4,345 research outputs found
Hybrid-Vehcloud: An Obstacle Shadowing Approach for VANETs in Urban Environment
Routing of messages in Vehicular Ad-hoc Networks (VANETs) is challenging due
to obstacle shadowing regions with high vehicle densities, which leads to
frequent disconnection problems and blocks radio wave propagation between
vehicles. Previous researchers used multi-hop, vehicular cloud or roadside
infrastructures to solve the routing issue among the vehicles, but they suffer
from significant packet delays and frequent packet losses arising from obstacle
shadowing. We proposed a vehicular cloud based hybrid technique called
Hybrid-Vehcloud to disseminate messages in obstacle shadowing regions, and
multi-hop technique to disseminate messages in non-obstacle shadowing regions.
The novelty of our approach lies in the fact that our proposed technique
dynamically adapts between obstacle shadowing and non-obstacle shadowing
regions. Simulation based performance analysis of Hybrid-Vehcloud showed
improved performance over Cloud-assisted Message Downlink Dissemination Scheme
(CMDS), Cross-Layer Broadcast Protocol (CLBP) and Cloud-VANET schemes at high
vehicle densities
DFCV: A Novel Approach for Message Dissemination in Connected Vehicles using Dynamic Fog
Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for
enhancing road safety. Routing of messages in VANET is challenging due to
packet delays arising from high mobility of vehicles, frequently changing
topology, and high density of vehicles, leading to frequent route breakages and
packet losses. Previous researchers have used either mobility in vehicular fog
computing or cloud computing to solve the routing issue, but they suffer from
large packet delays and frequent packet losses. We propose Dynamic Fog for
Connected Vehicles (DFCV), a fog computing based scheme which dynamically
creates, increments and destroys fog nodes depending on the communication
needs. The novelty of DFCV lies in providing lower delays and guaranteed
message delivery at high vehicular densities. Simulations were conducted using
hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that
DFCV ensures efficient resource utilization, lower packet delays and losses at
high vehicle densities
Asian Indian Perceptions of Normality: A Qualitative Study
Normal mental health has always been defined from a Euro-centric worldview that excludes non-Westem cultures. In fact, what is normal is biased against non-Westem cultural ideals that influenced the definition of mental health. The difference between Eastern and Western cultural values suggest that the two cultures may also have differing views on the definition of normal mental health. The most commonly accepted definition of normality currently in use in the West is based on the models of health, utopia, average, transactional systems, and pragmatism. However, people from non-European cultures, such as Asian Indians, may not be represented by these current parameters of mental health and illness.
In this study, the construct of normality was investigated from an Asian Indian perspective. Specifically, interviews were conducted with Asian Indian graduate students in which participants were asked to discuss their perceptions of normal mental health. A Consensual Qualitative Research analysis strategy was then conducted. Five domains were created: Perceptions of Normal, Perceptions of Abnormal, Cause of Mental Illness, Criteria Used to Differentiate Normal from Abnormal, and Difficulties in Defining Normal. The categories within these domains were discussed as they related to psychological treatment services for international students such as well as implications for future research
Vision Encoder-Decoder Models for AI Coaching
This research paper introduces an innovative AI coaching approach by
integrating vision-encoder-decoder models. The feasibility of this method is
demonstrated using a Vision Transformer as the encoder and GPT-2 as the
decoder, achieving a seamless integration of visual input and textual
interaction. Departing from conventional practices of employing distinct models
for image recognition and text-based coaching, our integrated architecture
directly processes input images, enabling natural question-and-answer dialogues
with the AI coach. This unique strategy simplifies model architecture while
enhancing the overall user experience in human-AI interactions. We showcase
sample results to demonstrate the capability of the model. The results
underscore the methodology's potential as a promising paradigm for creating
efficient AI coach models in various domains involving visual inputs.
Importantly, this potential holds true regardless of the particular visual
encoder or text decoder chosen. Additionally, we conducted experiments with
different sizes of GPT-2 to assess the impact on AI coach performance,
providing valuable insights into the scalability and versatility of our
proposed methodology.Comment: 6 pages, 2 figure
- …