Smart TV Pairing Interface Using Smart Phone

A way to pair a controller to a user device, for example pairing a smart phone to a smart TV to control the smart TV, is described. The user device is pre-programed to interpret a chirp packet that can include encoded commands. The command device is programed to send the chirp packet. For example, if the command packet is a smart phone, the crip packet encoder may be installed via an app

In-Conference Tool Virtual Assistant with Real Time Speaking Latency

A framework is proposed for reducing latency for real-time speaking applications, such as virtual assistants. The framework utilizes a hybrid scheme between a large language model (LLM) and a small sound model (SSM). The SSM timestamps the end of a user’s verbal query and notifies the transcription engine that the query has ended. The transcription engine will then abort and send the existing set of transcriptions to the LLM for processing without waiting for the buffer to meet the specific value or time. This minimizes the net response time for real time speech-based models due to the reduced latency from the transcription engine

A Type-to-Talk Framework Using Generative Voice Cloning Methods for Privacy-Preserving Communications

A method for providing a text-to-speech framework that generated speech that mimics a user’s voice is disclosed. The proposed method receives sample speech from the user, and generates speaker embeddings specific to the user. The speaker embeddings are generated using a neural network. The speaker embeddings are used to fine-tune a generative vocoder. The finetuned generative vocoder can be used to generate speech that mimics the speech patterns and vocal characteristics of the user. Thus, text entered by the user can be converted to audio that sounds like the user’s speech. The generated audio is then transmitted to other participants in a virtual meeting

Performance analysis of low-flux least-squares single-pixel imaging

A single-pixel camera is able to computationally form spatially resolved images using one photodetector and a spatial light modulator. The images it produces in low-light-level operation are imperfect, even when the number of measurements exceeds the number of pixels, because its photodetection measurements are corrupted by Poisson noise. Conventional performance analysis for single-pixel imaging generates estimates of mean-square error (MSE) from Monte Carlo simulations, which require long computational times. In this letter, we use random matrix theory to develop a closed-form approximation to the MSE of the widely used least-squares inversion method for Poisson noise-limited single-pixel imaging. We present numerical experiments that validate our approximation and a motivating example showing how our framework can be used to answer practical optical design questions for a single-pixel camera.This work was supported in part by the Samsung Scholarship and in part by the US National Science Foundation under Grant 1422034. (Samsung Scholarship; 1422034 - US National Science Foundation)Accepted manuscrip

Photon-Efficient Computational 3D and Reflectivity Imaging with Single-Photon Detectors

Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with single-photon detectors, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust method for estimating depth and reflectivity using on the order of 1 detected photon per pixel averaged over the scene. Our computational imager combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3D structure. Experiments conducted in the presence of strong background light demonstrate that our computational imager is able to accurately recover scene depth and reflectivity, while traditional maximum-likelihood based imaging methods lead to estimates that are highly noisy. Our framework increases photon efficiency 100-fold over traditional processing and also improves, somewhat, upon first-photon imaging under a total acquisition time constraint in raster-scanned operation. Thus our new imager will be useful for rapid, low-power, and noise-tolerant active optical imaging, and its fixed dwell time will facilitate parallelization through use of a detector array.Comment: 11 pages, 8 figure

Machine-Learning Based Smoke Detection

A machine learning based arrangement can be used to more accurately detect whether a smoke alarm should be sounded based on a determined rate of change in the measured amount of smoke. The machine learning model may be pre-trained based on training data then executed by a smoke detector to accurately distinguish between likely emergencies and nuisance conditions

Computational multi-depth single-photon imaging

We present an imaging framework that is able to accurately reconstruct multiple depths at individual pixels from single-photon observations. Our active imaging method models the single-photon detection statistics from multiple reflectors within a pixel, and it also exploits the fact that a multi-depth profile at each pixel can be expressed as a sparse signal. We interpret the multi-depth reconstruction problem as a sparse deconvolution problem using single-photon observations, create a convex problem through discretization and relaxation, and use a modified iterative shrinkage-thresholding algorithm to efficiently solve for the optimal multi-depth solution. We experimentally demonstrate that the proposed framework is able to accurately reconstruct the depth features of an object that is behind a partially-reflecting scatterer and 4 m away from the imager with root mean-square error of 11 cm, using only 19 signal photon detections per pixel in the presence of moderate background light. In terms of root mean-square error, this is a factor of 4.2 improvement over the conventional method of Gaussian-mixture fitting for multi-depth recovery.This material is based upon work supported in part by a Samsung Scholarship, the US National Science Foundation under Grant No. 1422034, and the MIT Lincoln Laboratory Advanced Concepts Committee. We thank Dheera Venkatraman for his assistance with the experiments. (Samsung Scholarship; 1422034 - US National Science Foundation; MIT Lincoln Laboratory Advanced Concepts Committee)Accepted manuscrip

Single-Photon Depth Imaging Using a Union-of-Subspaces Model

Light detection and ranging systems reconstruct scene depth from time-of-flight measurements. For low light-level depth imaging applications, such as remote sensing and robot vision, these systems use single-photon detectors that resolve individual photon arrivals. Even so, they must detect a large number of photons to mitigate Poisson shot noise and reject anomalous photon detections from background light. We introduce a novel framework for accurate depth imaging using a small number of detected photons in the presence of an unknown amount of background light that may vary spatially. It employs a Poisson observation model for the photon detections plus a union-of-subspaces constraint on the discrete-time flux from the scene at any single pixel. Together, they enable a greedy signal-pursuit algorithm to rapidly and simultaneously converge on accurate estimates of scene depth and background flux, without any assumptions on spatial correlations of the depth or background flux. Using experimental single-photon data, we demonstrate that our proposed framework recovers depth features with 1.7 cm absolute error, using 15 photons per image pixel and an illumination pulse with 6.7-cm scaled root-mean-square length. We also show that our framework outperforms the conventional pixelwise log-matched filtering, which is a computationally-efficient approximation to the maximum-likelihood solution, by a factor of 6.1 in absolute depth error.Samsung (Firm) (Scholarship)National Science Foundation (U.S.) (Grant 1422034)Lincoln Laboratory. Advanced Concepts Committe

Photon-efficient imaging with a single-photon camera

Reconstructing a scene’s 3D structure and reflectivity accurately with an active imaging system operating in low-light-level conditions has wide-ranging applications, spanning biological imaging to remote sensing. Here we propose and experimentally demonstrate a depth and reflectivity imaging system with a single-photon camera that generates high-quality images from ∼1 detected signal photon per pixel. Previous achievements of similar photon efficiency have been with conventional raster-scanning data collection using single-pixel photon counters capable of ∼10-ps time tagging. In contrast, our camera’s detector array requires highly parallelized time-to-digital conversions with photon time-tagging accuracy limited to ∼ns. Thus, we develop an array-specific algorithm that converts coarsely time-binned photon detections to highly accurate scene depth and reflectivity by exploiting both the transverse smoothness and longitudinal sparsity of natural scenes. By overcoming the coarse time resolution of the array, our framework uniquely achieves high photon efficiency in a relatively short acquisition time.National Science Foundation (U.S.) (1161413)National Science Foundation (U.S.) (1422034)Lincoln LaboratorySamsung (Firm

Computational three-dimensional and reflectivity imaging with high photon efficiency

Thesis: S.M. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.45Cataloged from PDF version of thesis.Includes bibliographical references (pages 77-81).Imaging the 3D structure and reflectivity of a scene can be done using photon-counting detectors. Traditional imagers of this type typically require hundreds of detected photons per pixel for accurate 3D and reflectivity imaging. Under low light-level conditions, in which the mean photon count is small, the inverse problem of forming 3D and reflectivity images is difficult due to the Poisson noise inherent in low-flux operation. In this thesis, we propose and study two computational imagers (one passive, one active) that can form accurate images at low light levels. We demonstrate the superior imaging quality of the proposed imagers by comparing them with the state-of-the-art optical imaging techniques.by Dongeek Shin.S.M. in Computer Science and Engineerin