Multiple description coding (MDC) for video transmission in cognitive radio network systems

Over recent years, Cognitive Radio (CR) network has been extensively investigated to improve spectrum utilization and satisfy the demand of bandwidth for multimedia services such as video transmission. The size of video stream requires a large volume of network resource and becoming a challenging problem to maintain or improve the quality performance of video transmission. Multiple Description Coding (MDC) is one of the promising methods used to improve the error resilient in video transmission. Each description generated from MDC provides a low but acceptable video quality and could be enhanced into a higher quality if both are received at the receiver. The objective of this paper is to investigate the performance of video transmission in CR system using joint design of MDC method with H.264/AVC coding technique. The video performance was evaluated in three different channels; error free, random erroneous and CR channel. The simulation results show that the proposed MDC design improved the video quality performance by 3.33 % compared to the conventional Single Description Coding (SDC)

Smart parking systems by using Espresso Lite 2.0

There are more and more vehicles on the road in every country, which have to be parked in spaces that are becoming increasingly packed. This is a big challenge especially for city planners, architects, and building owners. To meet this demand, the innovative and space saving-parking system is created. This is the new way of technology that can be applied to all users and it is more efficient than the previous parking systems. All buildings including hospitals, government building, shopping complex can access and apply this technology. This paper proposed a program that combined with the iOS system and connected through wireless connection. In addition, it will be connected to the mobile phones using this technology. Before the data can be displayed, the mobile phone has to be installed with BLINK's application that can be downloaded from the apple store. After this application is installed, then apps will show the available parking slot in the specific parking area. Any parking spaces can be assessed anywhere and anytime as long as users have internet connection to their smartphones. This paper proposed to develop the iOS apps that can show and update the parking spaces that are available and the hardware is designed by using ESPresso Lite 2.0 board

Electrical appliances control system by using iOS

Today, Internet of Things technology is preferred compared to manual systems. With the rapid increases in the number of users of Internet over the past decade, this situation has made Internet a part and way of life. Internet of Things, IoT is the latest and emerging internet technology. Internet of Things is known as a growing network of everyday activities from industrial machine to consumer's goods that can share information and complete tasks while everyone is busy with other activities. Electrical appliances control by using IoT technology is a system that uses computers or mobile devices to control basic home functions and automatic features. It is meant to save the electric power and human energy to control electrical appliances operations. Electrical appliances control system by using IoT systems differs from other systems by allowing users to operate the systems from anywhere around the world through internet connection. In this paper, the project uses Blynk application that employs the integration of cloud networking and wireless communication by providing the user with remote control of various electrical appliances such as lights, fans, and air conditioner in the house

Reconfigurable Enhanced Path Metric Updater Unit for Space Time Trellis Code Viterbi Decoder

Space Time Trellis Code (STTC) encoding and decoding techniques are effective for delivery of a reliable information because of the signal to noise ratio is very small. Even though the Viterbi algorithm is complicated to be designed, these methods typically used large memory space to store the information that have been processed mainly at the Path Metric Updater (PMU). Therefore, an effective memory management technique is one of the key factors in designing the STTC Viterbi decoder for low power consumption applications. This paper proposed the PMU memory reduction technique especially on Traceback activities that usually required a lot of memories for storing the data that has been processed in the past part by using Altera Quartus 2 and 0.18 µm Altera CPLD 5M570ZF256C5 as targeted hardware. Through this method, the reduction achieved at least 66% of memory requirements and 75% improvements in processing time without a significanct effects on the outputs results of the STTC Viterbi Decoder for 4-PSK modulation technique by using 50MHz clocks

Self Organising Cognitive Radio Networks

This thesis investigates the application of learning and cognition to self organisation of ad hoc and green small cell networks in order to improve performance in terms of throughput, delay and the network energy consumption to achieve ‘green communication’. Initially, an attempt is made to improve the spatial re-use of the network by dividing it into disjoint sets of nodes through a clustering process. A novel distributed clustering algorithm is developed that exploit cognitive radio based principles in that they have the ability to learn from received signal strength indicator (RSSI) beacons, to form clusters which reduce the average distance between nodes, as well as reducing the level of overlap between clusters. By making nodes repeatedly learn about their environment through RSSI, nodes effectively compete to become a cluster head, with the winning nodes being those that are located in an area of locally high node density. It is demonstrated that the resulting cluster formation through repeated learning is better than with no learning and node degree. The benefit of applying a hierarchical architecture to ad hoc and green small cell networks via two-hop backhauling is examined with respect to its energy efficiency. Energy efficiency is investigated in terms of the energy consumption ratio (ECR) and the energy reduction gain (ERG). The results are compared to that of a traditional single hop architecture with no hierarchical formation. It is shown that under certain conditions, dual hop clustered networks can potentially be more energy efficient that single hop transmission, but care needs to be taken to ensure that the backhaul links within the network do not become bottlenecks at high offered traffic levels. The application of directional antennas at a Hub Base Station significantly helps to reduce the total energy consumption of the network as well the backhaul connectivity of a dual-hop clustered network. Introducing Reinforcement Learning to channel assignment on the first hop reduces end to end delay and thus minimises the amount of time and energy for the nodes in the network to be in transmission or reception mode. The reinforcement learning schemes can exploit the spectrum in which it perceives as a good option based upon individual channel historical information and thereby further improve the network spatial re-use

A Study on the Impact of Nodes Density on the Energy Consumption of LoRa

LoRaWAN is one of the leading Low power wide area network (LPWAN) LPWAN technologies that compete for the formation of big scale Internet of Things (IoT). It uses LoRa protocol to achieve long range, low bit rate and low power communication. Large scale LoRaWAN based IoT deployments can consist of battery powered sensor nodes. Therefore, the energy consumption and efficiency of these nodes are crucial factors that can influence the lifetime of the network. However, there is no coherent experimental based research which identifies the factors that influence the LoRa energy efficiency at various nodes density. In this paper, results on measuring the packet delivery ratio, packet loss, data rate and energy consumption ratio ECR to gauge the energy efficiency of LoRa devices at various nodes density are presented. It is shown that the ECR of LoRa is inversely proportional to the nodes density and that the ECR of the network is smaller at higher traffic indicating better network energy efficiency. It is also demonstrated that at high node density, spreading factor SF of 7 and 9 can improve the energy efficiency of the network by 5 and 3 times, respectively, compare to SF 11

Comparison of convolutional neural network architectures for face mask detection

In 2020 World Health Organization (WHO) has declared that the Coronaviruses (COVID-19) pandemic is causing a worldwide health disaster. One of the most effective protections for reducing the spread of COVID-19 is by wearing a face mask in densely and close populated areas. In various countries, it has become mandatory to wear a face mask in public areas. The process of monitoring large numbers of individuals to comply with the new rule can be a challenging task. A costeffective method to monitor a large number of individuals to comply with this new law is through computer vision and Convolution Neural Network (CNN). This paper demonstrates the application of transfer learning on pre-trained CNN architectures namely; AlexNet, GoogleNet ResNet-18, ResNet-50, ResNet-101, to classify whether or not a person in the image is wearing a facemask. The number of training images are varied in order to compare the performance of these networks. It is found that AlexNet performed the worst and requires 400 training images to achieve Specificity, Accuracy, Precision, and F-score of more than 95%. Whereas, GoogleNet and Resnet can achieve the same level of performance with 10 times fewer number of training images

Development of blood oxygen level, heart rate and temperature monitoring system by using ESP32

Telehealth plays an even greater role in global healthcare delivery today. Ever since the pandemic strikes the world, the use of healthcare monitoring systems has a massive boost significantly. This paper aims to develop a prototype of a health monitoring device that can be put at a patient home to replace traditional physical checkups at the hospital by using MAX30102 PPG module and Infrared temperature sensor with the objective to derive PPG signal into basic health parameters such as heart rate and oxygen saturation from the patient through MAX30102 and to test the accuracy of those parameters with hospital patient monitor. The experiment is done by comparing each parameter for 30 seconds in both devices using index fingers coming from 13 participants. From the results obtained, although heart rate suffers motion artefact compared to the oxygen saturation, each accuracy is still excellent with 89.2 and 97.6 percent. In conclusion, MAX30102 PPG and ECG module can get initial health reading through the PPG signal and solve the problems mentioned

Reconfigurable enhanced path metric updater unit for Space Time Trellis Code Viterbi decoder

Space Time Trellis Code (STTC) encoding and decoding techniques are effective for delivery of a reliable information because of the signal to noise ratio is very small. Even though the Viterbi algorithm is complicated to be designed, these methods typically used large memory space to store the information that have been processed mainly at the Path Metric Updater (PMU). Therefore, an effective memory management technique is one of the key factors in designing the STTC Viterbi decoder for low power consumption applications. This paper proposed the PMU memory reduction technique especially on Traceback activities that usually required a lot of memories for storing the data that has been processed in the past part by using Altera Quartus 2 and 0.18 µm Altera CPLD 5M570ZF256C5 as targeted hardware. Through this method, the reduction achieved at least 66% of memory requirements and 75% improvements in processing time without a significant effects on the outputs results of the STTC Viterbi Decoder for 4-PSK modulation technique by using 50MHz clocks