Energy-Constrained SWIPT Networks:Enhancing Physical Layer Security With FD Self-Jamming

In this paper, we investigate the secrecy performance of energy-constrained wireless-powered networks with considering the passive eavesdropping scenario, where the simultaneous wireless information and power transfer-based full-duplex self-jamming (SWIPT-FDSJ) scheme is developed. The maximal ratio transmission protocol is applied at the multi-antenna source such that the wireless signals are designated to the destination directly. Besides, the energy harvesting and full-duplex self-jamming operations are adopted at the energy-constrained destination to prolong its lifetime as well as to confuse the eavesdropper. Specifically, the exact and asymptotic closed-form expressions of the connection outage probability (COP), the secrecy outage probability (SOP), and the secrecy throughput of the proposed system are obtained, based on which we optimize the time-switching ratio to maximize the secrecy throughput. We also degenerate the proposed SWIPT-FDSJ scheme to the reduced half-duplex with no self-jamming (HDNSJ) scheme. The finds suggest that in the HDNSJ scheme, adding the antenna number of the source only benefits the COP performance, but has no impact on the SOP performance. By contrast, it will promote the COP and SOP performance at the same time in the SWIPT-FDSJ scheme, which eventually results in the great improvement of secrecy throughput. In addition, we present the practical application condition of the SWIPT-FDSJ scheme. It is demonstrated that the secrecy throughput performance of the SWIPT-FDSJ scheme is much superior to the HDNSJ scheme on condition that the application condition is satisfied.</p

Carotenoids and their isomers : color pigments in fruits and vegetables.

Fruits and vegetables are colorful pigment-containing food sources. Owing to their nutritional benefits and phytochemicals, they are considered as ‘functional food ingredients’. Carotenoids are some of the most vital colored phytochemicals, occurring as all-trans and cis-isomers, and accounting for the brilliant colors of a variety of fruits and vegetables. Carotenoids extensively studied in this regard include β-carotene, lycopene, lutein and zeaxanthin. Coloration of fruits and vegetables depends on their growth maturity, concentration of carotenoid isomers, and food processing methods. This article focuses more on several carotenoids and their isomers present in different fruits and vegetables along with their concentrations. Carotenoids and their geometric isomers also play an important role in protecting cells from oxidation and cellular damages

Tri-modal Confluence with Temporal Dynamics for Scene Graph Generation in Operating Rooms

A comprehensive understanding of surgical scenes allows for monitoring of the surgical process, reducing the occurrence of accidents and enhancing efficiency for medical professionals. Semantic modeling within operating rooms, as a scene graph generation (SGG) task, is challenging since it involves consecutive recognition of subtle surgical actions over prolonged periods. To address this challenge, we propose a Tri-modal (i.e., images, point clouds, and language) confluence with Temporal dynamics framework, termed TriTemp-OR. Diverging from previous approaches that integrated temporal information via memory graphs, our method embraces two advantages: 1) we directly exploit bi-modal temporal information from the video streaming for hierarchical feature interaction, and 2) the prior knowledge from Large Language Models (LLMs) is embedded to alleviate the class-imbalance problem in the operating theatre. Specifically, our model performs temporal interactions across 2D frames and 3D point clouds, including a scale-adaptive multi-view temporal interaction (ViewTemp) and a geometric-temporal point aggregation (PointTemp). Furthermore, we transfer knowledge from the biomedical LLM, LLaVA-Med, to deepen the comprehension of intraoperative relations. The proposed TriTemp-OR enables the aggregation of tri-modal features through relation-aware unification to predict relations so as to generate scene graphs. Experimental results on the 4D-OR benchmark demonstrate the superior performance of our model for long-term OR streaming.Comment: 10 pages, 4 figures, 3 table

ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks

Spiking neural networks (SNNs) have manifested remarkable advantages in power consumption and event-driven property during the inference process. To take full advantage of low power consumption and improve the efficiency of these models further, the pruning methods have been explored to find sparse SNNs without redundancy connections after training. However, parameter redundancy still hinders the efficiency of SNNs during training. In the human brain, the rewiring process of neural networks is highly dynamic, while synaptic connections maintain relatively sparse during brain development. Inspired by this, here we propose an efficient evolutionary structure learning (ESL) framework for SNNs, named ESL-SNNs, to implement the sparse SNN training from scratch. The pruning and regeneration of synaptic connections in SNNs evolve dynamically during learning, yet keep the structural sparsity at a certain level. As a result, the ESL-SNNs can search for optimal sparse connectivity by exploring all possible parameters across time. Our experiments show that the proposed ESL-SNNs framework is able to learn SNNs with sparse structures effectively while reducing the limited accuracy. The ESL-SNNs achieve merely 0.28% accuracy loss with 10% connection density on the DVS-Cifar10 dataset. Our work presents a brand-new approach for sparse training of SNNs from scratch with biologically plausible evolutionary mechanisms, closing the gap in the expressibility between sparse training and dense training. Hence, it has great potential for SNN lightweight training and inference with low power consumption and small memory usage

CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation

We propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously calibrates depth and RGB features in the kernel and mask branches to generate instance-aware kernels and mask features. CalibNet consists of three simple modules, a dynamic interactive kernel (DIK) and a weight-sharing fusion (WSF), which work together to generate effective instance-aware kernels and integrate cross-modal features. To improve the quality of depth features, we incorporate a depth similarity assessment (DSA) module prior to DIK and WSF. In addition, we further contribute a new DSIS dataset, which contains 1,940 images with elaborate instance-level annotations. Extensive experiments on three challenging benchmarks show that CalibNet yields a promising result, i.e., 58.0% AP with 320*480 input size on the COME15K-N test set, which significantly surpasses the alternative frameworks. Our code and dataset are available at: https://github.com/PJLallen/CalibNet.Comment: This work has been accepted by TIP 202