11,861 research outputs found
A New Vehicle Localization Scheme Based on Combined Optical Camera Communication and Photogrammetry
The demand for autonomous vehicles is increasing gradually owing to their
enormous potential benefits. However, several challenges, such as vehicle
localization, are involved in the development of autonomous vehicles. A simple
and secure algorithm for vehicle positioning is proposed herein without
massively modifying the existing transportation infrastructure. For vehicle
localization, vehicles on the road are classified into two categories: host
vehicles (HVs) are the ones used to estimate other vehicles' positions and
forwarding vehicles (FVs) are the ones that move in front of the HVs. The FV
transmits modulated data from the tail (or back) light, and the camera of the
HV receives that signal using optical camera communication (OCC). In addition,
the streetlight (SL) data are considered to ensure the position accuracy of the
HV. Determining the HV position minimizes the relative position variation
between the HV and FV. Using photogrammetry, the distance between FV or SL and
the camera of the HV is calculated by measuring the occupied image area on the
image sensor. Comparing the change in distance between HV and SLs with the
change in distance between HV and FV, the positions of FVs are determined. The
performance of the proposed technique is analyzed, and the results indicate a
significant improvement in performance. The experimental distance measurement
validated the feasibility of the proposed scheme
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future
wireless communications owing to its potentials for cost-effective network
deployment and high data rate. There are several implementation issues in the
OWC which have not been encountered in radio frequency wireless communications.
First, practical OWC transmitters need an illumination control on color,
intensity, and luminance, etc., which poses complicated modulation design
challenges. Furthermore, signal-dependent properties of optical channels raise
non-trivial challenges both in modulation and demodulation of the optical
signals. To tackle such difficulties, deep learning (DL) technologies can be
applied for optical wireless transceiver design. This article addresses recent
efforts on DL-based OWC system designs. A DL framework for emerging image
sensor communication is proposed and its feasibility is verified by simulation.
Finally, technical challenges and implementation issues for the DL-based
optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on
Applications of Artificial Intelligence in Wireless Communication
Advances on CMOS image sensors
This paper offers an introduction to the technological advances of image sensors designed using
complementary metal–oxide–semiconductor (CMOS) processes along the last decades. We review
some of those technological advances and examine potential disruptive growth directions for CMOS
image sensors and proposed ways to achieve them. Those advances include breakthroughs on
image quality such as resolution, capture speed, light sensitivity and color detection and advances on
the computational imaging. The current trend is to push the innovation efforts even further as the
market requires higher resolution, higher speed, lower power consumption and, mainly, lower cost
sensors. Although CMOS image sensors are currently used in several different applications from
consumer to defense to medical diagnosis, product differentiation is becoming both a requirement and
a difficult goal for any image sensor manufacturer. The unique properties of CMOS process allows the
integration of several signal processing techniques and are driving the impressive advancement of the
computational imaging. With this paper, we offer a very comprehensive review of methods,
techniques, designs and fabrication of CMOS image sensors that have impacted or might will impact
the images sensor applications and markets
Vehicular Visible Light Communications
Vehicular communications are foreseen to play a key role to increase road safety and realize autonomous driving. In addition to the radio frequency (RF)-based dedicated short range communication (DSRC) and long-term evolution (LTE) communication technologies, vehicular visible light communication (V2LC) is proposed as a complementary solution, utilizing readily deployed vehicle light emitting diode (LED) lights as transmitter with image sensors such as photodetector (PD) and camera as the receivers. V2LC fundamentals including transmitter and receiver characteristics with dimming capabilities are reviewed in this chapter. Depending on the field measurements using off-the-shelf automotive LED light, communication constraints are demonstrated. Moreover, considering the line-of-sight (LoS) characteristics, security aspects of V2LC is compared with the DSRC for a practical vehicle-to-vehicle (V2V) communication scenario. Finally, superiority of V2LC in terms of communication security with the proposed SecVLC method is demonstrated through simulation results
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