Effect of a Pacific sea surface temperature anomaly on the circulation over North America

During the fall and winter of 1976-1977, sea surface temperature (SST) in the north Pacific was characterized by abnormally cold temperatures in the central and western portions of the north Pacific with a warm pool located off the west coast of the U.S. It was suggested that the north Pacific SST anomalies were one of the multiple causes of the abnormally cold temperatures in eastern North America during the 1976-1977 winter. An attempt was made to test this hypothesis by conducting a numerical experiment with the GLAS general circulation model

A detailed investigation of microbial cell disruption by hydrodynamic cavitation for selective product release

Includes bibliographical references.Hydrodynamic cavitation is a novel method for microbial cell disruption, mediated by intense pressure fluctuations caused by cavity oscillation and collapse. Selective release of intracellular microbial products is desirable to reduce the cost involved in their downstream processing. A study of the process variables that affect microbial cell disruption by hydrodynamic cavitation is presented in order to ascertain the conditions required for a selective release. Two model systems were considered (yeast and E. coil). Enzymes from different locations of the cell were studied and the release compared with other methods of disruption

Acoustic Thermometry Based on Accurate Measurements of Speed of Sound in Air

L'abstract è presente nell'allegato / the abstract is in the attachmen

Data Quality and Reliability Assessment of Wearable EMG and IMU Sensor for Construction Activity Recognition

The workforce shortage is one of the significant problems in the construction industry. To overcome the challenges due to workforce shortage, various researchers have proposed wearable sensor-based systems in the area of construction safety and health. Although sensors provide rich and detailed information, not all sensors can be used for construction applications. This study evaluates the data quality and reliability of forearm electromyography (EMG) and inertial measurement unit (IMU) of armband sensors for construction activity classification. To achieve the proposed objective, the forearm EMG and IMU data collected from eight participants while performing construction activities such as screwing, wrenching, lifting, and carrying on two different days were used to analyze the data quality and reliability for activity recognition through seven different experiments. The results of these experiments show that the armband sensor data quality is comparable to the conventional EMG and IMU sensors with excellent relative and absolute reliability between trials for all the five activities. The activity classification results were highly reliable, with minimal change in classification accuracies for both the days. Moreover, the results conclude that the combined EMG and IMU models classify activities with higher accuracies compared to individual sensor models

A Study on “Security of Cyber-Physical Systems in the Cloud”

The existing security models are built with certain assumptions. The solutions like distributed accountability, provable data possession (PDP), Third Party Auditing (TPA) and so on are secure as long as the assumptions hold true. To ensure fool proof security for cloud storage security little research has been made on quantum key cryptography. Since the quantum key distribution is unconditionally secure, we propose a new scheme known as Cloud QKDP (Quantum Key Distribution Protocol for Cloud Computing) which exploits the benefits of quantum mechanisms to secure cloud storage and data dynamics. We consider a case study in which three parties such as cloud server, data owner and trusted client have provably secure communications with our proposed scheme which uses random oracle model. Our empirical study revealed mixture of success and failure rates with private and public clouds respectively

Taguchi Analysis on Cutting Forces and Temperature in Turning Titanium Ti-6Al-4V

Titanium alloy machining is hindered basically due to its high chemical reactivity and low thermal conductivity. The present work is focused on investigating the effect of process parameters on machinability performance characteristics and there by optimization of the turning of Titanium (Grade 5) based on Taguchi method. The cutting speed, feed and depth of cut were used as the process parameters where as the cutting force and temperature ware selected as performance characteristics. The L9 orthogonal array based on design of experiments was used to conduct experiments. The degree of influence of each process parameter on individual performance characteristic was analyzed from the experimental results obtained using Taguchi Method. The cutting speed was identified as the most influential process parameter on cutting force and temperature

Sensor for the Characterization of 2D Angular Actuators with Picoradian Resolution and Nanoradian Accuracy with Microradian Range

High precision angular actuators are used for high demanding applications such as laser steering for photolithography. Piezo technology allows developing actuators with a resolution as low as a few nanoradians, with bandwidths as high as several kilohertz. In most demanding applications, the actual performance of these instruments needs to be characterized. The best angular measurement instruments available today do not sucient resolution and/or bandwidth to satisfy these needs. At the Istituto Nazionale di Ricerca Metrologica, INRIM a device was designed and built aiming at characterizing precision 2D angular actuators with a resolution surpassing the best devices on the market. The device is based on a multi reflection scheme that allows multiplying the deflection angle by a factor of 70. The ultimate resolution of the device is 2 prad/radq(Hz) over a measurement range of 36 urad with a measurement band >10 kHz. The present work describes the working principle, the practical realization, and a case study on a top-level commercial angular actuator (Nano-MTA2 produced by Mad City Labs)

Evaluating the Use of Lidar for Landslide Monitoring on Oklahoma Highways

Landslides cause huge human loss and property damage when they occur near infrastructure such as highways. The current approach for dealing with landslides by the Oklahoma Department of Transportation (ODOT) is primarily reactive because there is no effective monitoring mechanism to assess the risk of landslide properly. When the damage is already done, expensive repairs follows because the repair process is time driven and the use of resources may not be the most cost-effective. Traffic lane closures during the repair increases travel time and road users’ cost. This gives an opportunity to look for alternative practices. Several studies have proved that the LIDAR technology can be used to detect the slope changes in mountains, but there is no readily available generalized framework to apply this technology to monitor or assess the risk of landslides. The objectives of this study are 1) to develop a comprehensive workflow to apply this technology, 2) to evaluate registration and vegetation algorithms on the collected data, 3) to assess the displacement change over various seasons, and 4) to assess the impact of vegetation removal and downsampling algorithms on displacement change. For this study, the data was collected from four different sites that include both rock type and soil type slopes on Oklahoma highways, collected in four different seasons (summer, dry, winter and warm seasons) of the year. Then, M3C2 displacement analysis was performed on different seasons’ data to identify the displacement change over different seasons. Throughout the entire research process, various technical challenges associated with the application of the LIDAR technology were reported along with recommendations to overcome these challenges. Through M3C2 analysis, it was observed that the largest change was observed during June and September. By considering the current level of registration, no significant change was observed in the majority of the areas. It was also observed from the study that vegetation removal and downsampling have impacts on the result of statistical displacement and significant change analyses. The comprehensive workflow developed in this study can help ODOT to implement the LIDAR technology to monitor and assess the risk of landslides on highways in a cost effective manner.Civil Engineerin

Traffic Light Recognition using Convolutional Neural Networks: A Survey

Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive overview of the underlying model architectures for this task is currently missing. In this work, we conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs). We focus on two essential aspects: datasets and CNN architectures. Based on an underlying architecture, we cluster methods into three major groups: (1) modifications of generic object detectors which compensate for specific task characteristics, (2) multi-stage approaches involving both rule-based and CNN components, and (3) task-specific single-stage methods. We describe the most important works in each cluster, discuss the usage of the datasets, and identify research gaps.Comment: Accepted for publication at ITSC202

Importance Sampling BRDF Derivatives

We propose a set of techniques to efficiently importance sample the derivatives of several BRDF models. In differentiable rendering, BRDFs are replaced by their differential BRDF counterparts which are real-valued and can have negative values. This leads to a new source of variance arising from their change in sign. Real-valued functions cannot be perfectly importance sampled by a positive-valued PDF and the direct application of BRDF sampling leads to high variance. Previous attempts at antithetic sampling only addressed the derivative with the roughness parameter of isotropic microfacet BRDFs. Our work generalizes BRDF derivative sampling to anisotropic microfacet models, mixture BRDFs, Oren-Nayar, Hanrahan-Krueger, among other analytic BRDFs. Our method first decomposes the real-valued differential BRDF into a sum of single-signed functions, eliminating variance from a change in sign. Next, we importance sample each of the resulting single-signed functions separately. The first decomposition, positivization, partitions the real-valued function based on its sign, and is effective at variance reduction when applicable. However, it requires analytic knowledge of the roots of the differential BRDF, and for it to be analytically integrable too. Our key insight is that the single-signed functions can have overlapping support, which significantly broadens the ways we can decompose a real-valued function. Our product and mixture decompositions exploit this property, and they allow us to support several BRDF derivatives that positivization could not handle. For a wide variety of BRDF derivatives, our method significantly reduces the variance (up to 58x in some cases) at equal computation cost and enables better recovery of spatially varying textures through gradient-descent-based inverse rendering