Event-Related Potential N100 Vs. N170 Wave Results Comparison On Driving Alertness

Driver’s attention, especially during long rides, is very crucial to avoid road accidents, which may lead to injuries and fatalities on the roadway. However, with modern technologies, the decline in driver’s attention can be investigated through the electrical activity of the brain. Event-Related Potential (ERP) is the electrophysiological brain response measurement that related to the sensory, cognitive and motor events. By using simple averaging techniques, ERPs can give reliable result to measure the attentiveness of the driver during driving. In this paper, N100 and N170 wave results were presented, which obtained from temporal and occipital lobes respectively and been compared to measure the driver’s attention. In term of attentiveness difference percentage, it is 0.09% and 38% differences were observed from N100 and N170 wave results respectively. From the results, it clearly can be seen that using N170 wave from occipital lobe is more significant to measure the driver’s alertness compared to N100 wave that recorded from the temporal lobe

Hybrid Mean Fuzzy Approach For Attention Detection

Statistics around the world showed that attention deficit significantly leads to road accidents. Hence, the growth of studies on attention deficit detection becoming more important. The studies obtained the waveform from electroencephalography (EEG) to identify the characteristic of attention. However, each individual has own unique characteristics to significantly shown the attention deficit. Thus, this research aim is to use the fuzzy approach to minimize the variability gap of the EEG signal between each individual. The research conducted the prior experiment to develop control parameter for training set of fuzzy by using two distinct stimulations to create two groups of attention sample i.e., attentive and inattentive. An approach of novel Hybrid Mean Fuzzy (HMF) was proposed in this research to detect attention deficit in EEG signal. It is the combination of simple averaging (Mean) and Fuzzy approaches for EEG analysis and classification. The results of using this method shows a significantly change in EEG signal which correlates to the attention detection. An Attention Degradation Scale (ADS) is successfully developed as the threshold value of EEG for attention detection. Therefore, the findings in this research can be a promising foundation on attention deficit detection in large application not only for reducing the road accidents

N170 Wave Amplitude Analysis On Driving Performance On Highway Road

Attention in the physiological definition is taking possession of the mind in one of several simultaneously events of thoughts which implies withdrawing some things to deal effectively with other. In driving particularly, attention is essential to keep track of the driver’s vigilance to avoid the road accident. In this paper, analysis of the driver’s attention is done through their driving performance from both of Electroencephalographic (EEG) amplitude and accident score. During the driving experiment, two stimulations will be given to the subjects which are silent environment (in certain dB) and listening to the live streaming radio. The results show that when listening to the radio, the driving performance is improved and the score of the accident is reduced as well. This figure gave a concrete justification that driver’s attention able to maintain or even increase when the stimulation is triggered

Modeling arbiter-PUF in NodeMCU ESP8266 using artificial neural network

A hardware fingerprinting primitive known as physical unclonable function (PUF) has a huge potential for secret-key cryptography and identification/authentication applications. The hardware fingerprint is manifested by the random and unique binary strings extracted from the integrated circuit (IC) which exist due to inherent process variations during its fabrication. PUF technology has a huge potential to be used for device identification and authentication in resource-constrained internet of things (IoT) applications such as wireless sensor networks (WSN). A secret computational model of PUF is suggested tobe stored in the verifier’s database as an alternative to challenge and response pairs (CRPs) to reduce area consumption. Therefore, in this paper, the design steps to build a PUF model in NodeMCU ESP8266 using an artificial neural network (ANN) are presented. Arbiter-PUF is used in our study and NodeMCU ESP8266 is chosen because it is suitable to be used as a sensor node or sink in WSN applications. ANN with a resilient back-propagation training algorithm is used as it can model the non-linearity with high accuracy. The results show that ANN can model the arbiter-PUF with approximately 99.5% prediction accuracy and the PUF model only consumes 309,889 bytes of memory space

Attention Level Determination by Using Fuzzy

For safe driving, it is always crucial to keep 100% attention on driving without distraction. The road accident may happen with just a blink of eyes. Previous studies have shown that peak decrement of N170 wave relates to degrading of attention. However, to determine the level of attention is challenging due to inter-variability of decrement range between subjects. Hence, in this study, the attention level is investigated by analyzing the peak of N170 decrement versus accident score using fuzzy rule-based to minimize the grey area.  Accordingly, three levels of attention are found, i.e., attentive, begin of inattentive and inattentive. 23.3%% or less decrements of N170 peak is associate to significant attentive. When the decrements reach the 34.3% of N170 peak, the subjects showed the inattentive behavior and most of the subjects have shown that, 41.03% of N170 peak decrements is significant inattentive. This finding gives a promising foundation in developing a hardware for attention alarm system

A Novel N100 And N170 Wave Degrading Scale By Using Hybrid Fuzzy Logic Control Method For Driving Alertness Measurement

Previous studies have shown that degrading of N100 and N170 waves amplitudes are correlate with higher accident and broken rules in driving experiment. Hence, a foundation of developing a device to detect attention alertness during driving is promising. However, due to variability of the amplitude degrading value among subjects and some of them gives false positive results, the determination of the optimum thresholds to classify the degree of significant degrading of the N100 and N170 wave amplitude is impossible. In engineering application, the threshold value is important in order to give the correct measurement to activate the actuator. Hence, in this proposal, the aims are for a first time to formulate a scale of N100 and N170 amplitude degrading, to define a threshold to differentiate between significant degrading and not significant degrading of the response and to analyse the feasibility of the scale to be used to measure attention alertness while driving. This study proposes a state of the art hybrid analysis technique i.e., mean and fuzzy logic to classify degrading value into scale of very significant degrading, less significant degrading, no changes, less attention and very significant attention. The obtained scale helps to determine the threshold that could be used as foundation to design a device that alerts one vigilant on the road. With this device, hopefully the statistic of road accident will decrease. This scale also could be used in other evoked response potential amplitude degrading/habituation study or applications

Android Based Application For Visually Impaired Using Deep Learning Approach

People with visually impaired had difficulties in doing activities related to environment, social and technology. Furthermore, they are having issues with independent and safe in their daily routine. This research propose deep learning based visual object recognition model to help the visually impaired people in their daily basis using the android application platform. This research is mainly focused on the recognition of the money, cloth and other basic things to make their life easier. The convolution neural network (CNN) based visual recognition model by TensorFlow object application programming interface (API) that used single shot detector (SSD) with a pre-trained model from Mobile V2 is developed at Google dataset. Visually impaired persons capture the image and will be compared with the preloaded image dataset for dataset recognition. The verbal message with the name of the image will let the blind used know the captured image. The object recognition achieved high accuracy and can be used without using internet connection. The visually impaired specifically are largely benefited by this research

The Comparative Study of Deep Learning Neural Network Approaches for Breast Cancer Diagnosis

Breast cancer is one of the life threatening cancer that leads to the most death due to cancer among the women. Early diagnosis might help to reduce mortality. Thus, this research aims to study on different approaches of the deep learning neural network model for breast cancer early detection for better prognosis. The performance of deep learning approaches such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN) and Convolution Neural Network (CNN) are evaluated using the dataset from the University of Wisconsin. The findings show ANN achieved high accuracy of 99.9 % compared to others in detecting breast cancer. ANN is able to deliver better results with the provided dataset. However, more improvement needed for better performance to ensure that the approach used is reliable enough for breast cancer early diagnosis