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Research Project #1: Bridge
We have developed an emotion classification system based on vocal features (e.g., pitch, energy, MFCCs). The system uses a Support Vector Machine (SVM) machine learning algorithm to train various classifiers (e.g., the sample is "angry" or "not angry"; it is "happy" or "not happy", etc.) Then, the outputs of these individual classifiers are fused together to obtain the final emotion classification. We are looking for someone to help with the running of experiments for different training models for the SVM. We are also looking for folks to develop Android code for the extraction of some of the voice features and the entire emotion classification system. This project requires Matlab coding experience.
Research Project #2: GENIUS
We have developed a suite of passive wake-up radios to wake up "motes" and save energy. We are continuing to refine our devices and experiment with their abilities. We are looking for someone to aid in the experimentation of the wake-up radios, in the development of new hardware, and in simulations that use the experimental results to create models for the wake-up radios. This project requires the ability to perform experiments and analyze results. Matlab coding experience helpful but not required.
Research Project #3: Field Systems
We are working on a self-powered (via solar cells) video camera network that learns about the energy harvested and available and adjusts the protocols based on the current/predicted future energy capabilities of the node itself and those around it. We have developed some ns2 (simulation) models for the energy harvesting and are looking for students who can run experiments with different models for the energy harvesting. Some coding experience (in C/C++) helpful.
Research Project #4: GEMCloud
We have developed a distributed computing system that uses the free cycles on Android devices (cell phones and tablets) to perform computation for medical research (and other) purposes. We are looking for someone to help with some of the coding of this system for ad hoc networks. Some Java experience helpful.
Our research focuses primarily on understanding the fundamental physics of novel nonlinear optical, quantum optical, and optomechanical phenomena in micro-/nanoscopic photonic structures, and on finding their potential applications towards chip-scale photonic signal processing and sensing, in both classical and quantum regimes.
Research Project #1: Integrated quantum photonics
Advance of quantum optical science in the past few decades has now come to the engineering era of real practical application, which has been witnessed recent years in the areas of secure communication, metrology, sensing, and potentially future advanced computing. Chip-scale implementation would not only dramatically enhance the complexity and capacity of information processing, but also enable novel functionalities which are otherwise inaccessible in room-wide/table-top experiments. Recent advance of integrated quantum photonics has resulted in intriguing powerful functionalities that start to go beyond the reach of classical computing. Indeed, human beings have already experienced a similar technological advance in 50s-60s. The technological transformation of electronic processors from a room-wide size down to a chip scale eventually revolutionized our modern society. Although integrated quantum photonics is only in its infant stage, its future advance may bring similar or even more profound impact to our lives in the years to come. Envisioning such potential far-reaching impact, we are dedicated to exploring and developing chip-scale approaches that are capable of generating, processing, storing, and detecting versatile photonic quantum states on a single chip, aiming for broad applications in computing, communication, and sensing, by taking advantage of the intriguing quantum mechanical principles.
Research Project #2: Quantum Silicon Photonics
We currently focus on silicon platform to develop various quantum photonic devices. Silicon turns out to be an excellent material for this purpose. We have produced photon pairs and heralded single photons with extremely high purity inside high-Q silicon microresonators. By integrating novel physical mechanisms, innovative device design, together with advanced nanofabrication technology, we hope to open up a research avenue towards quantum silicon photonics that will eventually form fundamental building blocks for future quantum photonic interconnect.
Research Project #3: Cavity nano-optomechanics
Sensitive control of mechanical motion at the mesoscopic scale underlies a variety of applications ranging from bio-sensing to signal processing, which has been seen in various micro-/nano-electromechanical devices. Interestingly, mechanical motion can be efficiently manipulated by radiation pressure at the nanoscopic scale, particularly in high-Q micro-/nano-cavities where the photon-phonon interaction can be dramatically enhanced. The resulting strong optomechanical coupling exhibits exceptional capability of motion control down to single quantum level. On one hand, such intriguing capability enables manipulating the photonic and phononic quantum states, thus allowing to explore novel photon-phonon interaction dynamics. On the other hand, it offers a great opportunity for a variety of applications related to sensing and photonic signal processing with unprecedented performance. We currently develop different nano-optomechanical structures (NOMS) for various applications of optical clock, signal processing, and motion sensing.
Research Project #4: Silocon carbide photonics
Silicon carbide (SiC) exhibits superior mechanical and electronic properties which have found broad applications in power electronics and structure mechanics. Although rarely used, SiC is an excellent material candidate for photonic application, with very attractive material characteristics:
We have made the first 3C-SiC microresonator. We are currently developing SiC devices for various nonlinear optical, optomechanical, and quantum optical applications. With our effort, we hope to build a novel SiC photonic platform that is capable of offering diverse functionalities for broad photonic application.
Research Project #5: Integrated nonlinear photonics
Nonlinear optical processes have attracted long-lasting interest ever since the first observation of second-harmonic generation, which have found very broad application ranging from photonic signal processing, tunable coherent radiation, frequency metrology, optical microscopy, to quantum information processing. In general, nonlinear optical effects are fairly weak and have to rely on substantial optical power to support nonlinear wave interaction. However, high-quality nanophotonic devices are able to confine strongly the optical waves into a tiny volume/area with significant optical field inside, resulting in dramatically enhanced nonlinear optical effects to an extent inaccessible in conventional bulk media. On the other hand, operating in the micro-/nano-scopic scale offers unprecedented freedom of versatile device design that enables flexible engineering of device characteristics (such as geometry, dispersion, quality factor, optical/mechanical resonance, etc) for various application purposes. We currently explore new material platforms and innovative device designs for novel nonlinear photonic functionalities with high efficiency, long coherence, broad bandwidth, and/or large tunability.
Projects are underway in a number of areas related to medical imaging, image processing and novel scanning techniques using Doppler shift effects. All of these are built on the fundamentals of wave propagation combined with signal and image processing techniques. Many projects are undertaken in joint facilities of the Department of Electrical and Computer Engineering and the Department of Imaging Sciences at the University of Rochester Medical Center.
Research Project: Sonoelasticity Imaging
This novel hybrid imaging technique that uses Doppler ultrasound to map out, or image, the local vibrations within tissues or structures that are excited by shear wave oscillations at low frequencies (10-1000Hz typically). The concept is that stiff tumors surrounded by soft tissues will present abnormal vibration amplitudes and can therefore be detected. Initial results have been encouraging and applications to the analysis of structures may also be possible. Details are at http://www.ece.rochester.edu/projects/sonoelasticity.
Research Project #1: CSI Eastman: Image Processing based Characterization of Photolytic Degradation of Daguerreotypes
Introduced in 1839, the daguerreotype became the first commercially viable medium for photographic image capture and served as the primary photographic medium of record for over a decade. The history captured in daguerreotypes provides a unique detailed photorealistic record, which is of interest to social scientists and the general public alike. The recent discovery that daguerreotypes are subject to photolytic degradation, i.e. degradation under exposure to light, has, however, alarmed museums and the majority of daguerreotypes are no longer available for public display because of concerns about light induced damage. The George Eastman House (GEH) has one of the largest collections of daguerreotypes and is actively involved in efforts to analyze and prevent degradations in this medium. In this joint project with GEH, the goal is to characterize photolytic degradations produced by exposure to different wavelengths of light by using image processing on photomicrographs of daguerreotypes and to then use the characterization as a forensic tool to characterize degradations in actual collections.
Research Project #2: The Full-Spectrum Mobile Experience: UR Color Barcodes
UR researchers have recently invented a color version of the seemingly ubiquitous mobile barcodes that allow us to layer three independent pieces of information within each barcode by using the "spectral diversity" afforded by color printing and capture. In this project, we are looking to develop an easy to use mobile application and to test how the capability for layering can be advantageously used for student activity flyers and other events around campus.
Research Project #3: Adaptive Color Visualization for Color Deficient Observers on Android Smartphones
Around 7-10% of the male population in North America has some form of color deficiency. These viewers often find it difficult to tell the difference between certain colors that appear clearly different to observers with normal color vision. The color deficiency is particularly problematic when it causes a loss of discriminability of different objects or when a color deficient individual must engage in a conversation involving standard color terminology designed for color normal viewers. As increasingly popular personalized imaging devices, smart phones can be used as tools to help color-deficient users overcome their deficiency. As a participant in this research you will help develop and deploy techniques for improved color visualization on Android smartphones by specifically exploiting the adaptivity and personalization that these devices offer.
Research Project #4: Noncoding RNA Gene Search: Unlock the hidden information in Genomes
With the wide spread availability of high throughput sequencing technology, vast datasets of genomes are now available to researchers for exploration. Conventional protein coding genes can be located within these large genome data sets with relative ease using BLAST and other alignment tools. Noncoding RNAs (ncRNAs) that serve a direct functional role instead of providing a recipe for protein synthesis, however, present a challenge for genomic analysis. Across species ncRNAs are conserved in secondary structure rather than in sequence and they are therefore not discovered by common sequence alignment based search tools. With the discovery of an increasing number of ncRNAs it is clear that they represent the next frontier in advancing our understanding of the genomes. As a participant in this research, you will develop and evaluate new computational methods for identifying ncRNAs.
Research Project #5: Principled Machine Learning Methods for Multiple Sequence Alignment
The alignment of sequence data is a fundamental task in analyzing genomic data, which shares several commonalities with string comparison. For aligning two sequences, hidden Markov models have emerged as the model of choice for aligning two sequences. For more than two sequences, several heuristics have been proposed that operate on sequences in a pair wise fashion. In this project, we seek an alternate more principled framework for multiple sequence alignment using principled machine learning methods. As a participant in this research, you will be involved in developing, implementing, and evaluating new techniques for multiple sequence alignment.