16 research outputs found
Semantic Perceptual Image Compression using Deep Convolution Networks
It has long been considered a significant problem to improve the visual
quality of lossy image and video compression. Recent advances in computing
power together with the availability of large training data sets has increased
interest in the application of deep learning cnns to address image recognition
and image processing tasks. Here, we present a powerful cnn tailored to the
specific task of semantic image understanding to achieve higher visual quality
in lossy compression. A modest increase in complexity is incorporated to the
encoder which allows a standard, off-the-shelf jpeg decoder to be used. While
jpeg encoding may be optimized for generic images, the process is ultimately
unaware of the specific content of the image to be compressed. Our technique
makes jpeg content-aware by designing and training a model to identify multiple
semantic regions in a given image. Unlike object detection techniques, our
model does not require labeling of object positions and is able to identify
objects in a single pass. We present a new cnn architecture directed
specifically to image compression, which generates a map that highlights
semantically-salient regions so that they can be encoded at higher quality as
compared to background regions. By adding a complete set of features for every
class, and then taking a threshold over the sum of all feature activations, we
generate a map that highlights semantically-salient regions so that they can be
encoded at a better quality compared to background regions. Experiments are
presented on the Kodak PhotoCD dataset and the MIT Saliency Benchmark dataset,
in which our algorithm achieves higher visual quality for the same compressed
size.Comment: Accepted to Data Compression Conference, 11 pages, 5 figure
Performance Analysis of RIS-Aided NOMA Networks in & Generalized Fading Channel
For forthcoming 5G networks, Non-Orthogonal Multiple Access (NOMA) is a very
promising techniques. and in today's world, Line of Sight communication is
becoming increasingly harder to achieve. Hence, technologies like
Reconfigurable Intelligent Surfaces (RIS) emerge. RIS-aided NOMA networks is a
widely researched implementation of RIS. The environment where these networks
are employed are non-homogeneous & non-linear in nature. The effectiveness of
these systems must thus be evaluated using generalized fading channels. In this
paper, the performance of a RIS-aided NOMA is compared with conventional NOMA
in alpha-mu and kappa-mu channels. This paper also shows that the well-known
fading distribution are special cases of these generalized fading channels,
both analytically and through simulation.Comment: Reconfigurable Intelligent Surfaces, Non-Orthogonal Multiple Access,
Generalized Fading Channels, 5G & Beyond 5G Network
A Traffic Control Framework for Uncrewed Aircraft Systems
The exponential growth of Advanced Air Mobility (AAM) services demands
assurances of safety in the airspace. This research a Traffic Control Framework
(TCF) for developing digital flight rules for Uncrewed Aircraft System (UAS)
flying in designated air corridors. The proposed TCF helps model, deploy, and
test UAS control, agents, regardless of their hardware configurations. This
paper investigates the importance of digital flight rules in preventing
collisions in the context of AAM. TCF is introduced as a platform for
developing strategies for managing traffic towards enhanced autonomy in the
airspace. It allows for assessment and evaluation of autonomous navigation,
route planning, obstacle avoidance, and adaptive decision making for UAS. It
also allows for the introduction and evaluation of advance technologies
Artificial Intelligence (AI) and Machine Learning (ML) in a simulation
environment before deploying them in the real world. TCF can be used as a tool
for comprehensive UAS traffic analysis, including KPI measurements. It offers
flexibility for further testing and deployment laying the foundation for
improved airspace safety - a vital aspect of UAS technological advancement.
Finally, this papers demonstrates the capabilities of the proposed TCF in
managing UAS traffic at intersections and its impact on overall traffic flow in
air corridors, noting the bottlenecks and the inverse relationship safety and
traffic volume.Comment: 6 pages, 7 figure