5.15%, but the value difference between the sensor and the Avo meter was 0.01. The system was displayed on the web to make to monitor the motor condition more easily

/pdf/internet-of-things-based-steam-pump-motor-protection-due-to-3a3nbtygtg.pdf

Internet of Things-Based Steam Pump Motor Protection Due to Voltage Unbalance

Emotions play a major role in human decision making, perception, interaction & cognition. Visually human facial expressions can give us a lot of information, which plays an important role in the area of human-machine interaction. The main challenging task is recognizing the facial reaction of a person by the system. Face Recognition are carried out in three different stages: 1. face detection, 2.feature extraction, 3.expression classification. The main objective of this research methodology is to carry out emotion analysis by implementing and evaluating various research issues to obtain a high classification rate. Various researchers have implemented various algorithms for identifying various human emotions. This paper presents a review of the current work carried out in the field of human facial expression and EEG techniques. Pre-determination of disease identification can be done using this technique.

A review of EEG based human facial expression recognition systems in cognitive sciences

Analysis of combustion quality of flame images of thermal and gas turbine power plants is of great importance. In the domain of image processing the detection, recognition and understanding is the foundation for identifying the combustion condition. Soft sensors are the state of the art. So the flame temperature based on subsequent combustion quality estimation is done using Back Propagation Algorithm (BPA) and Ant Colony Optimization (ACO). The basic idea utilizes the colour information from the flame images. The first step is to define a feature vector. The 9 feature elements, from the samples of 51 flame images are used to train and test the model. Experiments prove this method to be effective. The classification of flame images based on combustion quality is dependent on the flame temperature and colour. The solution includes the Internet of Things (IoT). The intelligent sensors are embedded in the computing system to monitor the combustion quality and flame temperature. This flexible and dispensable form of environment needs continuous monitoring, controlling and behavior analysis in power plants. The prototype implementation consists of Arduino UNO board, intelligent sensors with Arduino hardware support package. The implementation is tested for monitoring the combustion quality and its subsequent flame temperature to provide a feed control for combustion quality monitoring and to make the environment smart.

Internet of things for flame monitoring power station boilers

In large amount applications appreciate Finite Impulse Response filters, Fast Fourier Transform, Signal Processing Technique and measurements, forced upon ∼45dB or ultimately less side lobe magnitudes. However, the setback is prompt in window based FIR filter design lies in its side lobe magnitude that are higher than the specification of application. The expected hybrid window Technique has transcend attitude like minimum side lobe peak and higher side lobe peak attenuation compared to the several generally used windows with main lobe width(−3dB) of 0.11328. The proposed window has reliant side lobe attenuation of −72.7 dB and peak side lobe amplitude of −98 dB for the filter term of N=35. The studied results also unmask significant performance upgrading of the coming window compared to Rectangular, Hanning, Hamming, Blackman and Kaiser Windows. Finally, the approaching window is applied to design Low pass FIR filter prove the simplicity of the window

A hybrid window function to design finite impulse response low pass filter with an improved frequency response

Combustion quality in power stations plays an important role in minimizing the flue gas emissions. Complete combustion occurs when all the energy in the fuel being burnt is extracted and none of the carbon and hydrogen compounds are left unburnt. Complete combustion will occur when proper amounts of fuel and air are mixed in correct proportion under the appropriate conditions of temperature. The Monitoring process is accomplished by capturing the image of the flame with a camera and processing the image in a laptop with MATLAB code. Estimation of Sulphur Dioxide (SO2) emissions from combustion images in thermic and gas turbine control plants is of immense significance in the field of image processing and the prime aim is the presentation, distinguishing proof and understanding of the start conditions. In this effort, virtual sensor utilizing single layer perceptron up skilled by Back Propagation Algorithm (BPA) and Ant Colony Optimization (ACO) for blaze examination. The arrangement incorporates the Industrial Internet of Things (IIoT) where the intelligent sensors form the embedded computing system to screen the changeability in parameters relating to the flame colour.

Monitoring of SO 2 emissions in power plants using Internet of Things

Railways are the most convenient mode of transport, but safety precaution is lagging. Train accidents due to an unknown person operating the engine will lead to the end of many lives and also loss of railway property. The golden solution to meet this problem here the proposed effective system is ‘Automation of Railway Engine Pilot Security System using Multimodal Biometrics Identification’ (AREPSS using MBI). Iris and fingerprint inputs are given by engine pilot from cabin to control room. In control room, identification takes place by fusing inputs, then passing the decision signal to automatically start the engine. It is the most commonly used unimodal biometric system, which can be seen in most of the places due to its popularity. Its reliability has decreased because it requires larger memory footprint and higher operational cost and it has slower processing speed. So, we are introducing Multimodal Biometric Identification System which uses iris and fingerprint for security purpose. The major advantage of this multimodal analysis is based on the template-matching phenomenon which utilizes less memory for storage as compared with footprint. User corroboration by multiple modality methods yields high output, high reliability, and high accuracy. So this technique enhances security in engine and thus saves lives and property.

T. Kalpalatha Reddy

Papers

A review of EEG based human facial expression recognition systems in cognitive sciences

Internet of things for flame monitoring power station boilers

A hybrid window function to design finite impulse response low pass filter with an improved frequency response

Monitoring of SO 2 emissions in power plants using Internet of Things

Automation of Railway Engine Pilot Security System Using Multimodal Biometric Identification