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As industry 4.0 pushes the limits of micro and nano-scale technologies, semiconductor, GPU, and robotics manufacturers are searching for ways to optimize their production lines while still maintaining the highest level of quality. Visual inspection of these advanced micro and nano-scale technologies requires remarkably high levels of precision and control. The piezoelectric actuators used for metrology are currently burdened by non-linearities that require slow and expensive internal closed-loop controllers to deliver sufficient precision to the imaging system. A UMass Amherst research team has developed a new control method that reduces the cost and complexity of high-precision imaging systems while still delivering rapid acquisition of clear and crisp images. The new method integrates the focus measurement and the troublesome non-linear effects in a single learning-based model. The method involves evaluating the focus from a short sequence of images in a deep learning-based control model to determine the optimal position for the lens. The technology leverages Long Short-Term Memory (LSTM) because of its superior ability to draw inferences from learned time sequence data. This novel method also utilizes an optimized backpropagation algorithm for efficiency, as well as a unique S-curve control input profile to minimize motor and image jerks. This method supports both rapid and stable dynamic lens transitions for a wide variety of imaging applications. Compared with the leading autofocus technologies, this method demonstrates significant advantages regarding autofocus time.  

This invention provides a nature-inspired, multilayer photothermal textile and personal heated wearables, such as clothing, comprising such textile for highly efficient thermoregulation and personal thermal management.

The cache plays a key role in determining the performance of applications, no matter for sequential or concurrent programs on homogeneous and heterogeneous architecture. Therefore, it is important to locate and differentiate cache misses accurately. This invention provides a first unified profiling tool–CachePerf–that can correctly and efficiently identify different types of cache misses while imposing reasonable overhead, differentiate issues of allocators from those of applications, and exclude minor issues without much performance impact.

Today’s computers have multiple processing cores to improve performance; however, a bottleneck arises in multi-core processors where there is only one memory controller, as each processor would need to be able to access the same memory. The Non-Uniform Memory Access computing architecture, or NUMA, addresses this bottleneck by giving a separate memory controller to each processing core. Those computing nodes are connected, and memory allocator software dictates how the system allocates resources to computing tasks. Current memory allocators have substantial drawbacks for tasks that require a large amount of remote accesses, have load imbalance among memory controllers, or have interconnect congestion.

 

The inventors have created a patent pending NUMA memory allocator called NUMAlloc that solves the above memory management problems through four innovations: 1) Binding-based memory management; 2) Interleaved heap; 3) Huge page support; 4) Reduced overhead of migrating objects among freelists. These innovations result in a drop-in, scalable memory allocator that compared to the state-of-the-art allocator speeds computing performance by an average of 13% and speeds up to about 5x for memory intensive applications.

Character rigging is the process where animators fit a skeleton to a 3D model, which then allows the animators to manipulate the movement of, or animate, the model. Currently, animators must manually define the skeleton’s joints, how they’re connected, and how the model’s body parts move. This is a very time intensive process that can take hours or days just for a single character. With the rapid rise in need for animation-ready characters and avatars in the areas of games, films, mixed reality and social media, character rigging is presently a major bottleneck to scaling the creation of animated characters.

 

In this work, the authors have written software called RigNet, which is an end-to-end automated method for producing animation rigs from input character models. By automating character rigging through RigNet, the character rigging process time is reduced from hours to minutes. RigNet is based on a deep learning architecture, trained on a large and diverse collection of rigged models, including their mesh, skeletons and corresponding skin weights. RigNet is able to predict both a unique skeleton and a skinning that match animator expectations, which is in contrast to prior art methods that just fit pre-defined, template skeletons to the 3D models that aren’t high enough quality. Finally, because all that is needed to use RigNet is a 3D model, animators do not need to be trained experts to rig characters, as is the case today.

With the ubiquity of software-driven products and services, it is critical that decisions made by software, such as loan approvals and employee screening, be fair and nondiscriminatory. However, current methods and software packages for testing for discrimination in software are both difficult to implement and ineffective at detecting causal discrimination.

 

To better address discrimination detection in software, Professors Yuriy Brun and Alexandra Meliou from the College of Information and Computer Science have developed a technique and its software implementation called Themis. To use Themis, the user provides: the software to be studied for discrimination, a desired confidence level and error bound, and a schema giving the format for valid inputs. Themis will then generate a test suite that will test the software for group and causal discrimination and provide scoring for each. Themis has been tested on 20 software packages and was shown to be effective at detecting discrimination and was able to detect instances of discrimination in software designed to be anti-discriminatory. More broadly, Themis may be applied to measure causal relationships between inputs and outputs in software.

Advances in RFID technology are opening up a myriad of commercial applications related to identifying and interacting with objects, from home automation and health and wellness to augmented reality and tele-rehabilitation. Passive UHF RFID readers are a particularly attractive option due to their low cost and no maintenance; however, their limited range necessitates the use of many readers to cover a single large room, an expensive and labor-intensive process.

This invention, known as WearID, overcomes the traditional limitations of UHF RFID readers through end-to-end design innovation, optimizing the wearable reader for low power, form-factor, and performance. WearID is able to detect grasping, releasing, touching, and passing near tagged objects.

Though many paradigms have been developed to study multitasking using eye tracking, most traditional applications of eye tracking are not used in real time, but instead to augment training or simply to observe optimal strategies. As eye tracking methods become more popular, they have been applied in the field of human-computer interaction and usability, as well as human-robot interaction. Recent real-time eye tracking assistance systems have focused on specific domains such as training, evaluation, or basic hypothesis testing in areas such as medical imaging, security, and aviation.

 

EyeFrame successfully addresses the need for domain-general multitasking assistance. EyeFrame allows the user to program sensory cues on a screen display which direct the user’s attention to neglected areas of the screen. These neglected areas are determined from real-time attention data gathered by an input device, such as a mouse or an eye tracker. EyeFrame is an assistive system for managing multiple visual tasks, which is domain-general, transparent, intuitive, non-interfering, non-command, improves control (without replacing direct control), and adaptively extrapolates to a variety of circumstances.

A new fully asymmetric backscatter communication, which allows for battery-less sensors and readers, protocol where nodes blindly transmit data as and when they sense. This model enables fully flexible node designs, from extraordinarily power efficient backscatter radios that consume barely a few micro-watts to high-throughput radios that can stream at hundreds of Kbps while consuming a paltry tens of micro-watts.

The Frequency-Shifted (FS) Backscatter invention promotes practical backscatter communication for ultra-low power on-body sensors by leveraging radios on existing smart phones and wearables. This invention addresses the self-interference from the wireless carrier without relying on built-in capability to cancel or reject the carrier interference.  Utilizing this invention, the tag shifts the carrier signal to an adjacent non-overlapping frequency band and isolates the spectrum of the backscatter signal from the spectrum of the primary signal to enable more robust decoding.