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Every communication system has a receiver and a transmitter. Irrespective if it is wired or wireless.The future of wireless communication consists of a massive number of transmitters and receivers. The question arises, can we use computer vision to help wireless communication? To satisfy the high data requirement, a large number of antennas are required. The devices that employ large-antenna arrays have other sensors such as RGB camera, depth camera, or LiDAR sensors.These vision sensors help us overcome the non-trivial wireless communication challenges, such as beam blockage prediction and hand-over prediction.This is further motivated by the recent advances in deep learning and computer vision that can extract high-level semantics from complex visual scenes, and the increasing interest of leveraging machine/deep learning tools in wireless communication problems.[1] <br/><br/>The research was focused solely based on technology like 3D cameras,object detection and object tracking using Computer vision and compression techniques. The main objective of using computer vision was to make Milli-meter Wave communication more robust, and to collect more data for the machine learning algorithms. Pre-build lossless and lossy compression algorithms, such as FFMPEG, were used in the research. An algorithm was developed that could use 3D cameras and machine learning models such as YOLOV3, to track moving objects using servo motors and low powered computers like the raspberry pi or the Jetson Nano. In other words, the receiver could track the highly mobile transmitter in 1 dimension using a 3D camera. Not only that, during the research, the transmitter was loaded on a DJI M600 pro drone, and then machine learning and object tracking was used to track the highly mobile drone. In order to build this machine learning model and object tracker, collecting data like depth, RGB images and position coordinates were the first yet the most important step. GPS coordinates from the DJI M600 were also pulled and were successfully plotted on google earth. This proved to be very useful during data collection using a drone and for the future applications of position estimation for a drone using machine learning. <br/><br/>Initially, images were taken from transmitter camera every second,and those frames were then converted to a text file containing hex-decimal values. Each text file was then transmitted from the transmitter to receiver, and on the receiver side, a python code converted the hex-decimal to JPG. This would give an efect of real time video transmission. However, towards the end of the research, an industry standard, real time video was streamed using pre-built FFMPEG modules, GNU radio and Universal Software Radio Peripheral (USRP). The transmitter camera was a PI-camera. More details will be discussed as we further dive deep into this research report.
ing systems. Performance of ROICs affect the quality of images obtained from IR
imaging systems. Contemporary infrared imaging applications demand ROICs that
can support large dynamic range, high frame rate, high output data rate, at low
cost, size and power. Some of these applications are military surveillance, remote
sensing in space and earth science missions and medical diagnosis. This work focuses
on developing a ROIC unit cell prototype for National Aeronautics and Space Ad
ministration(NASA), Jet Propulsion Laboratory’s(JPL’s) space applications. These
space applications also demand high sensitivity, longer integration times(large well
capacity), wide operating temperature range, wide input current range and immunity
to radiation events such as Single Event Latchup(SEL).
This work proposes a digital ROIC(DROIC) unit cell prototype of 30ux30u size,
to be used mainly with NASA JPL’s High Operating Temperature Barrier Infrared
Detectors(HOT BIRDs). Current state of the art DROICs achieve a dynamic range
of 16 bits using advanced 65-90nm CMOS processes which adds a lot of cost overhead.
The DROIC pixel proposed in this work uses a low cost 180nm CMOS process and
supports a dynamic range of 20 bits operating at a low frame rate of 100 frames per
second(fps), and a dynamic range of 12 bits operating at a high frame rate of 5kfps.
The total electron well capacity of this DROIC pixel is 1.27 billion electrons, enabling
integration times as long as 10ms, to achieve better dynamic range. The DROIC unit
cell uses an in-pixel 12-bit coarse ADC and an external 8-bit DAC based fine ADC.
The proposed DROIC uses layout techniques that make it immune to radiation up to
300krad(Si) of total ionizing dose(TID) and single event latch-up(SEL). It also has a
wide input current range from 10pA to 1uA and supports detectors operating from
Short-wave infrared (SWIR) to longwave infrared (LWIR) regions.
Back-propagation imaging methods are modified to reconstruct the image of the 2-D scenario (range, cross-range). The reflected signal from the target is collected using a SAR (Synthetic Aperture Radar) set-up in a lab environment. This imaging technique is verified using both full-wave 3-D numerical analysis models and lab experiments.
The novel imaging approach of non-line-of-sight-imaging could enable novel applications in rescue and surveillance missions, highly accurate localization methods, and improve channel estimation in mmWave and sub-mmWave wireless communication systems.
To have a better acknowledgment of constant false alarm rate approaches performance, three clutter models, Gamma, Weibull, and Log-normal, have been introduced to evaluate the detection's capability of each constant false alarm rate algorithm.
The order statistical constant false alarm rate approach outperforms other conventional constant false alarm rate methods, especially in clutter evolved environments. However, this method requires high power consumption due to repeat sorting.
In the automotive RADAR system, the computational complexity of algorithms is essential because this system is in real-time. Therefore, the algorithms must be fast and efficient to ensure low power consumption and processing time.
The reduced computational complexity implementations of cell-averaging and order statistic constant false alarm rate were explored. Their big O and processing time has been reduced.