Quantitative measurement of vehicle driver alertness and response readiness to on-road emergency cues could add more response time to driver assistance and safety features. Exogenous Electroencephalogram (EEG) potentials generated in Beta frequencies can imply driver’s attention to the situation, the absence of which may be associated with distraction or drowsiness. Detection of specific Alpha potentials during action-soliciting events may further indicate risk of violating response-trigger thresholds beyond which physical and vehicular response periods may become too short to avoid collision. Specific Event-Related Potentials (ERP) associated with early automatic cognitive process of consciousness to obstacles, emotional reaction to visual cues, risk evaluation, drier’s emotional intensity, anticipation and motor preparation can be identified, isolated and processed to create a model that predicts driver’s intension/capability to respond to obstacles within a derived time threshold. Isolating signals that indicate driver in-activity (extreme fatigue, sleep, visual or auditory distraction) during near-obstacle situations could further be used to preempt user-in-loop drive, in semi-autonomous vehicles, even if the driver is inhibiting normal transition to self-drive mode. Existing safety components, like airbags, could be issued pre-warnings about possible crash situations (currently they are actuated only upon impact), giving them critical additional time to get pre-actuation sequences triggered.

The latest developments in medical imaging and computer-aided solutions for image processing problems attract attention of various researchers to impart their research in the medical imaging field.   Designing and developing efficient algorithms to present the medical information effectively have become critical areas of research in this field. A 3-Stage Hybrid Computer Aided Design system is introduced to identify Brain tumor in earlier stages by extracting meaningful information from multimodal medical images. The preferred multi-modality images is Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). The CAD system proposed in this paper can eliminate the dependency on human operators as it is an efficient software-based system. The first stage in this model consists of image pre-processing with Wavelet and Curvelet transforms. The second stage of the CAD system is an image segmentation process, which involves a combination of Wavelet Transform and Watershed Technique. The third stage involves image fusion, where the individually segmented CT and MRI images are fused together to obtain an integrated complementary information from two different images. This is followed by decomposing CT and MRI images using the Dual Tree Complex Wavelet Transform (DTCWT) and Nonsubsampled Contourlet Transform (NSCT).