Visual Traffic Movement Counts at Intersection for Origin-Destination (O-D) Trip Table Estimation

Origin-destination (O-D) trip table is necessary information for transportation planning and traffic impact study. However, current O-D estimations rely on estimated directional traffic counts at intersections, which obviously diminish reliability in the table. A video-based vehicle tracking system that utilizes wide view-angle lenses has been studied [26] to provide accurate direction traffic counts. The system expands the area covered by a single camera and uses a particle filtering method to handle environmental changes as well as geometric distortion caused by those lenses. This paper shows tracking capability of the system and also shows how to incorporate the directional traffic counts in the O-D estimation

Bank partnership and liquidity crisis

This study empirically investigates the relationship between banking integration and liquidity management. To measure banks’ connectivity, we use the number of partnerships proxied via the syndicated loan arrangements in which they serve as lead arrangers. If banks establish more business partnerships through syndicated loan arrangements, those under market stress are more likely to face increased funding costs, create reduced liquidity, and originate declined small business loans and mortgages. Those banks with more partners are shown to have a lower liquidity coverage ratio, suggesting that business partnerships create a disincentive toward liquidity risk management

Whitening Technique Based on Gram–Schmidt Orthogonalization for Motor Imagery Classification of Brain–Computer Interface Applications

A novel whitening technique for motor imagery (MI) classification is proposed to reduce the accuracy variance of brain–computer interfaces (BCIs). This method is intended to improve the electroencephalogram eigenface analysis performance for the MI classification of BCIs. In BCI classification, the variance of the accuracy among subjects is sensitive to the accuracy itself for superior classification results. Hence, with the help of Gram–Schmidt orthogonalization, we propose a BCI channel whitening (BCICW) scheme to minimize the variance among subjects. The newly proposed BCICW method improved the variance of the MI classification in real data. To validate and verify the proposed scheme, we performed an experiment on the BCI competition 3 dataset IIIa (D3D3a) and the BCI competition 4 dataset IIa (D4D2a) using the MATLAB simulation tool. The variance data when using the proposed BCICW method based on Gram–Schmidt orthogonalization was much lower (11.21) than that when using the EFA method (58.33) for D3D3a and decreased from (17.48) to (9.38) for D4D2a. Therefore, the proposed method could be effective for MI classification of BCI applications

Bias-Voltage Stabilizer for HVHF Amplifiers in VHF Pulse-Echo Measurement Systems

The impact of high-voltage–high-frequency (HVHF) amplifiers on echo-signal quality is greater with very-high-frequency (VHF, ≥100 MHz) ultrasound transducers than with low-frequency (LF, ≤15 MHz) ultrasound transducers. Hence, the bias voltage of an HVHF amplifier must be stabilized to ensure stable echo-signal amplitudes. We propose a bias-voltage stabilizer circuit to maintain stable DC voltages over a wide input range, thus reducing the harmonic-distortion components of the echo signals in VHF pulse-echo measurement systems. To confirm the feasibility of the bias-voltage stabilizer, we measured and compared the deviations in the gain of the HVHF amplifier with and without a bias-voltage stabilizer. Between −13 and 26 dBm, the measured gain deviations of a HVHF amplifier with a bias-voltage stabilizer are less than that of an amplifier without a bias-voltage stabilizer. In order to confirm the feasibility of the bias-voltage stabilizer, we compared the pulse-echo responses of the amplifiers, which are typically used for the evaluation of transducers or electronic components used in pulse-echo measurement systems. From the responses, we observed that the amplitudes of the echo signals of a VHF transducer triggered by the HVHF amplifier with a bias-voltage stabilizer were higher than those of the transducer triggered by the HVHF amplifier alone. The second, third, and fourth harmonic-distortion components of the HVHF amplifier with the bias-voltage stabilizer were also lower than those of the HVHF amplifier alone. Hence, the proposed scheme is a promising method for stabilizing the bias voltage of an HVHF amplifier, and improving the echo-signal quality of VHF transducers

A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces

The brain–computer interface (BCI) is used to understand brain activities and external bodies with the help of the motor imagery (MI). As of today, the classification results for EEG 4 class BCI competition dataset have been improved to provide better classification accuracy of the brain computer interface systems (BCIs). Based on this observation, a novel quick-response eigenface analysis (QR-EFA) scheme for motor imagery is proposed to improve the classification accuracy for BCIs. Thus, we considered BCI signals in standardized and sharable quick response (QR) image domain; then, we systematically combined EFA and a convolution neural network (CNN) to classify the neuro images. To overcome a non-stationary BCI dataset available and non-ergodic characteristics, we utilized an effective neuro data augmentation in the training phase. For the ultimate improvements in classification performance, QR-EFA maximizes the similarities existing in the domain-, trial-, and subject-wise directions. To validate and verify the proposed scheme, we performed an experiment on the BCI dataset. Specifically, the scheme is intended to provide a higher classification output in classification accuracy performance for the BCI competition 4 dataset 2a (C4D2a_4C) and BCI competition 3 dataset 3a (C3D3a_4C). The experimental results confirm that the newly proposed QR-EFA method outperforms the previous the published results, specifically from 85.4% to 97.87% ± 0.75 for C4D2a_4C and 88.21% ± 6.02 for C3D3a_4C. Therefore, the proposed QR-EFA could be a highly reliable and constructive framework for one of the MI classification solutions for BCI applications

Design of an Internal Focusing Tube Lens for Optical Inspection Systems

The numerical aperture (NA) of objective lens optical (inspection) systems has been increased to achieve higher resolution. However, the depth of focus decreases with an increase in the NA, and focusing becomes difficult. Therefore, the entire optical lens in currently developed optical inspection systems must be moved to focus within the depth of focus. To achieve a high resolution, many lenses are used in optical inspection systems, increasing the size and weight of the optical systems. To address this issue, a focus control group was placed on a tube lens that could adjust its focus based on the movement of the sample in front of the objective lens. Therefore, we developed a focus range increment to focus on the range of the optical inspection system. Using objective lenses with focal lengths of 30 and 60 mm and tube lenses with a focal length of 300 mm, optical systems for 10× and 5× inspection were constructed. In the designed optical systems, the weights of the objective lenses with focal lengths of 30 and 60 mm were calculated to be approximately 844 and 570 g, respectively. These values confirm that the weight of the moving group can be reduced

Deep and Densely Connected Networks for Classification of Diabetic Retinopathy

Diabetes has recently emerged as a worldwide problem, and diabetic retinopathy is an abnormal state associated with the human retina. Due to the increase in daily screen-related activities of modern human beings, diabetic retinopathy is more prevalent among adults, leading to minor and major blindness. Doctors and clinicians are unable to perform early diagnoses due to the large number of patients. To solve this problem, this study introduces a classification model for retinal images that distinguishes between the various stages of diabetic retinopathy. This work involves deploying deep and densely connected networks for retinal image analysis with training from scratch. Dense connections between the convolutional layers of the network are an essential factor to enhance accuracy owing to the deeper supervision between layers. Another factor is the growth rate that further assists our model in learning more sophisticated feature maps regarding retinal images from every stage of the network. We compute the area under the curve, sensitivity, and specificity, particularly for messidor-2 and EyePACS. Compared to existing approaches, our method achieved better results, with an approximate rise rate of 0.01, 0.03, and 0.01, respectively. Therefore, computer-aided programs can help in diagnostic centers as automated detection systems

A preliminary study on real-time Rn/Tn discriminative detection using air-flow delay in two ion chambers in series

Due to its short half-life, thoron gas has been assumed to have negligible health hazards on humans compared to radon. But, one of the decay products with a long half-life can make it to be transported to a long distance and to cause a severe internal dose through respiration. Since most commercial radon detectors can not discriminate thoron signals from radon signals, it is very common to overestimate radon doses which in turn result in biased estimation of lung cancer risk in epidemiological studies. Though some methods had been suggested to measure thoron and radon separately, they could not be used for real-time measurement because of CR-39 or LR-115. In this study, an effective method was suggested to measure radon and thoron separately from the free air. It was observed that the activity of thoron decreases exponentially due to delay time caused by a long pipe between two chambers. Therefore from two ion chambers apart in time, it was demonstrated that thoron and radon could be measured separately and simultaneously. We also developed a collimated alpha source and with this source and an SBD, we could convert the ion chamber reading to count rate in cps

A device binding method based on content illumination pattern in public display environments.

Digital public displays installed in various locations provide valuable information for the passers-by. However, the static characteristic of the digital public display limits the consumption of the displayed content to a small area. Personal mobile devices such as smartphones are now capable of interacting with digital public displays, which enables the passers-by to "take-away" the content and consume it on-the-go. This process requires device binding, content selection, and transfer between the two devices. In this paper, we propose a device binding method which utilizes the content brightness changing pattern as a unique content ID on the public display and an illuminance sensor on the mobile to bind and transfer between two devices. We conducted performance evaluations for binding algorithm robustness in different conditions. Also comparative studies among other binding interaction methods were conducted. Our results show that our proposed method performed stably across the various conditions and overall performance in interaction completion time and error rate was similar or superior to the existing methods