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1. IMU-Assisted Learning of Single-View Rolling Shutter Correction

   Mo, J; Islam, Md J.; Sattar, J. (November 2021, Conference on Robot Learning)

   Rolling shutter distortion is highly undesirable for photography and computer vision algorithms (e.g., visual SLAM) because pixels can be potentially captured at different times and poses. In this paper, we propose a deep neural network to predict depth and row-wise pose from a single image for rolling shutter correction. Our contribution in this work is to incorporate inertial measurement unit (IMU) data into the pose refinement process, which, compared to the state-of-the-art, greatly enhances the pose prediction. The improved accuracy and robustness make it possible for numerous vision algorithms to use imagery captured by rolling shutter cameras and produce highly accurate results. We also extend a dataset to have real rolling shutter images, IMU data, depth maps, camera poses, and corresponding global shutter images for rolling shutter correction training. We demonstrate the efficacy of the proposed method by evaluating the performance of Direct Sparse Odometry (DSO) algorithm on rolling shutter imagery corrected using the proposed approach. Results show marked improvements of the DSO algorithm over using uncorrected imagery, validating the proposed approach. 

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2. A low-cost shutter driver and arbitrary waveform generator for optical switching using a programmable system-on-chip (PSoC) device

   https://doi.org/10.1063/5.0105884  

   Craven, J. M.; Meeks, E.; Delich, G.; Ayars, E.; Pechkis, H. K.; Pechkis, J. A. (November 2022, Review of Scientific Instruments)

   We have developed a low-cost mechanical shutter driver with integrated arbitrary waveform generation for optical switching and control using a programmable system-on-chip device. This microcontroller-based device with configurable digital and analog blocks is readily programmed using free software, allowing for easy customization for a variety of applications. Additional digital and analog outputs with arbitrary timings can be used to control a variety of devices, such as additional shutters, acousto-optical modulators, or camera trigger pulses, for complete control and imaging of laser light. Utilizing logic-level control signals, this device can be readily integrated into existing computer control and data acquisition systems for expanded hardware capabilities. 

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3. Enabling Multiple Access in Visible Light Communication Using Liquid Crystal Displays: A Proof-of-Concept Study

   https://doi.org/10.3390/electronics9050826  

   Rahman, MD Rashed; Adedara, Kehinde; Ashok, Ashwin (May 2020, Electronics)

   null (Ed.)

   The directionality of optical signals provides an opportunity for efficient space reuse of optical links in visible light communication (VLC). Space reuse in VLC can enable multiple-access communication from multiple light emitting transmitters. Traditional VLC system design using photo-receptors requires at least one receiving photodetector element for each light emitter, thus constraining VLC to always require a light-emitter to light-receptor element pair. In this paper, we propose, design and evaluate a novel architecture for VLC that can enable multiple-access reception using a photoreceptor receiver that uses only a single photodiode. The novel design includes a liquid-crystal-display (LCD) based shutter system that can be automated to control and enable selective reception of light beams from multiple transmitters. We evaluate the feasibility of multiple access on a single photodiode from two light emitting diode (LED) transmitters and the performance of the communication link using bit-error-rate (BER) and packet-error-rate (PER) metrics. Our experiment and trace based evaluation through proof-of-concept implementation reveals the feasibility of multiple LED reception on a single photodiode. We further evaluate the system in controlled mobile settings to verify the adaptability of the receiver when the LED transmitter changes position. 

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4. Integrated Design of Low Complexity RSS Based Visible Light Indoor Positioning and Power-Line Communication System for Smart Hospitals

   https://doi.org/10.1109/ICCE50685.2021.9427699  

   Lang, Tian; Pan, Zijin; Ortiz, Nathaniel; Chen, Gang; Kalay, Yehuda; Pai, Ramdas; Wang, Albert (January 2021, IEEE International Conference on Consumer Electronics (ICCE) 2021)

   null (Ed.)

   Future healthcare systems require smart hospitals with system-wide wireless communications and positioning functions, which cannot be facilitated by existing radio-frequency (RF) wireless technologies. In this paper, we present integrated design of a novel low-complexity received signal strength (RSS) based hybrid visible light communication (VLC) and indoor positioning (VLP) system. This VLC/VLP tracking system consist of host optical transceivers embedded in existing light-emitting diode (LED) bulbs and user-end optical tags, which interface with the existing 120AVC power wiring in a building. The new hybrid VLC/PLC tracking system was validated by simulation and experimentation. This LED VLC tracking system will enable smart hospital operations to modernize next-generation intelligent healthcare systems 

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5. An Empirical Study of Deep Learning Models for LED Signal Demodulation in Optical Camera Communication

   https://doi.org/10.3390/network1030016  

   Ahmed, AbdulHaseeb; Trichy Viswanathan, Sethuraman; Rahman, MD Rashed; Ashok, Ashwin (December 2021, Network)

   Optical camera communication is an emerging technology that enables communication using light beams, where information is modulated through optical transmissions from light-emitting diodes (LEDs). This work conducts empirical studies to identify the feasibility and effectiveness of using deep learning models to improve signal reception in camera communication. The key contributions of this work include the investigation of transfer learning and customization of existing models to demodulate the signals transmitted using a single LED by applying the classification models on the camera frames at the receiver. In addition to investigating deep learning methods for demodulating a single VLC transmission, this work evaluates two real-world use-cases for the integration of deep learning in visual multiple-input multiple-output (MIMO), where transmissions from a LED array are decoded on a camera receiver. This paper presents the empirical evaluation of state-of-the-art deep neural network (DNN) architectures that are traditionally used for computer vision applications for camera communication. 

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