Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

Effect of Self-Healing Concrete on Building Durability in the UK Construction Market

This study explores the effect of self-healing concrete in existing buildings and its crucial impact in terms of sustainability, permanence, and access to the internal strength of concrete in order to contribute to the process of protecting the internal parts of the building from the reinforcing steel structure as well as prevent the internal corrosion of iron reinforcement. This study also highlights the market growth side of the cement sector for self-healing products and access to growth in markets from (24 to 26%). Self-healing concrete technology is an advanced technology with self-healing compound. It allows for the repair of small cracks that affect the structure, which contributes to the support of the building to repair and rehabilitate the building. It also helps in repairing internal cracks without any financial costs. Compound prepares to spread when cracks occur and when reaching an appropriate environment in order to work to rehabilitate cracks. In the coming years, as growth thrives in order to reach the purchasing power in a prosperity in the cement sector, which improves internal and external investment and increases in attracting investors in the cement sector. The study aims to reach scientific research of high quality and it specializes in studying the United Kingdom market and the prosperity of self-healing concrete in the cement sector. Moreover, it contributes to the prosperity of self-healing products because of their contribution in reducing the financial costs that are under the building maintenance and rehabilitation department. This study also contributes to studies in the United Kingdom, which resulted from the impact of the cement market that will probably flourish and grow in the coming years. The researcher suggests that the development of the self-healing concrete will be reflected in the cement sector, which will be a reason for improving and developing self-healing concrete that will spread in sectors other than the cement industry

Dynamic Conditional Imitation Learning for Autonomous Driving

Conditional imitation learning (CIL) trains deep neural networks, in an end-to-end manner, to mimic human driving. This approach has demonstrated suitable vehicle control when following roads, avoiding obstacles, or taking specific turns at intersections to reach a destination. Unfortunately, performance dramatically decreases when deployed to unseen environments and is inconsistent against varying weather conditions. Most importantly, the current CIL fails to avoid static road blockages. In this work, we propose a solution to those deficiencies. First, we fuse the laser scanner with the regular camera streams, at the features level, to overcome the generalization and consistency challenges. Second, we introduce a new efficient Occupancy Grid Mapping (OGM) method along with new algorithms for road blockages avoidance and global route planning. Consequently, our proposed method dynamically detects partial and full road blockages, and guides the controlled vehicle to another route to reach the destination. Following the original CIL work, we demonstrated the effectiveness of our proposal on CARLA simulator urban driving benchmark. Our experiments showed that our model improved consistency against weather conditions by four times and autonomous driving success rate generalization by 52%. Furthermore, our global route planner improved the driving success rate by 37%. Our proposed road blockages avoidance algorithm improved the driving success rate by 27%. Finally, the average kilometers traveled before a collision with a static object increased by 1.5 times. The main source code can be reached at https://heshameraqi.github.io/dynamic_cil_autonomous_driving.Comment: 14 pages, 11 figures, 7 table

Peer learning on a shoe string: success of a distributive model for peer learning in a community radiology practice.

PURPOSE:To describe our 3-year experience operating a peer learning program with minimal resources, calculate the cost of the program, and compare participants' attitudes toward peer learning to the historical peer review system. MATERIALS AND METHODS:The peer learning conference is held monthly for 1 h via a web-based video conferencing platform. Case identification, curation, and conference presentation are performed by individual radiologists. Using national estimates for unit costs of radiologist time and other inputs, we calculated the marginal cost of the peer learning program to the medical group. After 21 months of holding the conference, we conducted an anonymous survey to assess the impact of the conference and how it may be improved. RESULTS:A peer learning conference was established for a 24- person community-based practice, which is part of a large multi-disciplinary medical group. Cases discussed included diagnostic errors, technologist errors, good calls, and challenging cases. Total annual cost of the program is $3288. Survey respondents had overall positive views of the conference and strongly preferred peer learning to the existing peer review system in place, with 80% of respondents rating peer learning more favorably than peer review on education value, supportive environment, and punitive process, and 70% more positively on culture of blaming. CONCLUSION:We established a peer learning program with minimal resources. Radiologists in the group strongly prefer the peer learning conference over the existing peer review program in place. A peer learning program can be successfully started and sustained with limited resources

Diffusion Tensor Imaging in a Large Longitudinal Series of Patients With Cervical Spondylotic Myelopathy Correlated With Long-Term Functional Outcome

BACKGROUND Fractional anisotropy (FA) of the high cervical cord correlates with upper limb function in acute cervical cord injury. We investigated the correlation between preoperative FA at the level of maximal compression and functional recovery in a group of patients after decompressive surgery for cervical spondylotic myelopathy (CSM). OBJECTIVE To determine the usefulness of FA as a biomarker for severity of CSM and as a prognostic biomarker for improvement after surgery. METHODS Patients received diffusion tensor imaging (DTI) scans preoperatively. FA values of the whole cord cross-section at the level of maximal compression and upper cervical cord (C1-2) were calculated. Functional status was measured using the modified Japanese Orthopedic Association (mJOA) scale preoperatively and at follow-up up to 2 yr. Regression analysis between FA and mJOA was performed. DTI at C4-7 was obtained in controls. RESULTS Forty-four CSM patients enrolled prior to decompression were compared with 24 controls. FA at the level of maximal compression correlated positively with preoperative mJOA score. Preoperative FA correlated inversely with recovery throughout the postoperative period. This was statistically significant at 12 mo postoperation and nearly so at 6 and 24 mo. Patients with preoperative FA0.55. CONCLUSION In the largest longitudinal study of this kind, FA promises a valid biomarker for severity of CSM and postoperative improvement. FA is an objective measure of function and could provide a basis for prognosis. FA is particularly useful if preoperative values are less than 0.55

A Biosensor-CMOS Platform and Integrated Readout Circuit in 0.18-μm CMOS Technology for Cancer Biomarker Detection

This paper presents a biosensor-CMOS platform for measuring the capacitive coupling of biorecognition elements. The biosensor is designed, fabricated, and tested for the detection and quantification of a protein that reveals the presence of early-stage cancer. For the first time, the spermidine/spermine N1 acetyltransferase (SSAT) enzyme has been screened and quantified on the surface of a capacitive sensor. The sensor surface is treated to immobilize antibodies, and the baseline capacitance of the biosensor is reduced by connecting an array of capacitors in series for fixed exposure area to the analyte. A large sensing area with small baseline capacitance is implemented to achieve a high sensitivity to SSAT enzyme concentrations. The sensed capacitance value is digitized by using a 12-bit highly digital successive-approximation capacitance-to-digital converter that is implemented in a 0.18 μm CMOS technology. The readout circuit operates in the near-subthreshold regime and provides power and area efficient operation. The capacitance range is 16.137 pF with a 4.5 fF absolute resolution, which adequately covers the concentrations of 10 mg/L, 5 mg/L, 2.5 mg/L, and 1.25 mg/L of the SSAT enzyme. The concentrations were selected as a pilot study, and the platform was shown to demonstrate high sensitivity for SSAT enzymes on the surface of the capacitive sensor. The tested prototype demonstrated 42.5 μS of measurement time and a total power consumption of 2.1 μW

Evolutionary conservation suggests a regulatory function of AUG triplets in 50 -UTRs of eukaryotic genes

By comparing sequences of human, mouse and rat orthologous genes, we show that in 50 -untranslated regions (50 -UTRs) of mammalian cDNAs but not in 30 - UTRs or coding sequences, AUG is conserved to a significantly greater extent than any of the other 63 nt triplets. This effect is likely to reflect, primarily, bona fide evolutionary conservation, rather than cDNA annotation artifacts, because the excess of conserved upstream AUGs (uAUGs) is seen in 50 -UTRs containing stop codons in-frame with the start AUG and many of the conserved AUGs are found in different frames, consistent with the location in authentic non-coding sequences. Altogether, conserved uAUGs are present in at least 20–30% of mammalian genes. Qualitatively similar results were obtained by comparison of orthologous genes from different species of the yeast genus Saccharomyces. Together with the observation that mammalian and yeast 50 -UTRs are significantly depleted in overall AUG content, these findings suggest that AUG triplets in 50 -UTRs are subject to the pressure of purifying selection in two opposite directions: the uAUGs that have no specific function tend to be deleterious and get eliminated during evolution, whereas those uAUGs thatdoserveafunctionareconserved.Mostprobably, the principal role of the conserved uAUGs is attenuation of translation at the initiation stage, which is often additionally regulated by alternative splicing in the mammalian 50 -UTRs. Consistent with this hypothesis, we found that open reading frames starting from conserved uAUGs are significantly shorter than those starting from non-conserved uAUGs, possibly, owing to selection for optimization of the level of attenuation