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Blockchain Protocols for Advancements in Throughput, Fault-Tolerance, and Scalability

Researchers at the University of California, Davis have developed several blockchain paradigms that provide new approaches and expand on existing protocols to improve performance in large-scale blockchain implementations.

In-Sensor Hardware-Software Co-Design Methodology of the Hall Effect Sensors to Prevent and Contain the EMI Spoofing Attacks

Researchers at UCI have developed a novel co-design methodology of hardware-software architecture used for protecting Hall sensors found in autonomous vehicles, smart grids, industrial plants, etc…, against spoofing attacks.There are currently no comprehensive measures in place to protecting Hall sensors.

(SD2020-340) Algorithm-Hardware Co-Optimization For Efficient High-Dimensional Computing

With the emergence of the Internet of Things (IoT), many applications run machine learning algorithms to perform cognitive tasks. The learning algorithms have been shown effectiveness for many tasks, e.g., object tracking, speech recognition, image classification, etc. However, since sensory and embedded devices are generating massive data streams, it poses huge technical challenges due to limited device resources. For example, although Deep Neural Networks (DNNs) such as AlexNet and GoogleNet have provided high classification accuracy for complex image classification tasks, their high computational complexity and memory requirement hinder usability to a broad variety of real-life (embedded) applications where the device resources and power budget is limited. Furthermore, in IoT systems, sending all the data to the powerful computing environment, e.g., cloud, cannot guarantee scalability and real-time response. It is also often undesirable due to privacy and security concerns. Thus, we need alternative computing methods that can run the large amount of data at least partly on the less-powerful IoT devices. Brain-inspired Hyperdimensional (HD) computing has been proposed as the alternative computing method that processes the cognitive tasks in a more light-weight way.  The HD computing is developed based on the fact that brains compute with patterns of neural activity which are not readily associated with numerical numbers. Recent research instead have utilized high dimension vectors (e.g., more than a thousand dimension), called hypervectors, to represent the neural activities, and showed successful progress for many cognitive tasks such as activity recognition, object recognition, language recognition, and bio-signal classification. 

(SD2019-340) Collaborative High-Dimensional Computing

Internet of Things ( IoT ) applications often analyze collected data using machine learning algorithms. As the amount of the data keeps increasing, many applications send the data to powerful systems, e.g., data centers, to run the learning algorithms . On the one hand, sending the original data is not desirable due to privacy and security concerns.On the other hand, many machine learning models may require unencrypted ( plaintext ) data, e.g., original images , to train models and perform inference . When offloading theses computation tasks, sensitive information may be exposed to the untrustworthy cloud system which is susceptible to internal and external attacks . In many IoT systems , the learning procedure should be performed with the data that is held by a large number of user devices at the edge of Internet . These users may be unwilling to share the original data with the cloud and other users if security concerns cannot be addressed.

DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM

UCLA researchers in the Department of Mathematicshave developed a method to maintain data privacy.

Lambda-Reservoir Computing

UCLA researchers in the Department of Electrical and Computer Engineering have developed a Spectral Reservoir Computer that processes data using nonlinear optical interactions.

Techniques for Creation and Insertion of Test Points for Malicious Circuitry Detection

Researchers led by Dr. Potkonjak from the UCLA Department of Computer Science have developed a technique to detect hardware Trojans in integrated circuits.

System For Eliminating Clickbaiters On Visual-Centric Social Media

Researchers from the Department of Communication at UCLA have developed a system for identifying and eliminating clickbait from social media.

Privacy Preserving Stream Analytics

UCLA researchers in the Department of Computer Science have developed a new privacy preserving mechanism for stream analytics.

DeepSign: Digital Rights Management in Deep Learning Models

As society becomes more and more complicated, we have also developed ways to analyze and solve some of these complexities via the convergence of the fields of artificial intelligence, cognitive science and neuroscience. What has emerged is the development of machine learning, which allows computers to improve automatically through experience. Thus, developers working on artificial intelligence (AI) systems have come forth to align AI with machine-learning algorithms to cover a wide variety of machine-learning problems. The most advanced of these are called supervised learning methods which form their predictions via learned mapping, which can include decision trees, logistic regression, support vector machines, neural networks and Bayesian classifiers. More recently, deep networks have emerged as multilayer networks involved in a number of applications, such as computer vision and speech recognition. A practical concern in the rush to adopt AI as a service is the capability to perform model protection: AI models are usually trained by allocating significant computational resources to process massive amounts of training data. The built models are therefore considered as the owner’s intellectual property (IP) and need to be protected to preserve the competitive advantage.

Librando: Transparent Code Randomization For Just-In-Time Compilers

Just-in-time compilation is a method of executing computer code which, while boasting superior execution times, is prone to security exploits. UCI researchers have developed librando, a software framework for increasing security for just-in-time compilers, ensuring that generated program code is not predictable to an attacker.

Value-Based Information Flow Tracking in Software Packages

A collaboration between UCLA and Rutgers have developed a novel information flow tracking technique to detect potential data leaks in mobile devices.

A Technique For Securing Key-Value Stores Against Malicious Servers

The advent of the Internet of Things (IoT) has drastically increased the potential scale and scope of destruction hackers can cause. Cloud servers now control and monitor devices such as cars, smart home controls, fitness trackers, medical monitoring systems. These cloud-based devices are at risk, however, in that if they become compromised, third parties could gain full control of all devices and stored information associated with that server. UCI researchers have developed the FIDELIUS system, a technique for secure communication and information storage.

Defending Side Channel Attack In Additive Layer Manufacturing Systems

Additive layer manufacturing systems, also known as 3D printers, are a powerful tool for manufacturers in both rapid prototyping stage and full-scale production. Sensitive intellectual property is carried in the electronic information of the design files utilized by 3D printers. However, the physical characteristics of the machine in operation, including power, temperature, sounds, and motion can also reveal sensitive information that could be used to reverse-engineer a product. The inventors at UCI have demonstrated the threat posed by such side-channel attacks, and have developed countermeasures that obscure information which would otherwise be exposed during printer operation.

Mechanical Process For Creating Particles Using Two Plates

UCLA researchers in the Department of Chemistry and Biochemistry & Physics and Astronomy have developed a novel method to lithograph two polished solid surfaces by using a simple mechanical alignment jig with piezoelectric control and a method of pressing them together and solidifying a material.

Synthesis Technique to Achieve High-Anisotropy FeNi

Researchers at the University of California, Davis have developed an innovative synthesis approach to achieve high anisotropy L1 FeNi by combining physical vapor deposition and a high speed rapid thermal annealing (RTA).

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.

Microfabrication of High Quality 3-D Structures Using Wafer-Level Glassblowing of Fused Quartz and Ultra Low Expansion Glasses

Micro-glassblowing MEMS fabrication process for low expansion and low loss materials

Referenceless Clock Recovery Circuit with Wide Frequency Acquisition Range

The technology is a circuit that recovers a full-rate clock signal from a random digital data signal. Properties include: achieves frequency and phase locking in a single loop and a wide acquisition range.

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.

Cost-Efficient Repair For Cloud Storage Systems Using Progressive Engagement

The technology is a coding process which facilitates efficient data failure recovery in cloud storage systems.It features greater flexibility in choosing subset of storage nodes for recovery and reduces amount of data that must be transferred upon recovery.

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.

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