the fear of driving can be severe enough to be considered a phobia. it is often great enough that people will avoid driving at all costs and instead find someone to drive them or use public transportation, regardless of how inconvenient or expensive. this fear will cause many to avoid driving, create excuses to not drive, or even refuse to get a driver's license for years. the aim of this ...

The development in computer vision has aided drivers the form of automatic self-driving cars etc. accidents are caused by driver's exhaustion and drowsiness about 20%. Its carriages a dangerous issue for which numerous methods were proposed. However, they not appropriate real-time implementation. major encounters confronted these approaches forcefulness to handle dissimilarity human face lightn...

The driver sleepiness is common and one of the main causes road accidents. So, there a need for automatically detecting this human behaviour. In any case, if drivers feel drowsy, they still keep on driving vehicle, accidents occur. This study can be implemented using CNN training model initiating an alarm drowsiness condition detected. Many authors suggested process Drowsiness (Problem Statemen...

Due to the increasing of traffic accidents, there is an urgent need control and reduce driving mistakes. Driver fatigue or drowsiness one these major Algorithms have been developed address this issue by detecting alerting driver dangerous condition. The problem algorithms their accuracy, well as time required detect status alert driver. accuracy represent a critical condition that affects reduc...

In current years, drowsy driver detection is the most necessary procedure to prevent any road accidents, probably worldwide. The aim of this study was construct a smart alert technique for building intelligent vehicles that can automatically avoid impairment. But drowsiness natural phenomenon in human body happens due different factors. Hence, it required design robust system cause mishap. prop...

The wakefulness state of healthy subjects tends to be an early drowsy state because of the prolonged times required for electroencephalography (EEG) measurements. In such cases, clinicians should consider accurate diagnosis of the wakefulness state of the subjects in order to interpret EEG signals accurately. The aim of the present study was to quantitatively evaluate the wakefulness state (ear...

We propose a novel method for automatic recognition of alertness level from full spectrum electroencephalogram (EEG) recordings. This procedure uses power spectral density (PSD) of discrete wavelet transform (DWT) of full spectrum EEG as an input to an artificial neural network (ANN) with three discrete outputs: alert, drowsy and sleep. The error back propagation neural network is selected as a...

This paper compares the effectiveness of state-preserving and non-state-preserving techniques for leakage control in caches by comparing drowsy cache and gated-V for data caches using 70nm technology parameters. To perform the comparison, we use “HotLeakage”, a new architectural model for subthreshold and gate leakage that explicitly models the effects of temperature, voltage, and parameter var...

In this paper, we propose a phased drowsy instruction cache with on-demand wakeup prediction policy (called “phased on-demand policy”) to reduce the leakage and dynamic energy with less performance overhead. As in prior non-phased on-demand policy, an extra stage for wakeup is inserted before the fetch stage in pipeline. The drowsy cache lines are woken up in wakeup stage and the wakeup latency...

During drowsy periods, performance on tasks requiring continuous attention becomes intermittent. Previously, we have reported that during drowsy periods of intermittent performance, 7 of 10 participants performing an auditory detection task exhibited episodes of non-responding lasting about 18 s (Makeig & Jung, 1996). Further, the time patterns of these episodes were repeated precisely in subse...