115 research outputs found
Fundamental properties of on-off transmission scheme for wiretap channels
This work reveals some fundamental properties of
an on-off transmission (OOT) scheme, in which a transmitter
sends signals occasionally as per the capacity of the main channel
in order to achieve physical layer security. To this end, we first
identify the widely used hybrid secrecy outage probability as
a function of the transmission probability and the conditional
secrecy outage probability of the OOT scheme. This indicates,
for the first time, that the hybrid secrecy outage probability
can be achieved by the OOT scheme. We then derive a lower
bound on the conditional secrecy outage probability of the OOT
scheme in case of transmission, which is solely determined by
the average signal-to-noise ratios (SNRs) of the main channel
and eavesdropper’s channel. Finally, we re-investigate the OOT
scheme within an absolutely completely passive eavesdropping
scenario, in which even the average SNR of the eavesdropper’s
channel is not required. Specifically, we derive an easy-evaluated
expression for the average conditional secrecy outage probability
of the OOT scheme by adopting an annulus threat model.ARC Discovery Projects Grant DP150103905
OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation
This paper presents OmniDataComposer, an innovative approach for multimodal
data fusion and unlimited data generation with an intent to refine and
uncomplicate interplay among diverse data modalities. Coming to the core
breakthrough, it introduces a cohesive data structure proficient in processing
and merging multimodal data inputs, which include video, audio, and text. Our
crafted algorithm leverages advancements across multiple operations such as
video/image caption extraction, dense caption extraction, Automatic Speech
Recognition (ASR), Optical Character Recognition (OCR), Recognize Anything
Model(RAM), and object tracking. OmniDataComposer is capable of identifying
over 6400 categories of objects, substantially broadening the spectrum of
visual information. It amalgamates these diverse modalities, promoting
reciprocal enhancement among modalities and facilitating cross-modal data
correction. \textbf{The final output metamorphoses each video input into an
elaborate sequential document}, virtually transmuting videos into thorough
narratives, making them easier to be processed by large language models. Future
prospects include optimizing datasets for each modality to encourage unlimited
data generation. This robust base will offer priceless insights to models like
ChatGPT, enabling them to create higher quality datasets for video captioning
and easing question-answering tasks based on video content. OmniDataComposer
inaugurates a new stage in multimodal learning, imparting enormous potential
for augmenting AI's understanding and generation of complex, real-world data
Evaluation of machining performance of STAVAX with PCBN tools
A study was undertaken to investigate the wear characteristics of polycrystalline cubic boron nitride (PCBN) cutting tools and surface integrity during machining of STAVAX (specialised stainless steel) with and without coolant. Plastic deformation and formation of overtempered martensite and white layer (untempered martensite) were the dominant subsurface and surface defects. It was found that decreasing the hardness of the STAVAX from 55 HRC to 40 HRC could result in fracture on the flank face, leading to a deterioration of the surface finish. It was observed that low CBN content tools (60%CBN/40%TiN) exhibited greater fracture resistance than high CBN content tools (85%CBN/15%TiN, 90%CBN/10%Co). Although coolant could not bring about a reduction in the flank wear, it was effective in reducing the subsurface and surface defects, and in preventing chipping of the tool edge, leading to an improved surface finish. A superior surface finish (Ra<0.3 μm) was obtained with cutting fluid using a tool with a radius of 0.8 mm, depth of cut of 0.05 mm and feed rate of 0.05 mm/rev
Three Artificial-Noise-Aided Secure Transmission Schemes in Wiretap Channels
We examine the secrecy performance of three
artificial-noise-aided secure transmission schemes, namely, the
partially-adaptive, fully-adaptive, and on-off schemes. To this
end, we provide new analysis to facilitate the optimization of the
fraction Ï• of the transmit power allocated to the useful signal
and redundancy rate RE. Surprisingly, our examination indicates
that the partially-adaptive scheme, in which only the codeword
rate RB varies with the instantaneous channel gains, significantly
outperforms the on-off scheme, in which both RB and RE vary.
This performance gain can be characterized in terms of a higher
average secrecy rate, subject to an upper bound on the secrecy
outage probability. Furthermore, our results also demonstrate
that the partially-adaptive scheme can achieve almost the same
secrecy performance as the fully-adaptive scheme, which is of a
much higher complexity, where Ï•, RB, and RE all vary with the
instantaneous channel gains
Artificial Noise: Transmission Optimization in Multi-Input Single-Output Wiretap Channels
We analyze and optimize the secrecy performance of
artificial noise (AN) in multi-input single-output wiretap channels
with multiple antennas at the transmitter and a single antenna at
the receiver and the eavesdropper. We consider two transmission
schemes: 1) an on-off transmission scheme with a constant secrecy
rate for all transmission periods, and 2) an adaptive transmission
scheme with a varying secrecy rate during each transmission
period. For the on-off transmission scheme, an easy-to-compute
expression is derived for the hybrid outage probability, which
allows us to evaluate the transmission outage probability and the
secrecy outage probability. For the adaptive transmission scheme
where transmission outage does not occur, we derive a closedform
expression for the secrecy outage probability. Using these
expressions, we determine the optimal power allocation between
the information signal and the AN signal and also determine the
optimal secrecy rate such that the effective secrecy throughput
is maximized for both transmission schemes. We show that the
maximum effective secrecy throughput requires more power to
be allocated to the AN signal when the quality of the transmitterreceiver
channel or the transmitter-eavesdropper channel improves.
We also show that both transmission schemes achieve
a higher maximum effective secrecy throughput while incurring
a lower secrecy outage probability than existing schemes.ARC Discovery Projects Grant DP150103905
RLLTE: Long-Term Evolution Project of Reinforcement Learning
We present RLLTE: a long-term evolution, extremely modular, and open-source
framework for reinforcement learning (RL) research and application. Beyond
delivering top-notch algorithm implementations, RLLTE also serves as a toolkit
for developing algorithms. More specifically, RLLTE decouples the RL algorithms
completely from the exploitation-exploration perspective, providing a large
number of components to accelerate algorithm development and evolution. In
particular, RLLTE is the first RL framework to build a complete and luxuriant
ecosystem, which includes model training, evaluation, deployment, benchmark
hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set
standards for RL engineering practice and be highly stimulative for industry
and academia.Comment: 22 pages, 15 figure
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