5,592 research outputs found

    Enhancing Closed-Loop Wireless Systems Through Efficient Feedback Reconstruction

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    PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback

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    In a frequency division duplexing multiple-input multiple-output (FDD-MIMO) system, the user equipment (UE) send the downlink channel state information (CSI) to the base station for performance improvement. However, with the growing complexity of MIMO systems, this feedback becomes expensive and has a negative impact on the bandwidth. Although this problem has been largely studied in the literature, the noisy nature of the feedback channel is less considered. In this paper, we introduce PRVNet, a neural architecture based on variational autoencoders (VAE). VAE gained large attention in many fields (e.g., image processing, language models, or recommendation system). However, it received less attention in the communication domain generally and in CSI feedback problem specifically. We also introduce a different regularization parameter for the learning objective, which proved to be crucial for achieving competitive performance. In addition, we provide an efficient way to tune this parameter using KL-annealing. Empirically, we show that the proposed model significantly outperforms state-of-the-art, including two neural network approaches. The proposed model is also proved to be more robust against different levels of noise

    Development of A Soft Robotic Approach for An Intra-abdominal Wireless Laparoscopic Camera

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    In Single-Incision Laparoscopic Surgery (SILS), the Magnetic Anchoring and Guidance System (MAGS) arises as a promising technique to provide larger workspaces and field of vision for the laparoscopes, relief space for other instruments, and require fewer incisions. Inspired by MAGS, many concept designs related to fully insertable magnetically driven laparoscopes are developed and tested on the transabdominal operation. However, ignoring the tissue interaction and insertion procedure, most of the designs adopt rigid structures, which not only damage the patients\u27 tissue with excess stress concentration and sliding motion but also require complicated operation for the insertion. Meanwhile, lacking state tracking of the insertable camera including pose and contact force, the camera systems operate in open-loop control. This provides mediocre locomotion precision and limited robustness to uncertainties in the environment. This dissertation proposes, develops, and validates a soft robotic approach for an intra-abdominal wireless laparoscopic camera. Contributions presented in this work include (1) feasibility of a soft intra-abdominal laparoscopic camera with friendly tissue interaction and convenient insertion, (2) six degrees of freedom (DOF) real-time localization, (3) Closed-loop control for a robotic-assisted laparoscopic system and (4) untethering solution for wireless communication and high-quality video transmission. Embedding magnet pairs into the camera and external actuator, the camera can be steered and anchored along the abdominal wall through transabdominal magnetic coupling. To avoid the tissue rapture by the sliding motion and dry friction, a wheel structure is applied to achieve rolling motion. Borrowing the ideas from soft robotic research, the main body of the camera implements silicone material, which grants it the bendability to passively attach along the curved abdominal wall and the deformability for easier insertion. The six-DOF pose is estimated in real-time with internal multi-sensor fusion and Newton-Raphson iteration. Combining the pose tracking and force-torque sensor measurement, an interaction model between the deformable camera and tissue is established to evaluate the interaction force over the tissue surface. Moreover, the proposed laparoscopic system is integrated with a multi-DOF manipulator into a robotic-assisted surgical system, where a closed-loop control is realized based on a feedback controller and online optimization. Finally, the wireless control and video streaming are accomplished with Bluetooth Low Energy (BLE) and Analog Video (AV) transmission. Experimental assessments have been implemented to evaluate the performance of the laparoscopic system

    Wired, wireless and wearable bioinstrumentation for high-precision recording of bioelectrical signals in bidirectional neural interfaces

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    It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Furthermore, electrical stimulation of specific target brain regions has been shown to alleviate symptoms of neurological disorders, such as Parkinson’s disease, essential tremor, dystonia, epilepsy etc. In recent years, the traditional practice of continuously stimulating the brain using static stimulation parameters has shifted to the use of disease biomarkers to determine the intensity and timing of stimulation. The main motivation behind closed-loop stimulation is minimization of treatment side effects by providing only the necessary stimulation required within a certain period of time, as determined from a guiding biomarker. Hence, it is clear that high-quality recording of local field potentials (LFPs) or electrocorticographic (ECoG) signals during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording ECoG signals, which are of particular importance in closed-loop DBS and epilepsy DBS. In addition, existing recording systems lack the ability to provide artefact-free high-frequency (> 100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. To address the problem of limited mobility often encountered by patients in the clinic and to provide a wide variety of high-precision sensor data to a closed-loop neurostimulation platform, a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless multi-instrument (55 × 80 mm2) was designed and developed. The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5 – 500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile tool to be utilized in a wide range of applications and environments. Moreover, in order to offer the capability of sensing and stimulating via the same electrode, novel real-time artefact suppression methods that could be used in bidirectional (recording and stimulation) system architectures are proposed and validated. More specifically, a novel, low-noise and versatile analog front-end (AFE), which uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency, is presented. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, the performance of the realised AFE is assessed by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5 – 250 Hz) during stimulation. Finally, a family of tunable hardware filter designs and a novel method for real-time artefact suppression that enables wide-bandwidth biosignal recordings during stimulation are also presented. This work paves the way for the development of miniaturized research tools for closed-loop neuromodulation that use a wide variety of bioelectrical signals as control signals.Open Acces

    FDD Massive MIMO Based on Efficient Downlink Channel Reconstruction

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    Massive multiple-input multiple-output (MIMO) systems deploying a large number of antennas at the base station considerably increase the spectrum efficiency by serving multiple users simultaneously without causing severe interference. However, the advantage relies on the availability of the downlink channel state information (CSI) of multiple users, which is still a challenge in frequency-division-duplex transmission systems. This paper aims to solve this problem by developing a full transceiver framework that includes downlink channel training (or estimation), CSI feedback, and channel reconstruction schemes. Our framework provides accurate reconstruction results for multiple users with small amounts of training and feedback overhead. Specifically, we first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP) algorithm to extract the frequency-independent parameters (i.e., downtilts, azimuths, and delays) from the uplink. Then, by leveraging the information from these frequency-independent parameters, we develop an efficient downlink training scheme to estimate the downlink channel gains for multiple users. This training scheme offers an acceptable estimation error rate of the gains with a limited pilot amount. Numerical results verify the precision of the eNOMP algorithm and demonstrate that the sum-rate performance of the system using the reconstructed downlink channel can approach that of the system using perfect CSI

    Towards Tactile Internet in Beyond 5G Era: Recent Advances, Current Issues and Future Directions

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    Tactile Internet (TI) is envisioned to create a paradigm shift from the content-oriented communications to steer/control-based communications by enabling real-time transmission of haptic information (i.e., touch, actuation, motion, vibration, surface texture) over Internet in addition to the conventional audiovisual and data traffics. This emerging TI technology, also considered as the next evolution phase of Internet of Things (IoT), is expected to create numerous opportunities for technology markets in a wide variety of applications ranging from teleoperation systems and Augmented/Virtual Reality (AR/VR) to automotive safety and eHealthcare towards addressing the complex problems of human society. However, the realization of TI over wireless media in the upcoming Fifth Generation (5G) and beyond networks creates various non-conventional communication challenges and stringent requirements in terms of ultra-low latency, ultra-high reliability, high data-rate connectivity, resource allocation, multiple access and quality-latency-rate tradeoff. To this end, this paper aims to provide a holistic view on wireless TI along with a thorough review of the existing state-of-the-art, to identify and analyze the involved technical issues, to highlight potential solutions and to propose future research directions. First, starting with the vision of TI and recent advances and a review of related survey/overview articles, we present a generalized framework for wireless TI in the Beyond 5G Era including a TI architecture, the main technical requirements, the key application areas and potential enabling technologies. Subsequently, we provide a comprehensive review of the existing TI works by broadly categorizing them into three main paradigms; namely, haptic communications, wireless AR/VR, and autonomous, intelligent and cooperative mobility systems. Next, potential enabling technologies across physical/Medium Access Control (MAC) and network layers are identified and discussed in detail. Also, security and privacy issues of TI applications are discussed along with some promising enablers. Finally, we present some open research challenges and recommend promising future research directions
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