A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification

Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.

Geometric shape and tensile forces on silo bags for grain storage

This work consists in the analytical derivation and numerical solution of the equation which determines the shape of the section of a silo bag: a long horizontal cylindrical plastic bag filled with either a liquid or a granular material which behaves similarly, exerting normal, but not frictional forces against the wall. The bag is considered inextensible and completely flexible, capable of supporting only tensile loads. These suppositions lead to a secondorder differential equation for the membrane shape, which is normalised and solved, in a way that allows, for any bag with any amount of filling, a simple computation of its geometry, enclosed area and tensile loads. A discussion is included about the effects on the theoretical results of the silo bag tensile deformation.

Verifiable Essential Secret Image Sharing with Multiple Decryptions

We know that the essential secret image sharing (ESIS) scheme differs from traditional visual secret sharing with respect to the essentiality of the shares. In ESIS, to reconstruct the secret image, threshold number of the shares are required which includes all the essential shares. All the shares are very sensitive because it carries the secret information. Hence, reliability and authenticity of the shares before the decoding of the secret image are required which prevents a participant from intentionally or unintentionally to provide invalid shares. Proposed method is a novel verifiable essential secret image sharing (VESIS) with multiple decryption. Multiple decryption means that the decoding and verification process is done by human visual system as well as by EX-ORing the shares. Apart from this, proposed scheme also eliminates unnecessary encryption constraints of VSS like pixel expansion, explicit codebook and the number of the participants and it is also required simple computation and O(k) complexity for the decoding process.

Fidelity-based unitary operation-induced quantum correlation for continuous-variable systems

We propose a measure of nonclassical correlation [Formula: see text] in terms of local Gaussian unitary operations based on square of the fidelity [Formula: see text] for bipartite continuous-variable systems. This quantity is easier to be calculated or estimated and is a remedy for the local ancilla problem associated with the geometric measurement-induced nonlocality. A simple computation formula of [Formula: see text] for any [Formula: see text]-mode Gaussian states is presented and an estimation of [Formula: see text] for any [Formula: see text]-mode Gaussian states is given. For any [Formula: see text]-mode Gaussian states, [Formula: see text] does not increase after performing a local Gaussian channel on the unmeasured subsystem. Comparing [Formula: see text] in scale with other quantum correlations such as Gaussian geometric discord for two-mode symmetric squeezed thermal states reveals that [Formula: see text] is much better in detecting quantum correlations of Gaussian states.

Risk Assessment for Model and Simulation Credibility Characteristics

The Department of Defense (DoD) uses the principles of risk assessment extensively throughout the acquisition process to identify, manage, and mitigate risk in many areas including software (both simulation software and software embedded in materiel systems). The approach to assessing the risks associated with using results from Models and Simulations (M&S) to influence decision-making that is described here is used by the Naval Air Systems Command (NAVAIR) 5.4H Verification, Validation, and Accreditation office. The focus of the risk assessment is on ten specific characteristics of M&S Capability, Accuracy, and Usability that have the most potential impact on the intended uses. In this method, the characteristics of the M&S are evaluated against standard criteria, and then assigned a color-rating of Green, Yellow, or Red based on the actual data obtained from the M&S itself. A simple computation is then used to weigh the ratings of the characteristics into the likelihood of error aspect of the risk of using the M&S. A thorough understanding of the M&S and its application is a prerequisite to using this approach since the analyst is required to provide a technical justification for the rating assigned to a characteristic. Should the assigned rating for a characteristic of the M&S not be green, the analyst is required to recommend actions and/or mitigations for the M&S developer to take that would improve the rating to green.

Focusing attention in populations of semi-autonomously operating sensing nodes

Cognition and the cognitive processing of sensory information in biological entities is known to occur over multiple layers of processing. In the example of human vision there are a vast number of photo-receptors feeding into various layers of cells which pre-process the original information before it arrives to the brain (as biased data).We propose to use a mechanism known to theoretical biologists as a means to bring about adaptive selforganization in colonies of social insects, and to apply it to such early stage signal processing. The underlying mathematical model is simple, and in the coming years, robotics will move into an era when aggregating simple computation devices into massively large collectives becomes feasible, making it possible to actually build such distributed cognitive sensing systems.