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Autonomous Comfort Systems Via An Infrared-Fused Vision-Driven Robotic Systems

Robotic comfort systems have been developed which use fans to deliver heated/cooling air to building occupants to provide greater levels of personal comfort.  However, current robotic systems rely on surveys asking individuals about their comfort state through a web interface or app.  This reliance on user feedback becomes impractical due to survey fatigue on the part of the user.  Researchers at the University of California, Berkeley have developed a system which uses a visible light camera located on the nozzle of a robotic fan to detect human facial features (e.g., eyes, nose, and lips).  Images from a co-located thermal camera are then registered onto the visible light image and temperatures of different facial features are captured and used to infer the comfort state of the individual.  Accordingly, the fan/heater system blows air with a specific velocity and temperature toward the occupant via a closed-loop feedback control.  Since the system can track a person in an environment, it addresses issues with prior data collection systems that needed occupants to be positioned in a specific location.

Vehicle Make and Model Identification

Prof. Bir Bhanu and his colleagues from the University of California, Riverside have developed a method for  analyzing real-time video feed of vehicles from a rear  view  perspective to identify the make and model of a vehicle. This method works by using a software system for detecting the Regions-of-Interest (ROIs) of moving vehicles and moving shadows, computing structural and other features and using a vehicle make and model database for vehicle identification. The system performs calculations based on factors found in all vehicles, so it is reliable regardless of vehicle color and type. The system is compatible with low resolution video feed, so it is able to analyze video feed in real-time. Thus, this technology holds potential for innovating fields like vehicle surveillance, vehicle security, class-based vehicle tolling, and traffic monitoring where reliable real-time video analysis is needed.  Figure 1: Example of the direct rear view of moving vehicles.  

F5‐HD: Fast Flexible FPGA‐based Framework for Refreshing Hyperdimensional Computing

Hyperdimensional (HD) computing is a novel computational paradigm that emulates the brain functionality in performing cognitive tasks. The underlying computation of HD involves a substantial number of element-wise operations (e.g., addition and multiplications) on ultra-wise hypervectors, in the granularities of as small as a single bit, which can be effectively parallelized and pipelined. In addition, though different HD applications might vary in terms of number of input features and output classes (labels), they generally follow the same computation flow. Such characteristics of HD computing inimitably matches with the intrinsic capabilities of FPGAs, making these devices a unique solution for accelerating these applications.

Multi-Omics CoAnalysis (MOCA) Software

Researchers at the University of California, Riverside have developed a software program named Multi-Omics CoAnalysis (MOCA), which is an integrative, interactive, and informative (i3) workbench. Using MOCA, researchers will be able to statistically analyze and interactively visualize the experimental data and generate the corresponding correlative omics data. Data can be presented in various formats including box plots, line plots, heat maps, volcano plots, principal component analysis, coefficient distribution plot, and network plot with an adjacency matrix. The graphical user-interface (GUI) of MOCA delivers intuitive and interactive data visualizations, and enables access to many types of metadata and experimental data in a user-friendly manner.  Fig 1: MOCA-generated image of a metabolic network in MEP pathway Fig 2: MOCA-generated pattern plot by using machine learning

Mutation Organization Software for Adaptive Laboratory Evolution (ALE) Experimentation

Adaptive Laboratory Evolution (ALE) is a tool for the study of microbial adaptation. The typical execution of an ALE experiment involves cultivating a population of microorganisms in defined conditions (i.e., in a laboratory) for a period of time that enables the selection of improved phenotypes. Standard model organisms, such as Escherichia coli, have proven well suited for ALE studies due to their ease of cultivation and storage, fast reproduction, well known genomes, and clear traceability of mutational events. With the advent of accessible whole genome resequencing, associations can be made between selected phenotypes and genotypic mutations.   A review of ALE methods lists 34 separate ALE studies to date. Each study reports on novel combinations of selection conditions and the resulting microbial adaptive strategies. Large scale analysis of ALE results from such consolidation efforts could be a powerful tool for identifying and understanding novel adaptive mutations. 

Vehicle Logo Identification in Real-Time

Brief description not available

Generating Visual Analytics and Player Statistics for Soccer

Prof. Bhanu and his colleagues from the University of California, Riverside have developed a system to automate the process of player talent identification by performing visual analytics and generating statistics at the match, team and player level for soccer from a video using computer vision and machine learning techniques. This work uses a database of 49,952 images which are annotated into two classes namely: players with the ball and players without the ball. The system can identify which players are controlling the ball. Compared to other state-of-the-art approaches, this technology has demonstrated an accuracy of 86.59% on identifying players controlling the ball and an accuracy of 84.73% in generating the match analytics and player statistics. Figure 1: Visualization of features learned by the system Figure 2: Visualization of gray scale features learned by the system  

Machine Learning Program that Diagnoses Hypoadrenocorticism in Dogs Using Standard Blood Test Results

Researchers at the University of California, Davis have developed a program based on machine learning algorithms to aid in diagnosing hypoadrenocorticism.

Applying a Neural Network Algorithm to Canine Radiographs to Help Detect Left Atrial Enlargement

Researchers at the University of California, Davis have developed a method of detecting canine left atrial enlargement by applying a neural network algorithm to right lateral radiographs.

BioScript: A Programming Language for Microfluidic Devices

Prof. Philip Brisk and his colleagues from the University of California, Riverside have developed a new programming language and tool to design microfluidic (MF) devices. The new presented language, BioScript, offers a user-friendly syntax that reads user input like a cookbook recipe to optimize human readability. The advantage of the BioScript type system is that it ensures that each fluid is never consumed more than once, and that unsafe combinations of chemicals are never mixed on the chip. This result establishes the feasibility of high-level programming language and compiler design for programmable chemistry, and opens up future avenues for research in microfluidic systems. Fig 2: A Laboratory-on-a-Chip (LoC) system  

Computational Image Analysis of Guided Acoustic Waves Enables Rheological Assessment of Sub-Nanoliter Volumes

UCLA researchers in the Department of Electrical and Computer Engineering have developed an image analysis platform to measure the viscosity of nanoliter volume liquids.

IgEvolution: A Novel Tool for Clonal Analysis of Antibody Repertoires

Constructing antibody repertoires is an important error-correcting step in analyzing immunosequencing datasets that is important for reconstructing evolutionary (clonal) development of antibodies. However, the state-of-the-art repertoire construction tools typically miss low-abundance antibodies that often represent internal nodes in clonal trees and are crucially important for clonal tree reconstruction. Thus, although repertoire construction is a prerequisite for follow up clonal tree reconstruction, the existing repertoire reconstruction algorithms are not well suited for this task because they typically miss low-abundance antibodies that often represent internal nodes in clonal trees and are crucially important for clonal tree reconstruction.

Predictive Controller that Optimizes Energy and Water Used to Cool Livestock

Researchers at the University of California, Davis have developed a controller that applies environmental data to optimizing operations of livestock cooling equipment.

Low Complexity Maximum-Likelihood Decoding of Cyclic Codes

UCLA researchers in the Department of Electrical and Computer Engineering have developed a low complexity decoding algorithm of cyclic codes with better performance and lower latency than current approaches.

Method of Reducing Placebo/Nocebo Effects Associated with the Tapering of Medication and Storing Drug Tablet Fragments

UCLA researchers in the Department of Medicine have developed drug tapering schedule software to reduce factors that may impede patients’ discontinuation of a drug.

Deep Learning of Biomimetic Sensorimotor Control for Biomechanical Human Animation

UCLA researchers from the Department of Computer Science have developed a computer simulation model and associated software system for biomimetic human sensorimotor control.

Software for Automated Microfluidic Chip Design

Professor Brisk’s research group at the University of California, Riverside, has developed software to design and analyze an entire microfluidic chip. This is done using Microfluidic Design Automation (MDA) software to synthesize and physically lay out the devices.This software uses Microfluidic  Design  Automation (MDA) to  physically  render chips.  This  approach  is  similar  to  Electronic  Design Automation (EDA) in the semiconductor industry. The  software  automatically creates a chip architecture that is converted to MHDL, a  human-readable microfluidic hardware design language, enabling manual refinement. When  the  chip  designer  is  satisfied  with  the  architecture,  the software  physically  lays  out  the  different  layers  of  the  chip. The  output  is  an  AutoCAD  DXF  (or  other  vector  graphics) file that can be transferred to a foundry for fabrication. Fig. 1 shows a microfluidic device layout designed and laid-out by the UCR software.  

Use of Machine Learning to Predict Non-Diagnostic Home Sleep Apnea Tests

Researchers led by Robert Stretch from the Division of Pulmonary, Critical Care & Sleep Medicine at UCLA have developed an algorithm that can predict whether a patient will have a non-diagnostic home sleep apnea test based upon data from the electronic health record and a brief questionnaire.

Automatic Identification of Ophthalmic Medication for The Visually Impaired

Researchers at UCI are developing technology that allows visually impaired patients to use their smartphones to take pictures of their eye medication/eye drop bottles. The technology will recognize the eye medication and verbally communicate the medication and will audibly confirm the medication along with the instructions on use.

Fast Deep Neural Network (DNN) Training/Execution on Hardware Platforms

With the growing range of applications for Deep Neural Networks (DNNs), the demand for higher accuracy has directly impacted the depth of the state-of-the-art models. Although deeper networks are shown to have higher accuracy, they suffer from drastically long training time and slow convergence speed with high computational complexity.

New Classes Of Cage And Polyhedron And New Classes Of Nanotube And Nanotube With Planar Faces

UCLA researchers have developed a novel algorithm that can be used to design unique self-assembled molecules and nanostructures.

Healthcare Hand Hygiene Medical Training Software

UCI researchers have developed a curriculum for training and evaluation of customers performing Healthcare Hand Hygiene.

Advanced Airway Management: Intubation medical Training Software

UCI researchers have developed a curriculum for training and evaluation of customers performing Advanced Airway Mangement: Intubation.

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