Among the different types of legged robots, hopping robots, aka hoppers, can be classified as one of the simplest sufficient models that capture the important features encompassed in dynamic locomotion: underactuation, compliance, and hybrid features. There is an abundance of work regarding the implementation of highly simplified hopper models, the prevalent example being the spring loaded inverted pendulum (SLIP) model, with the hopes of extracting fundamental control ideas for running and hopping robots. However, real world systems cannot be fully described by such simple models, as real actuators have their own dynamics including additional inertia and non-linear frictional losses. Additionally, implementing feedback control for hopping systems with significant amounts of compliance is difficult as the input variable does not instantaneously change the leg length acceleration. The current state-of-the-art of step length control in the presence of non-steady state motions required for foothold placement is not precise enough for operation in the real world. Therefore, an important step towards demonstrating high controllability and robustness to real-world elements is in providing accurate higher order models of real-world hopper dynamics, along with compatible control strategies.
Our modeling work is based on a series-elastic actuated (SEA) hopping robot prototype constructed by our lab group, and we provide verifying hardware results that high order partial feedback linearization (HOPFL) can be implemented directly on the leg state of the robot. Using HOPFL, we investigate several paths of compatible trajectory generation that can accomplish desirable tasks such as precise foothold planning and apex control. We investigate the practicality of using SLIP-based trajectory generation techniques on more realistic hopping robots, and show that by implementing HOPFL directly on the robot’s leg, we can make use of computationally fast SLIP-based reachability approximations, account for non-trivial pitch dynamics, and improve the state-of-the-art of precision step length control for SEA hoppers. We also consider control strategies towards hoppers for which SLIP-based trajectories may not be compatible, by planning all ground reaction force vector (GRFV) components during the stance phase concurrently, using a lower order and very general model to construct trajectories for the system’s center of mass (CoM), and maintain body stability by controlling the orientation of the GRF directly. While not purely analytical as our SLIP-based approaches, this method is general enough to work on a variety of hopping robots that are not necessarily kinematically structured resembling the classical SLIP model.
This dissertation is concerned with the problem of global optimization of delay constrained communication and control strategies. Specifically, we are interested in obtaining optimal encoder decoder functions that map between the source space and the channel space to minimize a given cost function. The cost surfaces associated with these problems are highly complex and riddled with local minima, rendering gradient descent based methods ineffective. We propose and develop a powerful non-convex optimization method based on the concept of deterministic annealing (DA) — which is derived from information theoretic principles with analogies to statistical physics, and was successfully employed in several problems including vector quantization, classification and regression. DA has several useful properties including reduced sensitivity to initialization and strong potential to avoid poor local minima. We develop DA-based optimization methods for the following fundamental communication problems: the Wyner-Ziv setting where only a decoder has access to side information, the distributed setting where independent encoders transmit over independent channels to a central decoder, and analog multiple descriptions setting which is an extension of the well known source coding problem of multiple descriptions. We present comparative numerical results that show strict superiority of the proposed method over gradient descent based optimization methods as well as prior approaches in literature. We give a detailed analysis of the highly non-trivial structure of obtained mappings.
We also study the related problem of global optimization of controller mappings in decentralized stochastic control problems including Witsenhausen’s celebrated 1968 counter-example. It is well-known that most decentralized control problems do not admit closed-form solutions and require numerical optimization. We develop an optimization method for a class of decentralized stochastic control problems. We present comparative numerical results for two test problems that show strict superiority of the proposed method over prior approaches in literature, and analyze the structure of obtained controller functions.
We compare characteristics of various modulators of light. Included are semiconductor QW’s with band-to-band and intersubband transitions) , graphene, two dimensional materials like MoS2 and polymers. The efficiency enhancement using either micro resonators or plasmonic structures is considered as well. The results indicate that the performance of different modulators depends on the very few characteristics of modulator, essentially on the ratio of absorption cross-section of the modulating medium to the waveguide cross-section and none of the currently fashionable 2D materials offer any meaningful improvement over a simple QW modulator. We also show that electro-optic modulators typically offer lower switching energies than all-optical modulators, but still their performance simply cannot match electronic devices.
College of Engineering video highlights Assistant Professor Yon Visell’s haptics research
Electrical & Computer Engineering Professor Yon Visell’s haptics and robotics research group catalogs patterns of vibration on the skin of the hand that are at the foundation of how we sense the world through touch. Visell’s research is the first of its kind to map the fast propagation of touch, designing custom sensor networks worn on the hand that capture displacements of the skin at a very fast resolution.
3D imaging technologies have significantly improved in recent years. 3D displays that actively or passively provide 3D illusions to the two eyes have already stepped from movie theaters into living rooms and even become portable/wearable; 3D sensing technologies have enabled the acquisition of scene depth in real time on a mobile device, and estimating the geometry of a room can be done in only minutes. Targeting intuitive manipulation of such ubiquitous 3D data, this dissertation is focused on advancing the traditional 2D image segmentation, a core technique in image processing and computer vision, to 3D.
Traditionally, 2D image segmentation has aimed at partitioning an image to non-overlapping pieces where each preserves a certain property, such as consisting of only the pixels belonging to the foreground object. In 3D segmentation, the goal is instead to partition the 3D space into multiple entities. When the input 3D data are captured in a manner of multi-view imagery (e.g., stereoscopic 3D), this requires introducing an additional important property — view consistency. In order to maintain a consistent 3D interpretation, corresponding segments in different views should share the same property (e.g., all the segments belong to the foreground object), considering the fact that they are observations of the same entity in the 3D world.
There are many challenges for view-consistent 3D segmentation: an object can be visible from one viewpoint but occluded from the others; the user guidance, if any, is typically in only one view but not the others; most importantly, the coarse and noisy depth information obtained by modern depth estimation techniques is not sufficient to precisely determine the 3D position of each 2D pixel and equivalently its cross-view correspondence. These all limit the performance of prior 3D segmentation algorithms that attempt to group pixels into consistent segments in different views. In some cases, an explicitly reconstructed 3D geometric model is provided so that its segmentation result can be projected to different observation viewpoints, thus naturally guaranteeing the view consistency. However, these geometric models are typically low quality and/or low resolution, which makes direct segmentation on the models very challenging.
Addressing these issues, this dissertation proposes to integrate the typically low-quality third dimensional information and high-quality 2D images in a global optimization to take advantage of both. The insight here is that powerful 3D geometric constraints and rich 2D image context can complement each other. We show that by following this principle, the proposed algorithms achieve state-of-the-art performance in several applications of 3D image editing and object extraction.
In the field of neuromorphic VLSI connectivity is a huge bottleneck in implementing brain-inspired circuits due to the large number of synapses needed for performing brain-like functions. (E.g. pattern recognition, classification, etc.). In this thesis I have addressed this problem using a two pronged approach namely spatial and temporal.
Spatial: The real-estate occupied by silicon synapses have been an impediment to implementing neuromorphic circuits. In recent years, memristors have emerged as a nano-scale analog synapse. Furthermore, these nano-devices can be integrated on top of CMOS chips enabling the realization of dense neural networks. As a first step in realizing this vision, a programmable CMOS chip enabling direct integration of memristors was realized. In a collaborative MURI project, a CMOS memory platform was designed for the memristive memory array in a hybrid/3D architecture (CMOL architecture) and memristors were successfully integrated on top of it. After demonstrating feasibility of post-CMOS integration of memristors, a second design containing an array of spiking CMOS neurons was designed in a 5mm x 5mm chip in a 180nm CMOS process to explore the role of memristors as synapses in neuromorphic chips.
Temporal: While physical miniaturization by integrating memristors is one facet of realizing area-efficient neural networks, on-chip routing between silicon neurons prevents the complete realization of complex networks containing large number of neurons. A promising solution for the connectivity problem is to employ spatio-temporal coding to encode neuronal information in the time of arrival of the spikes. Temporal codes open up a whole new range of coding schemes which not only are energy efficient (computation with one spike) but also have much larger information capacity than their conventional counterparts. This can result in reducing the number of connections to do similar tasks with traditional rate-based methods.
By choosing an efficient spatio-temporal coding scheme we developed a system architecture by which pattern classification can be done using a “Winners-share-all” instead of a “Winner-takes-all” mechanism. Winner-takes-all limits the code space to the number of output neurons, meaning n output neurons can only classify n pattern. In winners-share-all we exploit the code space provided by the temporal code by training different combination of k out of n neurons to fire together in response to different patterns. Optimal values of k in order to maximize information capacity using n output neurons were theoretically determined and utilized. An unsupervised network of 3 layers was trained to classify 14 patterns of 15 x 15 pixels while using only 6 output neurons to demonstrate the power of the technique. The reduction in the number of output neurons results in the reduction of number of training parameters and results in lower power, area and memory required for the same functionality.
Award recognizes Bowers’s “pioneering research in silicon photonics, including hybrid silicon lasers, photonic integrated circuits and ultra low-loss waveguides”
Bowers, who holds the Fred Kavli Chair in Nanotechnology and is an internationally renowned authority on optoelectronics, has focused his expertise on silicon photonics and optoelectronics, with the goals of developing energy-efficient technology for the next generation of optical networks. “Silicon photonics has the potential to revolutionize photonics and electronics by enabling low-cost, high-volume manufacturing of optical interconnects with a path toward embedding high-capacity fiber optics on circuit boards and eventually on electronic chips.”
“This is a major award,” said Rod Alferness, dean of the UCSB College of Engineering, who received an IEEE Photonics Award in 2005, before coming to UCSB. “The IEEE Photonics Award is the most prestigious recognition of contributions to the field of photonics and optics. John Bowers’ work in integrated silicon photonics is leading the way to the future of electronics and telecommunications.”
Bowers, who came to UCSB in 1987, is a member of the National Academy of Engineering and the National Academy of Inventors, a fellow of the IEEE, Optical Society of America (OSA) and the American Physical Society. He is a recipient of the OSA Holonyak Prize, and the IEEE LEOS William Streifer Award. He and colleagues received the 2007 Annual Creativity in Electronics Award for Most Promising Technology for the hybrid silicon laser.
ECE Professor Kaustav Banerjee’s research group have made pioneering contributions to the domain of contacts and interfaces to 2D semiconductors, which are critical to harnessing their full potential for electronics, optoelectronics, and spintronics applications
ECE Ph.D. candidate Jiahao Kang has been one of the earliest to study and decode the nature of electrical contacts to 2D semiconductors. Recently, one of his early papers published in the journal American Institue of Physics (AIP) | Applied Physics Letters (APL) has been highlighted among the most cited articles of that journal in 2015.
2D materials belonging to the graphene family, various transition metal dichalcogenides including molybdenum disulphide (MoS2) and tungsten diselenide (WSe2), as well as other 2D semiconductors such as monolayer Black Phosphorus have displayed unique potential in overcoming the limitations of conventional bulk materials (such as silicon and III-V semiconductors) for a number of exciting applications in electronics and optoelectronics, as well as spintronics and valleytronics. However, ensuring low-resistance or optimal contacts to such materials is the primary hindrance to using this technology.
In 2015, Jiahao also co-authored a comprehensive review article on contacts to atomically-thin 2D semiconductors in the prestigious journal Nature Materials. His doctoral research is being carried out in the Nanoelectronics Research Lab under the tutelage of Professor Kaustav Banerjee.
With 222,233 citations, APL is the most cited journal in applied physics in 2015.
Freedom Photonics among three Central Coast companies to receive the newest series of United States Department of Energy grants awarded to small businesses to encourage clean energy research and technology development
Freedom Photonics, whose research and production facilities are located in Santa Barbara, prides itself on the business of energy efficiency — through computer communication. Founded in 2005 by electrical engineering PhD’s Leif A. Johansson and Milan L. Mashanovitch, the team of 25 creates photonic integrated circuits, the same technology that allow servers for sites like Google, Facebook, and Amazon to communicate with each other. “Overheating is always an issue when working with so many servers,” said Mashanovitch about the airplane-hanger-sized facilities that house the Internet’s largest. The team plans to use its $1 million grant to fund research on solving such overheating issues and also developing circuits that emit less energy. Through its research, the company continues to pave inroads for hardware used by the Department of Defense, NASA, and private companies touting fiber optic “fencing.”
The $1 million grants were awarded by the Department of Energy to businesses across the United States as part of the 2016 Small Business Innovation Research and Small Business Technology Transfer programs.
Redox-Based Resistive Switching Memories (ReRAM), also called nanoionic memories or memristive elements, are widely considered to provide a potential leap beyond the limits of Flash (with respect to write speed, write energies) and DRAM (with respect to scalability, retention times) as well as energy-efficient approaches to neuromorphic concepts.
In this seminar talk, the ultra-high non-linearity of the switching kinetics of redox-based resistive switching devices will be discussed with an emphasis on the so-called valence change mechanism (VCM) typically encountered as a bipolar switching in metal oxides. The involved electrochemical and physical processes can be either electric field/voltage enhanced or accelerated by a local increase in temperature due to Joule heating. The analysis of the published SET switching kinetics data of VCM-type ReRAM systems showed that their nonlinearity is mainly dominated by temperature-accelerated ion hopping, controlled by the local power during the switching process. The gradual RESET transition can be explained in terms of temperature-accelerated ion movement with counter-acting ion drift and diffusion processes. It will be shown that a designated combination of oxides can significantly improve the long-term kinetics, i.e. the retention time, by tailoring the ion diffusion properties in the oxide layers.