EyeLoop: An Open-Source System for High-Speed, Closed-Loop Eye-Tracking

Eye-trackers are widely used to study nervous system dynamics and neuropathology. Despite this broad utility, eye-tracking remains expensive, hardware-intensive, and proprietary, limiting its use to high-resource facilities. It also does not easily allow for real-time analysis and closed-loop design to link eye movements to neural activity. To address these issues, we developed an open-source eye-tracker – EyeLoop – that uses a highly efficient vectorized pupil detection method to provide uninterrupted tracking and fast online analysis with high accuracy on par with popular eye tracking modules, such as DeepLabCut. This Python-based software easily integrates custom functions using code modules, tracks a multitude of eyes, including in rodents, humans, and non-human primates, and operates at more than 1,000 frames per second on consumer-grade hardware. In this paper, we demonstrate EyeLoop’s utility in an open-loop experiment and in biomedical disease identification, two common applications of eye-tracking. With a remarkably low cost and minimum setup steps, EyeLoop makes high-speed eye-tracking widely accessible.

Closing the Loop With Cortical Sensing: The Development of Adaptive Deep Brain Stimulation for Essential Tremor Using the Activa PC+S

Deep Brain Stimulation (DBS) is an important tool in the treatment of pharmacologically resistant neurological movement disorders such as essential tremor (ET) and Parkinson's disease (PD). However, the open-loop design of current systems may be holding back the true potential of invasive neuromodulation. In the last decade we have seen an explosion of activity in the use of feedback to “close the loop” on neuromodulation in the form of adaptive DBS (aDBS) systems that can respond to the patient's therapeutic needs. In this paper we summarize the accomplishments of a 5-year study at the University of Washington in the use of neural feedback from an electrocorticography strip placed over the sensorimotor cortex. We document our progress from an initial proof of hardware all the way to a fully implanted adaptive stimulation system that leverages machine-learning approaches to simplify the programming process. In certain cases, our systems out-performed current open-loop approaches in both power consumption and symptom suppression. Throughout this effort, we collaborated with neuroethicists to capture patient experiences and take them into account whilst developing ethical aDBS approaches. Based on our results we identify several key areas for future work. “Graded” aDBS will allow the system to smoothly tune the stimulation level to symptom severity, and frequent automatic calibration of the algorithm will allow aDBS to adapt to the time-varying dynamics of the disease without additional input from a clinician. Additionally, robust computational models of the pathophysiology of ET will allow stimulation to be optimized to the nuances of an individual patient's symptoms. We also outline the unique advantages of using cortical electrodes for control and the remaining hardware limitations that need to be overcome to facilitate further development in this field. Over the course of this study we have verified the potential of fully-implanted, cortically driven aDBS as a feasibly translatable treatment for pharmacologically resistant ET.

Development of Wireless Sensor Device for Machine English Oral Pronunciation Noise Detection

There is often noise in spoken machine English, which affects the accuracy of pronunciation. Therefore, how to accurately detect the noise in machine English spoken language and give standard spoken pronunciation is very important and meaningful. The traditional machine-oriented spoken English speech noise detection technology is limited to the improvement of software algorithm, mainly including speech enhancement technology and speech endpoint detection technology. Based on this, this paper will develop a wireless sensor network based on machine English oral pronunciation noise based on air and nonair conduction, reasonably design and configure air sensors, and nonair conduction sensors to deal with machine English oral pronunciation noise, so as to improve the naturalness and intelligibility of machine English speech. At the hardware level, this paper mainly optimizes the AD sampling, sensor matching layout, and internal hardware circuit board layout of the two types of sensors, so as to solve the compatibility problem between them and further reduce the hardware power consumption. In order to further verify or evaluate the performance of the machine spoken English speech noise detection sensor designed in this paper, a machine spoken English training system based on Android platform is designed. Compared with the traditional system, the training system can improve the intelligence of machine oriented oral English noise detection algorithm, so as to continuously improve the accuracy of system detection. The machine English pronunciation is adjusted and corrected by combining the data sensed by the sensor, so as to form a closed-loop design. The experimental results show that the wireless sensor sample proposed in this paper has obvious advantages in detecting the accuracy of machine English oral pronunciation, and its good closed-loop system is helpful to further improve the accuracy of machine English oral pronunciation.

A Water Loop Design for the CRAFT Project towards the Testing of CFETR Water-Cooled Blanket and Divertor

As one of the tasks of the Comprehensive Research Facility for Fusion Technology (CRAFT), a High Heat Flux (HHF) testing device will be built to test the blanket and divertor of Chinese Fusion Engineering Testing Reactor (CFETR). The water loop is a key system of the HHF testing device. The main objective of the water loop is to provide deionized water at specific temperature, pressure, and flow rate for different testing experiments of the water-cooled blanket and water-cooled divertor components. The design of the water loop has been through three major steps. Firstly, the water cooled blanket and divertor were designed and analyzed, in detail, for CFETR. Secondly, thermal hydraulic features of the prototypes were abstracted from the analyses results. Then, the experiment plan was made so that the preliminary design of the water loop was carried out. The third step was the engineering design, which was conducted through cooperation with an industrial enterprise with certifications. At present, the water loop is ready for fabrication and construction. The water loop will be completed, for commissioning operation, by August 2022, as scheduled. After that, the experiments will be carried out step by step and provide solid technical base to CFETR.

Multi-user transmission for the joint radar communication systems based on amplitude phase shift keying modulation and waveform diversity

In this paper, an efficient dual function radar-communication system is proposed to improve the system's resource utilization. In this work, we considered a scenario where the location of the communication receiver is known prior but the radar target is moving and its location is changing with time. Therefore, we proposed a closed-loop design that allows an adaptive selection of appropriate information embedding strategies during tracking operations. We used two strategies that utilize the amplitudes and/or phases of the transmitted radar waveforms toward the communication direction according to the position of the communication receiver during each scan. Hence, during each radar illumination, the system carries out a target-tracking task and simultaneously maintains the communication symbols transmission toward the intended communication direction. The simulation results verify the effective performance of the proposed approach in terms of target detection and tracking performance and angular bit error rate (BER). Furthermore, the proposed amplitude phase-shift keying signaling strategy can transmit different communication symbols to different users located within the sidelobe region and also provides a significant improvement in data rate transmission and BER performance compared with the existing sidelobe-based communication strategies.

Stability of Intraocular Lens With Different Haptic Design: A Swept-Source Optical Coherence Tomography Study

Purpose: To investigate the stability of intraocular lens (IOLs) with different haptics by swept-source anterior-segment optical coherence tomography (AS-OCT).Methods: Sixty-eight eyes from 55 patients received the implantation of Rayner 920H (Closed C-loop Group), Zeiss 509M (Plate Group) or Lenstec SOFTEC HD (C-loop Group) IOLs. The tilt and decentration of IOLs were evaluated using AS-OCT at least 1 month postoperatively.Results: Mean decentration and tilt of IOLs were 0.18 ± 0.12 mm (range 0.02 to 0.59 mm) and 5.63 ± 1.65° (range 2.2 to 9.6°) respectively. Decentration was significantly smaller in the plate haptic group (0.12 ± 0.06 mm) as compared to the C-loop group (0.22 ± 0.13 mm, P = 0.02). The tilt of IOL was also significantly smaller in the plate haptic group (4.96 ± 0.89°) as compared to the C-loop group (6.28 ± 1.83°, P = 0.01). There was marginal difference between the Closed C-loop group (5.52 ± 1.74°) and C-loop group (6.28 ± 1.83°, P = 0.07).Conclusions: The Plate-haptic IOLs should have better stability for the decentration and tilt than the C-loop design IOLs.