Rapid Sensing of Hidden Objects and Defects Using a Single-Pixel Diffractive Terahertz Processor (Case No. 2023-184)
Inspecting hidden structures is a critical requirement in various fields, including security, manufacturing, and medicine. Terahertz-based, non-invasive systems show promise towards this end; they penetrate most opaque materials and can identify their internal structural makeups. While terahertz systems have been successfully employed in quality control applications, existing systems are slow and offer relatively low signal-to-noise ratios. Optical processes that speed up object detection require bulky and expensive lasers to match the required efficiencies. Overall, the types of information that can be acquired by current systems are limited, and time and frequency-resolved data are not provided. Aside from detection limitations, feature identification is bottlenecked by the large amount of digital storage, data, and image processing needed for existing terahertz imaging systems.
UCLA researchers have developed an optically diffractive processor that can rapidly inspect a sample using terahertz radiation. Unlike traditional systems, this technology is entirely optical, obviating the need for image transmission, storage, and processing, thereby greatly simplifying and accelerating the defect detection process. Additionally, passive optical components eliminate the need for an external power source, except for the illumination and single-pixel detector. A machine learning algorithm, when coupled with the terahertz radiation, is used to create sample-specific defect detectors which can identify material deviations even when fully obfuscated by an opaque sample. This technology can also be applied to other frequency bands such as infrared and X-ray, further expanding its detection capabilities. Ultimately, this diffractive processor will be useful in fields where high-throughput material screening and inspection is critical. Unlike conventional imaging-based methods, which are often hindered by 3D image data overload, this defect detection approach can deliver markedly higher sensing throughput while offering cost-effectiveness and simplicity.
Potential Applications:
• Security screening.
• Biomedical sensing.
• Industrial quality control.
• Anti-counterfeiting measures.
• Historical artefact preservation.
Advantages:
• Simplified and accelerated defect detection.
• Cost effective.
• Eliminates need for external power source, except for the illumination and single-pixel detector..
• No image processing required.
• No data transmission or storage required.
• Eliminates need for focal plane array and raster scanning.
• Can be applied to any wavelength in electromagnetic spectrum.
Class-Specific Diffractive Cameras with All-Optical Erasure of Undesired Objects (Case No. 2022-287)
There are over 1 billion surveillance cameras around the world today, and the installation of such video surveillance technology will continuously grow due to increasing adoption of automation systems in public and private settings. In this digital era, privacy protection has become a rising problem. Moreover, the demand for digital computing power and data storage space is constantly growing to support the processing of massive amounts of visual data gathered every day. The basic and most commonly adopted image processing methods, such as image blurring, encryption, and deep learning-based algorithms, address privacy concern with software-based techniques. The intrinsic flaw of these techniques is that the sensitive information is only preserved after the recording and transmission of the raw data. Data breaches can still easily occur during image digitization and transmission, especially when captured from remote devices. Another set of solutions are implemented at the hardware level, in which the data processing happens before data transmission. However, they do not completely address the privacy concern, and exposure of confidential information can still happen during the digitization process. There are methods that can enforce privacy before the image digitization, but they are not widely adopted because of poor quality of the generated images, and such methods limit the further application of captured images for downstream tasks, i.e., human pose estimation. There is a growing need for a novel camera design that can generate images with desired quality while ensuring privacy and promoting sustainability.
Professor Ozcan and his research team have invented a novel camera design that can intervene into the light propagation and image formation stage to passively enforce privacy before raw data is recorded. This innovation provides a desired solution to both privacy and environmental concerns. The imaging system is comprised of diffractive layers that are trained using state-of-the-art deep learning techniques and fabricated with a 3D printer. This innovation is able to perform optical mode filtering to accurately form images of the target objects, while simultaneously erasing objects of the irrelevant class at the output field-of-view. Applying the same idea, the design of class-specific permutation cameras is realized successfully to provide all-optical image encryption for the desired objects—providing an extra layer of data security. Additionally, this innovation can easily be scaled to different parts of the electromagnetic spectrum, ranging from the visible to infrared wavelengths, to open up new possibilities for task-specific imaging/video that is privacy-preserving and data-efficient.
Potential Applications:
• Surveillance cameras
• Live streaming camera
• Deployment on self-driving cars
• Image encryption
Advantages:
• Accurately captures target class with high fidelity
• Optically and instantaneously erases irrelevant objects
• Privacy protection
• Saves transmission load and digital storage space
• Transformative nature over the electromagnetic spectrum
• Easy fabrication using 3D-printing
• Environmentally-sustainable methodology
UWHear: Through-Wall Extraction and Separation of Audio Vibrations Using Wireless Signals (Case No. 2022-118)
Audio recordings have become a powerful tool for a wide array of technological applications such as smart devices that use audio inputs for commands. Most devices use microphones to record the audio information, which works well when audio input is clear and distinct from background noise. While microphones have been the standard source of detecting audio signals, they may not be the ideal device since they have difficulty working when the audio source is not in a direct path with the microphone. The ability to unmix multiple sounds and separate them by source is essential for the development of these technologies. Recent research has suggested that radio waves can be used to detect sound directly via their vibrations, propagate through light building materials, and distinguish the source of audio signals with decent spatial resolution. There is a clear and pressing need for the development of radio-based audio sensing technology since these audio sensors would provide rich information that is essential for improving smart devices.
Researchers at UCLA have developed UWHear: a system that uses impulse radio ultra-wideband technology to record audio signals. The system is capable of detecting and distinguishing sound sources that are placed only 25cm apart and is robust to background noise. UWHear is capable of working through light building materials like walls without the need for line of sight. This innovation could be a solution for the development of future smart devices that need to separate signal from noise with the ability to work through solid structures.
Potential Applications:
• Audio Sensing
• Smart Devices
Advantages:
• Background noise invariant
• Unmixing of sound sources
• Works through walls
• Doesn’t require line of sight
Real-time, Passive Non-Line-of-Sight Imaging with Thermal Camera by Exploiting Bidirectional Reflectance Distribution Function (Case No. 2019-642)
Non-line-of-sight (NLOS) imaging provides the ability to look around corners and has gained significant interest in recent years. Current NLOS technologies utilizes visible light, recovering hidden 2D images with a limited field of view, dependent on the imaged geometries and on sight and the lighting conditions. A more potent NLOS imaging system is urgently needed to advance many of the current search and navigation technologies.
UCLA researchers led by Professor Achuta Kadambi, in collaboration with MIT Media lab, have developed a Non-line-of-sight Imaging system using low cost thermal cameras to exploit bidirectional reflectance distribution function (BRDF) of a corner wall. Passive 3D recovery of NLOS heat source (i.e. object of interest) was achieved for the fist time. The integration of thermal cameras utilizes the unique BRDF properties of the long-wave IR, granting both technical advantages such as geometry independence and robustness under ambient light, and foreseeable commercial values for a massive upscale.
Potential Applications:
- Autonomous vehicle navigation
- Military object detection
- Search-and-rescue operations Robotics
Advantages:
- Improved signal-to-noise ratio
- Lower cost
- Larger field of view
- Enhanced depth of resolution
- Scene geometry independent
- Robust under ambient light