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 ϕ 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 ϕ, 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