A Review of Autism Spectrum Disorder Diagnostic Tools

This is an overview of diagnostic tools used in Malaysia. In this analysis, the benefits and disadvantages of each autism spectrum disorder diagnosis diagnostic technique are discussed. Pediatrists and psychiatric professionals who are focused on linguistic delay, brain function, or behavioural problems such as aggression, tantrum, etc. have currently noted the extent of impairments. The defects are assessed using multiple diagnostic tools. This review contrasts the seven forms of testing devices, including the Autism Diagnostic Observation Schedule (ADOS), Autism Diagnostic Interview-Revised (ADI-R), Childhood Autism Rating Scale (CARS), Gillian Autism Rating Scale (GARS), Diagnostic Interview for Social and Communication Disorder (DISCO), Developmental, Dimensional & Diagnostic Interview (3DI), and Diagnostic and Statistical Manual of Mental Disorder (DSM). The advantages and limitations of each tool are discussed in detail

A Review of Autism Spectrum Disorder Diagnostic Tools

This is an overview of diagnostic tools used in Malaysia. In this analysis, the benefits and disadvantages of each autism spectrum disorder diagnosis diagnostic technique are discussed. Pediatrists and psychiatric professionals who are focused on linguistic delay, brain function, or behavioural problems such as aggression, tantrum, etc. have currently noted the extent of impairments. The defects are assessed using multiple diagnostic tools. This review contrasts the seven forms of testing devices, including the Autism Diagnostic Observation Schedule (ADOS), Autism Diagnostic Interview-Revised (ADI-R), Childhood Autism Rating Scale (CARS), Gillian Autism Rating Scale (GARS), Diagnostic Interview for Social and Communication Disorder (DISCO), Developmental, Dimensional & Diagnostic Interview (3DI), and Diagnostic and Statistical Manual of Mental Disorder (DSM). The advantages and limitations of each tool are discussed in detail

Adaptive Impedance Tuning Network using Genetic Algorithm: ITuneGA

Adaptive impedance tuning algorithms are used to preserve the link quality of mobile phones under fluctuating user conditions. It is highly desirable to correct the complex impedance mismatch with high convergence rate. Presented here, is a novel technique for correcting impedance mismatch in adaptive impedance tuning network by exploiting the relationships among the genetic algorithm’s coefficient values derived from the matching network parameters. Simulation results demonstrate that the proposed impedance tunable algorithm (ITuneGA) outperforms conventional GA and LMS, with its fast convergence speed and high accuracy. The robustness of ITuneGA has been verified by using Pi-networks with two and four tuning elements. ITuneGA corrects antenna impedance mismatches and reduces the reflected power, thereby significantly improving the quality of the signal

An Improved Retraining Scheme for Convolutional Neural Network

A feed-forward neural network artificial model, or multilayer perceptron (MLP), learns input samples adaptively and solves non-linear problems for data that are noisy and imprecise. Another variant of MLP, known as Convolutional Neural Network (CNN) has additional features such as weight sharing, local receptive field, and subsampling, making CNN superior in handling challenging pattern-recognition tasks. Although CNN has improved the performance of MLP, the complexity of its structure has caused retraining processes to become inefficient whenever new categories or neurons using a winner-takes-all approach are added at the classifier stage. Thus, it is necessary to retrain the complete network set when new categories are added to the network. However, such a retraining incurs additional cost and training time. In this paper, we propose a retraining scheme that could overcome the mentioned problem. The proposed retraining scheme generalizes the feature of extraction layers, hence the retraining process only involves the last two layers instead of the whole network. The design was evaluated on AT&T and JAFFE databases. The results obtained have proved that training an additional category is approximately more than 70 times faster than retraining the whole network architectur

Convolutional Neural Network for Object Detection System for Blind People

Blind or visually impaired people are usually unaware of the danger that they are facing in their daily life. They may face many challenges when performing their daily activity even in familiar environments. This work proposed a smart object detection system based on Convolutional Neural Network (CNN) to provide a smart and safe living for visually impaired people. To reduce the complexity load, region proposals from the edge maps of each image were produced using edge box algorithm. Then, the proposals passed through a fine-tuned CaffeNet model. The object scene was captured by the webcam in real time and the feature of the image was extracted. After that, audio-based detector was generated on the detected object to notify the visually impaired people about the identified object. The result was evaluated by using mean average precision (mAP) and frame-per-second and it was found that the Single Shot MultiBox Detector (SSD) reduces the complexity and achieves higher accuracy as well as faster speed in object detection compared to Fast R-CNN

Automatic Brain Lesion Detection And Classification Based On Diffusion-Weighted Imaging Using Adaptive Thresholding And A Rule-Based Classifier

In this paper,a brain lesion detection and classification approach using thresholding and a rule-based classifier is proposed.Four types of brain lesions based on diffusion-weighted imaging i.e. acute stroke,solid tumor, chronic stroke,and necrosis are analyzed.The analysis is divided into four stages:pre-processing,segmentation, feature extraction,and classification.In the detection and segmentation stage,the image is divided into 8x8 macro-block regions.Adaptive thresholding technique is applied to segment the lesion’s region.Statistical features are measured on the region of interest.A rulebased classifier is used to classify four types of lesions.Jaccard’s similarity index of the segmentation results for acute stroke,solid tumor,chronic stroke,and necrosis are 0.8, 0.55, 0.27, and 0.42,respectively.The classification accuracy is 93% for acute stroke,73% for solid tumor,84% for chronic stroke,and 60% for necrosis. Overall,adaptive thresholding provides high segmentation performance for hyper-intensity lesions.The best segmentation and classification performance is achieved for acute stroke.The establishment of the technique could be used to automate the diagnosis and to clearly understand major brain lesions

Design of Finger-vein Capture Device with Quality Assessment using Arduino Micrcontroller

This paper focuses on designing and developing a finger-vein capturing device by using Arduino Microcontroller. It is a device that will capture the human finger-vein image and be controlled by Arduino Microcontroller. This device is applicable for authentication, verification and identification. It uses the concept of near-infrared light (NIR) emitted by a bank of NIR Light Emitting Diodes (LEDs). The NIR penetrates the finger and then absorbed by the haemoglobin in the blood. The areas in which the NIR rays are absorbed (i.e. Veins) thus appear as dark regions in an image conveyed by a CCD camera located on the opposite side of the finger. The brightness of the NIR will be controlled automatically using Arduino Microcontroller to obtain sufficient quality of image brightness. Although the Arduino Microcontroller is more expensive than potentiometer, it is more convenient and efficient as brightness adjuster. Besides that, it is definitely a low-cost device compares to FPGA. The image captured is analyzed by using Mean Square Error (MSE) and Peak Signalto-Noise Ratio (PSNR). A low cost capturing device is developed and decent quality finger-vein images are produced

Weather Detector for Motorcyclist with Notification from GSM/GPRS Module

This paper presents a weather detector for the motorcyclist. The device notifies the user by sending SMS and turns on the emergency light during rain. The device will detect three different weather conditions which are rain warning, heavy rain and sunny day. Water drop sensor will measure the resistance of the sensor if the water drops at the surface of the sensor and send data to the Arduino. Arduino will make a decision accordingly; then it will command the GSM/GPRS module to send a message to the user regarding the weather condition. The message will be sent for every 30 minutes throughout the rain. If the user were riding the motorcycle when the rain started, the emergency light would be turned on to alert another vehicle. Results showed that the system could differentiate three different weather condition and able to send out a warning message to the user when it would rain or heavy rain