6,285 research outputs found
Studying sign processes in the emergence of communication
Communication depends on the production and interpretation \ud
of representations, but the study of representational processes \ud
underlying communication finds little discussion in \ud
computational experiments. Here we present an experiment \ud
on the emergence of both interpretation and production of \ud
multiple representations, with multiple referents, where \ud
referential processes can be tracked. Results show the \ud
dynamics of semiotic processes during the evolution of \ud
artificial creatures and the emergence of a variety of semiotic \ud
processes, such as sign production, sign interpretation, and \ud
sign-object-interpretant relations
On a Joint Physical Layer and Medium Access Control Sublayer Design for Efficient Wireless Sensor Networks and Applications
Wireless sensor networks (WSNs) are distributed networks comprising small sensing devices equipped with a processor, memory, power source, and often with the capability for short range wireless communication. These networks are used in various applications, and have created interest in WSN research and commercial uses, including industrial, scientific, household, military, medical and environmental domains. These initiatives have also been stimulated by the finalisation of the IEEE 802.15.4 standard, which defines the medium access control (MAC) and physical layer (PHY) for low-rate wireless personal area networks (LR-WPAN).
Future applications may require large WSNs consisting of huge numbers of inexpensive wireless sensor nodes with limited resources (energy, bandwidth), operating in harsh environmental conditions. WSNs must perform reliably despite novel resource constraints including limited bandwidth, channel errors, and nodes that have limited operating energy. Improving resource utilisation and quality-of-service (QoS), in terms of reliable connectivity and energy efficiency, are major challenges in WSNs. Hence, the development of new WSN applications with severe resource constraints will require innovative solutions to overcome the above issues as well as improving the robustness of network components, and developing sustainable and cost effective implementation models.
The main purpose of this research is to investigate methods for improving the performance of WSNs to maintain reliable network connectivity, scalability and energy efficiency. The study focuses on the IEEE 802.15.4 MAC/PHY layers and the carrier sense multiple access with collision avoidance (CSMA/CA) based networks. First, transmission power control (TPC) is investigated in multi and single-hop WSNs using typical hardware platform parameters via simulation and numerical analysis. A novel approach to testing TPC at the physical layer is developed, and results show that contrary to what has been reported from previous studies, in multi-hop networks TPC does not save energy.
Next, the network initialization/self-configuration phase is addressed through investigation of the 802.15.4 MAC beacon interval setting and the number of associating nodes, in terms of association delay with the coordinator. The results raise doubt whether that the association energy consumption will outweigh the benefit of duty cycle power management for larger beacon intervals as the number of associating nodes increases.
The third main contribution of this thesis is a new cross layer (PHY-MAC) design to improve network energy efficiency, reliability and scalability by minimising packet collisions due to hidden nodes. This is undertaken in response to findings in this thesis on the IEEE 802.15.4 MAC performance in the presence of hidden nodes. Specifically, simulation results show that it is the random backoff exponent that is of paramount importance for resolving collisions and not the number of times the channel is sensed before transmitting. However, the random backoff is ineffective in the presence of hidden nodes. The proposed design uses a new algorithm to increase the sensing coverage area, and therefore greatly reduces the chance of packet collisions due to hidden nodes. Moreover, the design uses a new dynamic transmission power control (TPC) to further reduce energy consumption and interference. The above proposed changes can smoothly coexist with the legacy 802.15.4 CSMA/CA.
Finally, an improved two dimensional discrete time Markov chain model is proposed to capture the performance of the slotted 802.15.4 CSMA/CA. This model rectifies minor issues apparent in previous studies. The relationship derived for the successful transmission probability, throughput and average energy consumption, will provide better performance predictions. It will also offer greater insight into the strengths and weaknesses of the MAC operation, and possible enhancement opportunities.
Overall, the work presented in this thesis provides several significant insights into WSN performance improvements with both existing protocols and newly designed protocols.
Finally, some of the numerous challenges for future research are described
Unifying Two-Stream Encoders with Transformers for Cross-Modal Retrieval
Most existing cross-modal retrieval methods employ two-stream encoders with
different architectures for images and texts, \textit{e.g.}, CNN for images and
RNN/Transformer for texts. Such discrepancy in architectures may induce
different semantic distribution spaces and limit the interactions between
images and texts, and further result in inferior alignment between images and
texts. To fill this research gap, inspired by recent advances of Transformers
in vision tasks, we propose to unify the encoder architectures with
Transformers for both modalities. Specifically, we design a cross-modal
retrieval framework purely based on two-stream Transformers, dubbed
\textbf{Hierarchical Alignment Transformers (HAT)}, which consists of an image
Transformer, a text Transformer, and a hierarchical alignment module. With such
identical architectures, the encoders could produce representations with more
similar characteristics for images and texts, and make the interactions and
alignments between them much easier. Besides, to leverage the rich semantics,
we devise a hierarchical alignment scheme to explore multi-level
correspondences of different layers between images and texts. To evaluate the
effectiveness of the proposed HAT, we conduct extensive experiments on two
benchmark datasets, MSCOCO and Flickr30K. Experimental results demonstrate that
HAT outperforms SOTA baselines by a large margin. Specifically, on two key
tasks, \textit{i.e.}, image-to-text and text-to-image retrieval, HAT achieves
7.6\% and 16.7\% relative score improvement of Recall@1 on MSCOCO, and 4.4\%
and 11.6\% on Flickr30k respectively. The code is available at
\url{https://github.com/LuminosityX/HAT}.Comment: Accepted at ACM Multimedia 202
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