<span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">Autism Spectrum Disorder (ASD) is a neurodevelopmental that impact the social interaction and communication skills. Diagnosis of ASD is one of the difficult problems facing researchers. This research work aimed to reveal the different pattern between autistic and normal children via electroencephalogram (EEG) by using the deep learning algorithm. The brain signal database used pattern recognition where the extracted features will undergo the multilayer perceptron network for the classification process. The promising method to perform the classification is through a deep learning algorithm, which is currently a well-known and superior method in the pattern recognition field. The performance measure for the classification would be the accuracy. The higher percentage means the more effectiveness for the ASD diagnosis. </span><span style="color: black; font-family: 'Times New Roman',serif; font-size: 9pt; mso-fareast-font-family: 'Times New Roman+FPEF'; mso-themecolor: text1; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">This can be seen as the ground work for applying a new algorithm for further development diagnosis of autism to see how the treatment is working as well in future.</span>

-Traffic congestion problem is a phenomena which contributed huge impact to the transportation system in country. This causes many problems especially when there are emergency cases at traffic light intersections which are always busy with many vehicles. A traffic light controller system is designed in order to solve these problems. This system was designed to be operated when it received signal from emergency vehicles based on radio frequency (RF) transmission and used the Programmable Integrated Circuit (PIC) 16F877A microcontroller to change the sequence back to the normal sequence before the emergency mode was triggered. This system will reduce accidents which often happen at the traffic light intersections because of other vehicle had to huddle for given a special route to emergency vehicle. As the result, this project successful analyzing and implementing the wireless communication; the radio frequency (RF) transmission in the traffic light control system for emergency vehicles. The prototype of this project is using the frequency of 434 MHz and function with the sequence mode of traffic light when emergency vehicles passing by an intersection and changing the sequence back to the normal sequence before the emergency mode was triggered. In future, this prototype system can be improved by controlling the real traffic situation, in fact improving present traffic light system technology.

An improvement on redundancy to achieve high compression ratio in video coding is developed. Block Matching Motion Estimation (BMME) techniques have been particularly used in various coding standards. In the BMME, search patterns with different shapes or sizes and the center-biased characteristics of motion vector (MV) have large impact on the search speed (search points) and peak signal-to-noise ratio (PSNR) as the quality of video images. These basic algorithms are Full Search and other two fast search methods. The Cross Diamond Search (CDS) algorithm was designed to fit the cross-center-biased (CCB) MV distribution characteristics of the real-world video sequences. CDS compares favorably with the other algorithms for low motion sequences in terms of speed, quality and computational complexity.

No abstract

No abstract

Autism Spectrum Disorder (ASD) is categorized as a neurodevelopmental disability. Having an automated technology system to classify the ASD trait would have a huge influence on paediatricians, which can aid them in diagnosing ASD in children using a quantifiable method. A novel autism diagnosis method based on a bidirectional long-short-term-memory (LSTM) network's deep learning algorithm is proposed. This multi-layered architecture merges two LSTM blocks with the other direction of propagation to classify the output state on the brain signal data from an electroencephalogram (EEG) on individuals; normal and autism obtained from the Simon Foundation Autism Research Initiative (SFARI) database. The accuracy of 99.6% obtained for 90:10 train:test data distribution, while the accuracy of 97.3% was achieved for 70:30 distribution. The result shows that the proposed approach had better autism classification with upgraded efficiency compared to single LSTM network method and potentially giving a significant contribution in neuroscience research.

The design of a smart green environment of home automation for appliances monitoring systems is developed based on an IoT(Internet of Things) application. The smart home concept represents a motivating platform for innovation of information technology services to produce more operative house devices and system that can improve the standard of life. This project aims at controlling home appliances via Smartphone using Wi-Fi as a communication protocol and the Raspberry Pi as a server system. The user here can move directly with the system through a web-based interface over the net. The designed system not solely monitors the sensor data but also actuates a process according to the need. Therefore, globally accessible automation of electronic appliances is created attainable with the utilization of a Raspberry Pi micro-controller board, a web affiliation and relay switches in a user-friendly way for the users to regulate home electronic appliances with high flexibility and security.

Garbage management is one of the primary problem faced by cleaners in terms of the duration of the cleaning process. The design of a smart green environment of garbage monitoring systems is developed based on an IoT (Internet of Things) application is believed to overcome this waste issue. The main contribution of this project is demonstrated by a system based on IoT that allows the waste management to monitor based on the garbage depth inside the dustbin and also the notification of its full condition by using a mobile phone and the Blynk apps. The proposed system consisted of the ultrasonic sensor, which measures the garbage level inside the dustbin. The system shows the status of the garbage through LCD, and a WiFi module (ESP8266) used to send the information to the smartphone. Thus, it is expected that this system can build a greener environment by monitoring and controlling the collection of garbage smartly through an IoT application.