Quantification of Lifeline System Interdependencies after the 27 February 2010 Mw 8.8 Offshore Maule, Chile, Earthquake

Data on lifeline system service restoration is seldom exploited for the calibration of performance prediction models or for response comparisons across systems and events. This study explores utility restoration curves after the 2010 Chilean earthquake through a time series method to quantify coupling strengths across lifeline systems. When consistent with field information, cross-correlations from restoration curves without significant lag times quantify operational interdependence, whereas those with significant lags reveal logistical interdependence. Synthesized coupling strengths are also proposed to incorporate cross-correlations and lag times at once. In the Chilean earthquake, coupling across fixed and mobile phones was the strongest per region followed by coupling within and across telecommunication and power systems in adjacent regions. Unapparent couplings were also revealed among telecommunication and power systems with water networks. The proposed methodology can steer new protocols for post-disaster data collection, including anecdotal information to evaluate causality, and inform infrastructure interdependence effect prediction models

Multiple-input soft-switching power converters

A multiple-input power converter transferring energy from multiple input sources to a load comprises a plurality of voltage inputs. The power converter implements soft-switching techniques thereby reducing the converter switching losses and increasing the converter efficiency while using fewer components than presently designed multiple-input power converters. Such a power converter may include multiple input sources, where serially connected switches are coupled to one of the multiple input sources in an input leg. A voltage blocking capacitor is inserted between these input legs. Furthermore, the power converter includes a transformer for isolating the load from the multiple input sources, where the voltage blocking capacitor is connected to the primary winding of the transformer.Board of Regents, University of Texas Syste

Cross-Layer Resource Allocation Protocols for Multimedia CDMA Networks

The design of mechanisms to efficiently allow many users to maintain simultaneous communications while sharing the same transmission medium is a crucial step during a wireless network design. The resource allocation process needs to meet numerous requirements that are sometimes conflicting, such as high efficiency, network utilization and flexibility and good communication quality. Due to limited resources, wireless cellular networks are normally seen as having some limit on the network capacity, in terms of the maximum number of calls that may be supported. Being able to dynamically extend network operation beyond the set limit at the cost of a smooth and small increase in distortion is a valuable and useful idea because it provides the means to flexibly adjust the network to situations where it is more important to service a call rather than to guarantee the best quality. In this thesis we study designs for resource allocation in CDMA networks carrying conversational-type calls. The designs are based on a cross-layer approach where the source encoder, the channel encoder and, in some cases, the processing gains are adapted. The primary focus of the study is on optimally multiplexing multimedia sources. Therefore, we study optimal resource allocation to resolve interference-generated congestion for an arbitrary set of real-time variable-rate source encoders in a multimedia CDMA network. Importantly, we show that the problem could be viewed as the one of statistical multiplexing in source-adapted multimedia CDMA. We present analysis and optimal solutions for different system setups. The result is a flexible system that sets an efficient tradeoff between end-to-end distortion and number of users. Because in the presented cross-layer designs channel-induced errors are kept at a subjectively acceptable level, the proposed designs are able to outperform equivalent CDMA systems where capacity is increased in the traditional way, by allowing a reduction in SINR. An important application and part of this study, is the use of the proposed designs to extend operation of the CDMA network beyond a defined congestion operating point. Also, the general framework for statistical multiplexing in CDMA is used to study some issues in integrated real-time/data networks

Weekly Monitoring of Hemodynamic Changes in Diabetic Foot Ulcers

Diabetic foot ulcers (DFUs) affect approximately 25% of the estimated 29.1 million people diagnosed with diabetes. Patients with diabetic foot ulcers report an overall lower quality of life and a 5-year mortality rate of 40%. For doctors treating patients with these ulcers, it is important to evaluate the blood oxygenation in the wound and peri-wound regions, as oxygen is vital for wound healing. DFUs were imaged using a Near Infrared Optical Scanner (NIROS) that utilizes near infrared light at different wavelengths to obtain hemodynamic maps of the wound and peri-wound tissue. DFU patients from Podiatry Care Partners and the University of Miami Wound Care Center were imaged over several weeks. Hemodynamic maps of their wounds were obtained. The hemodynamic maps contain the changes in oxygenated (?HbO) and deoxygenated (?HbR) hemoglobin concentration of the wound and surrounding tissue. Results show that as the wound was healing, wound size became smaller and regions of reduced ?HbO contrast between wound and peri-wound decreased. Increased oxygenation assisted in wound healing, as observed from the non-contact hemodynamic imaging studies of DFUs

Healing Vs. Non- Healing Hemodynamic Differences in Venous Leg Ulcers

Venous leg ulcers (VLUs) account for over 90% of all ulcer cases and it is estimated that ~ 1 in 50 people over the age of 80 are affected. Although the standard for clinical assessment is visual inspection, there is a need to develop a physiological approach that differentiates tissue oxygenation in and around the wound region. Herein, the Optical Imaging Laboratory (OIL) developed a portable, non-contact near-infrared optical scanner (NIROS) for sub-surface imaging of wounds. VLUs were imaged using NIROS on a weekly basis at the University of Miami Wound Care Center and Podiatry Care Partners Clinic. The near infrared images were used to evaluate the oxygenated and deoxygenated hemoglobin maps of the wound and the surrounding tissue. The oxygenation hemoglobin contrast between wound and its surroundings differed between healing and non-healing VLU imaged across weeks. For a healing VLU, as the weeks progressed, there was a positive contrast between wound and background and at week 14 the wound region was not distinguishable from the surrounding tissue. This is possibly due to the physiological similarity in the oxygenation content in the wound and normal tissue, as the wound almost healed. In the non-healing case the oxygenation levels were lower at the wound site most of the weeks, possibly causing it to remain at inflammatory stage and not progress towards healing. Systematic hemodynamic analysis of wounds across weeks of treatment can potentially predict healing sooner than visual decrease in wound size, which is the gold-standard approach to assess healing

Effects of high penetration levels of residential photovoltaic generation

Photovoltaic (PV) generation technologies, often deemed a viable solution for reducing greenhouse gases and decreasing electricity demand, have become increasingly prevalent in their deployment. Particular progress in their implementation has been evidenced in residential areas characterized by rooftop-mounted PV arrays. Aside from the known advantages provided by residential PV generation, one noteworthy disadvantage confronting the electric utility is the degradation of power factor when grid-tied PV sources are extensively integrated into the electrical distribution system. In this paper, the effect of PV sources on power factor is studied using recorded field data from a residential community that is part of a large-scale smart grid demonstration project in Austin, Texas.Center for Electromechanic

KF-Loc: A Kalman Filter and Machine Learning Integrated Localization System Using Consumer-Grade Millimeter-wave Hardware

With the ever-increasing demands of e-commerce, the need for smarter warehousing is increasing exponentially. Such warehouses requires industry automation beyond Industry 4.0. In this work, we use consumer-grade millimeter-wave (mmWave) equipment to enable fast, and low-cost implementation of our localization system. However, the consumer-grade mmWave routers suffer from coarse-grained channel state information due to cost-effective antenna array design limiting the accuracy of localization systems. To address these challenges, we present a Machine Learning (ML) and Kalman Filter (KF) integrated localization system (KF-Loc). The ML model learns the complex wireless features for predicting the static position of the robot. When in dynamic motion, the static ML estimates suffer from position mispredictions, resulting in loss of accuracy. To overcome the loss in accuracy, we design and integrate a KF that learns the dynamics of the robot motion to provide highly accurate tracking. Our system achieves centimeter-level accuracy for the two aisles with RMSE of 0.35m and 0.37m, respectively. Further, compared with ML only localization systems, we achieve a significant reduction in RMSE by 28.5% and 54.3% within the two aisles

Autonomous Vehicles and Machines Conference, at IS&T Electronic Imaging

The performance of autonomous agents in both commercial and consumer applications increases along with their situational awareness. Tasks such as obstacle avoidance, agent to agent interaction, and path planning are directly dependent upon their ability to convert sensor readings into scene understanding. Central to this is the ability to detect and recognize objects. Many object detection methodologies operate on a single modality such as vision or LiDAR. Camera-based object detection models benefit from an abundance of feature-rich information for classifying different types of objects. LiDAR-based object detection models use sparse point clouds, where each point contains accurate 3D position of object surfaces. Camera-based methods lack accurate object to lens distance measurements, while LiDAR-based methods lack dense feature-rich details. By utilizing information from both camera and LiDAR sensors, advanced object detection and identification is possible. In this work, we introduce a deep learning framework for fusing these modalities and produce a robust real-time 3D bounding box object detection network. We demonstrate qualitative and quantitative analysis of the proposed fusion model on the popular KITTI dataset