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Browse Category: Security and Defense > Screening/Imaging


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Sub-Carrier Successive-Approximation Mm-Wave Radar For High-Resolution 3D Imaging

UCLA researchers in the Department of Electrical Engineering have developed a sub-carrier successive approximation radar (SAR) system with a sufficiently high accuracy to capture three-dimensional images of objects concealed either under the clothing of a person, or within small packages. 

Novel Applicator Using FTA Paper to Collect Touch DNA

Researchers at the University of California, Davis have developed a novel approach to an applicator designed to expedite and increase the efficiency of the DNA collection process at crime scenes.

External Cavity Laser Based Upon Metasurfaces

UCLA researchers in the Department of Electrical Engineering have developed a novel approach for terahertz (THz) quantum-cascade (QC) lasers to achieve scalable output power, high quality diffraction limited, and directive output beams.

Forest Convolutional Neural Networks

In machine learning, a convolutional neural network (CNN) is a type of feed-forward artificial neural network where the individual neurons are tiled in such a way that they respond to overlapping regions in the visual field. They are widely used to model image and video recognition, being a powerful tool for different vision problems. Compared to other image classification algorithms, convolutional neural networks use relatively little pre-processing. This means that the network is responsible for learning the filters that in traditional algorithms were hand-engineered. Despite major reductions in error, current implementations of CNN models still leave significant room for improvement due to the lack of transparency and flexibility in architecture design.

Facial Recognition & Vehicle Logo Super-Resolution System

Background: The video surveillance market is projected to grow annually at 17% and reach $42B by 2020. Video surveillance is a popular tool to track and monitor movement of people and vehicles to provide protection and discover information for investigations. Current technologies are competent in capturing images but not with high definition. Therefore, a more advanced security system that is smarter and multidimensional is needed.  Brief Description: UCR Researchers have developed a novel method and system for unified face representation for individual recognition in surveillance videos along with vehicle recognition. They extracted facial images from a video, generated an emotion avatar image (EAI) and computed features using their innovative algorithms. Low-resolution vehicle images can also be enhanced by using their super-resolution algorithms to produce high-resolution images. Existing technologies can only take frontal images but this new technology can handle out-of-plane, rotated images.

Faces: Art, And Computerized Evaluation Systems-A Feasibility Study Of The Application Of Face Recognition Technology To Works Of Portrait Art

Background: Portraits are not just forms of art; they usually identify important people and the artistic styles of that era. Currently, face recognition technologies for portraits do not exist and therefore, many great pieces in museums remain unidentified. Curators spend an excruciating amount of time, energy and already limited resources to identify paintings. A computer program that helps answer these questions will be beneficial not only for art identification-sakes but to discover the historical stories behind unknown paintings.  Brief Description: UCR researchers have developed a novel computerized system for identifying artists and artists’ styles. First, they fed known portraits into their algorithm for face recognition system training. Then, the Portrait Feature Space (PFS) feature analyzes the unknown portrait and looks for a match in the system. The system is able to learn artistic conventions, such as variation in brush strokes and facial proportion metrics, to compute a similarity score. Identity verification is a 2-step process where style modeling results in assigning the unknown portrait to a particular artist, then further authentication through analysis with known sitters.

A Video Based Hierarchical Vehicle Classification System

Background: Transportation and vehicle classification systems are becoming smarter and more automated. For example, electronic toll collection systems have been introduced and drivers are not required to stop, eliminating road delays. New technologies have also been added to these systems that enable service providers to acquire data on what type of vehicles are utilizing their amenities as well as vehicle identification for safety & control purposes.  Brief Description: UCR Researchers have developed a method and system for vehicle classification using video imaging. This novel invention entails a vehicle ground clearance measurement system along with a video camera that captures a travelling vehicle and categorizes it into a vehicle class. The cameras on current methods and systems rely on side views of the vehicle, which can easily be obstructed by other vehicles.

An Ultra-Sensitive Method for Detecting Molecules

To-date, plasmon detection methods have been utilized in the life sciences, electrochemistry, chemical vapor detection, and food safety. While passive surface plasmon resonators have lead to high-sensitivity detection in real time without further contaminating the environment with labels. Unfortunately, because these systems are passively excited, they are intrinsically limited by a loss of metal, which leads to decreased sensitivity. Researchers at the University of California, Berkeley have developed a novel method to detect distinct molecules in air under normal conditions to achieve sub-parts per billion detection limits, the lowest limit reported. This device can be used detecting a wide array of molecules including explosives or bio molecular diagnostics utilizing the first instance of active plasmon sensor, free of metal losses and operating deep below the diffraction limit for visible light.  This novel detection method has been shown to have superior performance than monitoring the wavelength shift, which is widely used in passive surface plasmon sensors. 

Highly Accurate Occupancy Estimation Using RF Signals and Wi-Fi

A framework that counts the number of people in an area based on RF signals and a Wi-Fi card or network. 

Distributed Scalable Interaction Paradigm for Multi-User Interaction Across Tiled Multi-Displays

The technology is a method for multiple users to interact simultaneously with multiple tiled displays.Under this technology, multiple users are allowed to interact with a tiled display with a distributed registration technique.It features easy scalability across different applications, modalities and users and user interactions involve hand gestures or are laser-based.

Image Filtering Algorithm for Enhanced Noise Removal and Feature Preservation

UCLA researchers in the Department of Chemistry & Biochemistry have developed a novel image filtering algorithm that removes image noise while preserving image features with unprecedented fidelity.   

Crystal Laser Wakefield Accelerator and Its Applications

The technology is a development of a more efficient particle accelerator in terms of energy, cost and space considerations. It is used in particle acceleration applications (cancer treatment, manufacture of components for electronic devices, etc.) The technology is an ultra-compact particle accelerator and particle source. The properties include: Laser Wakefield Accelerator in a solid medium, i.e. crystal in which the Laser Wakefield by charged particle beam bunch. The driver is a high intensity pulsed x-ray. The technology applicable to electron, proton, and ion acceleration and can be used for ultra-compact particle source (neutrons, muons, and neutrinos)

A Neuromorphic Robot that Interacts with People Through Tactile Sensing and Bi-directional Learning

The device is an interactive neuromorphic robot, using to mimic neuro-biological architectures and learning.Properties include:a spiking neural network to control robot behavior, inexpensive parts which are easily available, and two-way learning and behavior shaping.The technology is autonomous, highly mobile, and includes on-board measurement equipment.

Cacophony: A Framework for Next Generation Software Sensors

The technology is a software architecture for providing robust predictions for software systems from cloud sourced data points. Properties include:the ability to “wrap” existing software sensors with additional services. The technology is used by executing software on a cloud based server and manipulating data points from user update systems, such as Waze, and provide predictive services around these data points.

Low-Duty-Cycle Continuous-Wave Photoconductive Terahertz Imaging and Spectroscopy Systems

Professor Mona Jarrahi in the UCLA Department of Electrical Engineering has developed a technique for operating continuous-wave (CW) terahertz imaging and spectroscopy systems based on photoconductive terahertz sources and/or detectors that uses a low-duty-cycle optical pump, achieving high radiation powers and detection sensitivities without causing thermal breakdown, as well as higher quality image and spectra data.

Sensor-Assisted Facial Authentication System For Smartphones

Researchers at the University of California, Davis have developed a method using standard mobile device sensors assisting with facial authentication to overcome the limitations faced by current methods.

Superhydrophobic Induced High Numerical Plastic Lenses

The application of novel manufacturing techniques, chemical modifications and alternative materials produces the next generation of lenses. These lenses are inexpensive, contain improved numerical aperture and can be easily manufactured. Overall, these improvements create new applications for miniaturized optical and optical electronic devices.

System And Method For Capturing Vital Vascular Fingerprint

Improved reliability of fingerprint authentication is achieved through a unique vascular fingerprint which increases accuracy and verifies liveness.

Automatic Facial Expression Recognition System Using Emotion Avatar

Current facial recognition techniques are limited to analyzing the spatial and temporal information for every single frame of video.  The inherent challenge for facial expression recognition and predicting human emotion is the dilemma between rigid motion of the head pose and the non-rigid motion of facial muscles.  Current technology has a credible capacity to estimate head pose, however, difficulty arises estimating non-rigid motion of facial muscles with issues such as non-rigid morphing and person specific appearance.        

Self-Calibrating Micro-Fabricated Resonant Load Cells

The technology is a cost-efficient and highly sensitive micro-mechanical test frames for the characterization of small-scale materials and structures. It is designed for a manufacturing process and self-calibration procedure for the practical use of MEMS resonant sensors as ultra-sensitive load cells. The properties of the technology include:cost-effective fabrication and implementation, load cells with unprecedented combinations of resolution and range, the ability for load cells to be mounted on hybrid micro-mechanical test frames or integrated with on-chip actuators, and the calibration involves no external instrumentation.

Chip-Based Droplet Sorting

Microfluidic devices are poised to revolutionize environmental, chemical, biological, medical and pharmaceutical detectors and diagnostics. The term “microfluidic devices” loosely describes the new generation of instruments that mix, react, count, fractionate, detect, and characterize samples in a micro-electro-mechanical system (MEMS) circuit manufactured through standard semiconductor lithography techniques. Although a wide array of microfluidic technologies are currently available, novel MEMS fluidic systems are needed as scientists continue to work with smaller sample volumes and desire devices with increased sensitivity and effectiveness. Researchers at the University of California, Irvine have developed a unique non-contact system for sorting monodisperse water-in-oil emulsion droplets in a microfluidic device. The technology can be coupled to other on-chip processes to increase device efficiency by sorting out un-reacted droplets.

Novel Imaging Technique Combines Optical and MR Imaging Systems To Obtain High Resolution Optical Images

Researchers at the University of California, Irvine have developed a novel high resolution imaging technique, referred to as Photo-Magnetic Imaging (PMI), that combines the abilities of optical and magnetic resonance (MR) imaging systems. Images are created with PMI by heating tissue with a light (e.g. laser) and measuring the resulting temperature change with MR Thermometry. This change in temperature can then be related to a tissue’s absorption, scattering, and metabolic properties. PMI addresses the limitations of current optical imaging techniques by providing a repeatable, non-contact, high resolution optical image with increased quantitative accuracy. This technique can be used for a wide-range of applications including but not limited to imaging of small animals for research purposes. This technique may also be used in imaging the tissue and organs of a patient.

Decoding Heard Speech And Imagined Speech From Human Brain Signals

Thousands of severely disabled patients are unable to communicate due to paralysis, locked-in syndrome, Lou Gehrig’s disease, or other neurological disease. Restoring communication in these patients have proven a major challenge. Prosthetic devices that are operated by electrical signals measured by sensors implanted in the brain are being developed in an effort to address this problem.  Investigators at University of California at Berkeley have responded to this challenge by developing an algorithm to decode speech, including arbitrary words and sentences, using brain recordings from the human cortex.  A computational model is trained and determines how recorded electrical signals at specific brain sites represent different speech features, for example acoustic frequencies.  The trained model then takes as input novel brain recordings and outputs a set of predicted speech features.  Once these steps are accomplished, speech sounds are either directly synthesized or words are identified from the predicted speech features using statistical techniques.  The brain signal decoding algorithm can decode speech solely from brain signals and may permit communication via thought alone.   

Automated Facial Action Coding System

Brief description not available

A Rapid Method To Measure Cyanide In Biological Samples In The Field

Cyanide is a highly toxic and rapidly acting poison that is infamous due to its use in murders, suicides, wars and attempted genocide.In the present day, cyanide may be responsible for up to 10,000 deaths annually in the United States due to smoke inhalation.Cyanide may also be used as a terrorist weapon. Prior methods to measure cyanide in the blood have involved acidifying the blood after lysis of red blood cells.However, this method is time consuming (takes at least a few hours) and tedious, and thus, inadequate for rapid detection of cyanide toxicity in field or hospital settings.Field or laboratory devices capable of rapidly measuring cyanide levels in blood or body fluids are not currently available, however such field or laboratory devices would be highly useful. Researchers at the University of California, Irvine have developed a method to rapidly measure cyanide in biological samples, which can be carried out in field settings.This method is based on measuring cyanide based on spectral changes that occur when cyanide binds to the reagent.Advantages of this method are its ease of use, stability, and applicability across a wide range of cyanide concentrations and may be used with ease in the field or on laboratory devices.

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