Iraqi Journal of Information and communication technology

This paper investigates the use of LDA algorithm In the EEG classification. EEG feature extraction is Implemented to reduce the dimensionality of data. The Sliding Window Technique is used to calculate the mean within each window samples. Then, classification is done based on hyperplane technique. The LDA algorithm is described in details with all the implementation Issues. The LDA regularization is also discussed and its effects on the classification accuracy is given. In addition, both window size and channel selection effect on the accuracy is Illustrated. Results show that a window size of 150 samples, channel 3 and regularization parameter of 0.9 gives an accuracy of 90%

As new ideas and perspectives continue to be shared by various authors, many different aspects of WSN technology have been generated according to the pivotal role of WSN for many fields in our lives. This technology contains different components, the most basic one is the sensor nodes that communicate with each other and deliver the data. Routing protocols make the sensors’ communication and delivering of data thoughtfully planned to be more reliable. Routing protocols are a wide field given the importance of their presence in the network to route the data to the best path or shortest path as much as possible among sensor nodes. This paper focus on cluster-based routing protocols that have hierarchal architecture, this class of protocols has gone through a lot of developments and improvements because of the great benefits that offer to WSN. Part of these improvements that applied to IEEPB protocol (Improved Energy- Efficient PEGASIS- Based protocol) which is present in this paper is enhancing the protocol work mechanism using the Ant colony optimization technique (ACO) to conserve wasted energy and thus increase the network lifetime. Therefore, this paper proposes a SIEEPB- ACO (Single IEEPB - ACO) protocol that is an improved protocol over the IEEPB protocol using ACO. The protocol is simulated using the MatlabR2018b simulator which provides clear and effective simulation results, especially for energy saving. Therefore, due to the simulation results, SIEEPB-ACO provides a noticeable improvement compared to the PEGASIS and IEEPB protocols

Cognitive Internet of Things (CIoT) is the next leap towards enhancing the accuracy and effectiveness of Internet of Things (IoT) technology used in combination with cognitive computing that has a significant role in healthcare and diseases diagnosis. The work in this paper proposes a system to diagnose the sensitivity toward sound and light by developing a mechanism based on facial emotions recognition system that integrate with IoT protocols and cloud computing. The proposed system has been achieved by establishing a cognitive IoT environment composed of hardware and software components that practically implemented in a laboratory to identify the behavior of people suffering from sensitivity toward sound and light. This behavior has been observed by monitoring human face emotions through a live video capturing using camera and image processing using facial emotion recognition software. Emotions values obtained has been examined and collected in a cloud using Message Queuing TelemetryTransport (MQTT) protocol which is an IoT application layer protocol. These emotions were classified as normal and abnormal. Normal state represented by happy or natural emotions give the impression that the surrounding environment is appropriate for people senses and they do not suffer from any discomfort toward this environment ,therefore the system works on a mechanism using a microcontroller to increase the intensities of sound or light in this environment. While in case of detection hypersensitivity, the system automatically decreases these intensities. The project aims to be an important platform in the diagnosis for medical and social cases that suffer from a disturbance towards the surrounding environment

Cryptographic algorithm is a tool that is used to secure the transmitted data on the network. The current standard algorithm the Advanced Encryption Standard (AES) is used to maintain the security and reliability of the encrypted data whether these data are stored in computer or in transmit. AES can be implemented either in hardware or software, however hardware implementation is more sensible for high speed applications. In this paper, AES- 128algorithm is implemented in hardware in order to achieve a high-speed data processing. It is implemented on an FPGA platform using HLL language and Xilinx ISE software. The design is effectively optimized and Synthesizable with high accuracy using the conventional blocks of Xilinx System Generator. The results of implementation have enhanced the performance in terms of resource utilization, speed and power consumption as compared with other related works. The circuit operates at a maximum frequency of 800.000 MHz which offers a high throughput of102.4 Gbps on virtex6 xc6vlx130t- 3ff1156, in addition it occupies only 2,405 slices