117 research outputs found
Improved methods for functional neuronal imaging with genetically encoded voltage indicators
Voltage imaging has the potential to revolutionise neuronal physiology, enabling high temporal and spatial resolution monitoring of sub- and supra-threshold activity in genetically defined cell classes. Before this goal is reached a number of challenges must be overcome: novel optical, genetic, and experimental techniques must be combined to deal with voltage imaging’s unique difficulties.
In this thesis three techniques are applied to genetically encoded voltage indicator (GEVI)
imaging. First, I describe a multifocal two-photon microscope and present a novel source localisation control and reconstruction algorithm to increase scattering resistance in functional
imaging. I apply this microscope to image population and single-cell voltage signals from voltage sensitive fluorescent proteins in the first demonstration of multifocal GEVI imaging. Second, I show that a recently described genetic technique that sparsely labels cortical pyramidal
cells enables single-cell resolution imaging in a one-photon widefield imaging configuration.
This genetic technique allows simple, high signal-to-noise optical access to the primary excitatory
cells in the cerebral cortex. Third, I present the first application of lightfield microscopy
to single cell resolution neuronal voltage imaging. This technique enables single-shot capture of dendritic arbours and resolves 3D localised somatic and dendritic voltage signals. These approaches are finally evaluated for their contribution to the improvement of voltage imaging for physiology.Open Acces
Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO
This paper proposes a grant-free massive access scheme based on the
millimeter wave (mmWave) extra-large-scale multiple-input multiple-output
(XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency,
high data rate, and high localization accuracy in the upcoming sixth-generation
(6G) networks. The XL-MIMO consists of multiple antenna subarrays that are
widely spaced over the service area to ensure line-of-sight (LoS)
transmissions. First, we establish the XL-MIMO-based massive access model
considering the near-field spatial non-stationary (SNS) property. Then, by
exploiting the block sparsity of subarrays and the SNS property, we propose a
structured block orthogonal matching pursuit algorithm for efficient active
user detection (AUD) and channel estimation (CE). Furthermore, different
sensing matrices are applied in different pilot subcarriers for exploiting the
diversity gains. Additionally, a multi-subarray collaborative localization
algorithm is designed for localization. In particular, the angle of arrival
(AoA) and time difference of arrival (TDoA) of the LoS links between active
users and related subarrays are extracted from the estimated XL-MIMO channels,
and then the coordinates of active users are acquired by jointly utilizing the
AoAs and TDoAs. Simulation results show that the proposed algorithms outperform
existing algorithms in terms of AUD and CE performance and can achieve
centimeter-level localization accuracy.Comment: Submitted to IEEE Transactions on Communications, Major revision.
Codes will be open to all on https://gaozhen16.github.io/ soo
1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface
A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Recommended from our members
Fear-related Memory: A Role for Circadian Rhythms In The Medial Prefrontal Cortex
Exposure therapy remains a gold standard for the treatment of fear-based mental disorders, including specific phobias, obsessive-compulsive disorder, and post-traumatic stress disorder. Exposure draws on the principles of Pavlovian fear extinction, a learning and memory process in which repeated exposure to danger- associated cues generates a gradual reduction in fear responding. But extinction memories are fragile, and fear symptoms return over time in a large proportion of individuals who have successfully completed exposure therapy. Consequently, it is of great interest to identify strategies for optimizing extinction. To this end, accumulating evidence suggests that extinction may be improved through manipulations of the circadian system, a conserved biological network of molecular timekeepers that enables cells throughout the body to coordinate their functions across a 24-h timescale. Rodents and humans exhibit circadian rhythms in the learning and recall of extinction. In rats, these rhythms require intact circadian function in the infralimbic subdivision of the medial prefrontal cortex. Yet the neurobiological mechanisms linking prefrontal circadian function to rhythms in extinction remain poorly understood. In Chapter 1, we review the extant literature examining circadian influences on memory neurobiology and present a framework for interpreting circadian studies on memory. Using this framework, we illustrate in Chapter 2 that circadian rhythms in extinction recall in rats depend on the time of day of extinction recall, not extinction learning, which points to the circadian modulation of mechanisms supporting extinction retention and retrieval. We then demonstrate in Chapter 3 that circadian rhythms in extinction are associated with rhythms in neural activity in the infralimbic prefrontal cortex. In Chapter 4, we show that norepinephrine contributes to stress-induced disruption of medial prefrontal cortex circadian function, one possible mechanism for stress-induced impairments in extinction. We close in Chapter 5 with an integration of current knowledge, formulating a working hypothesis about how circadian modulation of medial prefrontal cortex neurobiology may produce circadian rhythms in extinction. We then lay out avenues for future research and discuss potential implications of this work for the optimization of exposure therapy.</p
A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles
Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy
Heterogeneous integration of optical wireless communications within next generation networks
Unprecedented traffic growth is expected in future wireless networks and new
technologies will be needed to satisfy demand. Optical wireless (OW) communication offers vast unused spectrum and high area spectral efficiency. In this work, optical
cells are envisioned as supplementary access points within heterogeneous RF/OW networks. These networks opportunistically offload traffic to optical cells while utilizing
the RF cell for highly mobile devices and devices that lack a reliable OW connection.
Visible light communication (VLC) is considered as a potential OW technology due
to the increasing adoption of solid state lighting for indoor illumination.
Results of this work focus on a full system view of RF/OW HetNets with three primary areas of analysis. First, the need for network densication beyond current RF
small cell implementations is evaluated. A media independent model is developed
and results are presented that provide motivation for the adoption of hyper dense
small cells as complementary components within multi-tier networks. Next, the relationships between RF and OW constraints and link characterization parameters are
evaluated in order to define methods for fair comparison when user-centric channel
selection criteria are used. RF and OW noise and interference characterization techniques are compared and common OW characterization models are demonstrated
to show errors in excess of 100x when dominant interferers are present. Finally,
dynamic characteristics of hyper dense OW networks are investigated in order to optimize traffic distribution from a network-centric perspective. A Kalman Filter model
is presented to predict device motion for improved channel selection and a novel OW
range expansion technique is presented that dynamically alters coverage regions of
OW cells by 50%.
In addition to analytical results, the dissertation describes two tools that have
been created for evaluation of RF/OW HetNets. A communication and lighting
simulation toolkit has been developed for modeling and evaluation of environments
with VLC-enabled luminaires. The toolkit enhances an iterative site based impulse
response simulator model to utilize GPU acceleration and achieves 10x speedup over
the previous model. A software defined testbed for OW has also been proposed
and applied. The testbed implements a VLC link and a heterogeneous RF/VLC
connection that demonstrates the RF/OW HetNet concept as proof of concept
Applications of Antenna Technology in Sensors
During the past few decades, information technologies have been evolving at a tremendous rate, causing profound changes to our world and to our ways of living. Emerging applications have opened u[ new routes and set new trends for antenna sensors. With the advent of the Internet of Things (IoT), the adaptation of antenna technologies for sensor and sensing applications has become more important. Now, the antennas must be reconfigurable, flexible, low profile, and low-cost, for applications from airborne and vehicles, to machine-to-machine, IoT, 5G, etc. This reprint aims to introduce and treat a series of advanced and emerging topics in the field of antenna sensors
Mechanisms of place recognition and path integration based on the insect visual system
Animals are often able to solve complex navigational tasks in very challenging terrain,
despite using low resolution sensors and minimal computational power, providing
inspiration for robots. In particular, many species of insect are known to solve complex
navigation problems, often combining an array of different behaviours (Wehner
et al., 1996; Collett, 1996). Their nervous system is also comparatively simple, relative
to that of mammals and other vertebrates.
In the first part of this thesis, the visual input of a navigating desert ant, Cataglyphis
velox, was mimicked by capturing images in ultraviolet (UV) at similar wavelengths
to the ant’s compound eye. The natural segmentation of ground and sky lead to
the hypothesis that skyline contours could be used by ants as features for navigation.
As proof of concept, sky-segmented binary images were used as input for an
established localisation algorithm SeqSLAM (Milford and Wyeth, 2012), validating
the plausibility of this claim (Stone et al., 2014). A follow-up investigation sought to
determine whether using the sky as a feature would help overcome image matching
problems that the ant often faced, such as variance in tilt and yaw rotation. A robotic
localisation study showed that using spherical harmonics (SH), a representation in
the frequency domain, combined with extracted sky can greatly help robots localise
on uneven terrain. Results showed improved performance to state of the art point
feature localisation methods on fast bumpy tracks (Stone et al., 2016a).
In the second part, an approach to understand how insects perform a navigational
task called path integration was attempted by modelling part of the brain of the sweat
bee Megalopta genalis. A recent discovery that two populations of cells act as a celestial
compass and visual odometer, respectively, led to the hypothesis that circuitry at their
point of convergence in the central complex (CX) could give rise to path integration.
A firing rate-based model was developed with connectivity derived from the overlap
of observed neural arborisations of individual cells and successfully used to build up
a home vector and steer an agent back to the nest (Stone et al., 2016b). This approach
has the appeal that neural circuitry is highly conserved across insects, so findings
here could have wide implications for insect navigation in general. The developed
model is the first functioning path integrator that is based on individual cellular
connections
- …