3,243 research outputs found
Non-Orthogonal Multiplexing in the FBL Regime Enhances Physical Layer Security with Deception
We propose a new security framework for physical layer security (PLS) in the
finite blocklength (FBL) regime that incorporates deception technology,
allowing for active countermeasures against potential eavesdroppers. Using a
symmetric block cipher and power-domain non-orthogonal multiplexing (NOM), our
approach is able to achieve high secured reliability while effectively
deceiving the eavesdropper, and can benefit from increased transmission power.
This work represents a promising direction for future research in PLS with
deception technology.Comment: Submitted to SPAWC 2023 (appendices are omitted in the submitted
version due to length limit
Time-Energy-Constrained Closed-Loop FBL Communication for Dependable MEC
The deployment of multi-access edge computing (MEC) is paving the way towards
pervasive intelligence in future 6G networks. This new paradigm also proposes
emerging requirements of dependable communications, which goes beyond the
ultra-reliable low latency communication (URLLC), focusing on the performance
of a closed loop instead of that of an unidirectional link. This work studies
the simple but efficient one-shot transmission scheme, investigating the
closed-loop-reliability-optimal policy of blocklength allocation under
stringent time and energy constraints.Comment: Accepted for publication at CSCN 2021 V1: accepted version V2: minor
correction in the modulation order V3: corrections to resolve chaos caused by
different normalizations of the FBL PER equation, model figure file updated
in H
Fairness for Freshness: Optimal Age of Information Based OFDMA Scheduling with Minimal Knowledge
It is becoming increasingly clear that an important task for wireless
networks is to minimize the age of information (AoI), i.e., the timeliness of
information delivery. While mainstream approaches generally rely on the
real-time observation of user AoI and channel state, there has been little
attention to solve the problem in a complete (or partial) absence of such
knowledge. In this article, we present a novel study to address the optimal
blind radio resource scheduling problem in orthogonal frequency division
multiplexing access (OFDMA) systems towards minimizing long-term average AoI,
which is proven to be the composition of time-domain-fair clustered round-robin
and frequency-domain-fair intra-cluster sub-carrier assignment. Heuristic
solutions that are near-optimal as shown by simulation results are also
proposed to effectively improve the performance upon presence of various
degrees of extra knowledge, e.g., channel state and AoI.Comment: Accepted on 05.06.2021 by the IEEE Transactions on Wireless
Communications for publicatio
Aquabis(methacrylato-κO)bis(pyridine-κN)copper(II)
In the crystal structure of the title complex, [Cu(C4H5O2)2(C5H5N)2(H2O)], the CuII cation is located on a twofold rotation axis and coordinated by two methylacrylate anions, two pyridine ligands and one water molecule in a distorted square-pyramidal geometry. The coordinated water molecule is also located on the twofold axis. In the crystal structure O—H⋯O hydrogen bonds link the molecules, forming chains along the c axis
Blockchain-enabled Wireless IoT Networks with Multiple Communication Connections
Blockchain-enabled wireless network has been recognized as an emerging network architecture to be widely employed into the Internet of Things (IoT) ecosystems for establishing trust and consensus mechanisms without the involvement of a third party. However, the uncertainty and vulnerability of wireless channels among the IoT nodes may pose a serious challenge to facilitate the deployment of blockchain in wireless networks. In this paper, we first present a generic system model for blockchain enabled wireless networks with multiple communication connections, where the number of communication connections between a client IoT node and the blockchain full nodes can be any arbitrary positive integer to satisfy different security requirements. Based on the proposed spatial-temporal network model, we theoretically calculate the transmission successful probability and the required communication throughput to support a wireless blockchain network. Finally, simulation results validate the accuracy of our theoretical analysis
ADAPTIVE ROBUST CASCADE FORCE CONTROL OF 1-DOF JOINT EXOSKELETON FOR HUMAN PERFORMANCE AUGMENTATION
ABSTRACT The control objective of exoskeleton for human performance augmentation is to minimize the human machine interaction force while carrying external loads and following human motion. This paper addresses the dynamics and the interaction force control of a 1-DOF hydraulically actuated joint exoskeleton. A spring with unknown stiffness is used to model the humanmachine interface. A cascade force control method is adopted with high-level controller generating the reference position command while low level controller doing motion tracking. Adaptive robust control(ARC) algorithm is developed for both two controllers to deal with the effect of parametric uncertainties and uncertain nonlinearities of the system. The proposed adaptive robust cascade force controller can achieve small human-machine interaction force and good robust performance to model uncer-
Brachial Plexus Nerve Trunk Segmentation Using Deep Learning: A Comparative Study with Doctors' Manual Segmentation
Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve
block anesthesia method that can observe the target nerve and its surrounding
structures, the puncture needle's advancement, and local anesthetics spread in
real-time. The key in UGNB is nerve identification. With the help of deep
learning methods, the automatic identification or segmentation of nerves can be
realized, assisting doctors in completing nerve block anesthesia accurately and
efficiently. Here, we establish a public dataset containing 320 ultrasound
images of brachial plexus (BP). Three experienced doctors jointly produce the
BP segmentation ground truth and label brachial plexus trunks. We design a
brachial plexus segmentation system (BPSegSys) based on deep learning. BPSegSys
achieves experienced-doctor-level nerve identification performance in various
experiments. We evaluate BPSegSys' performance in terms of
intersection-over-union (IoU), a commonly used performance measure for
segmentation experiments. Considering three dataset groups in our established
public dataset, the IoU of BPSegSys are 0.5238, 0.4715, and 0.5029,
respectively, which exceed the IoU 0.5205, 0.4704, and 0.4979 of experienced
doctors. In addition, we show that BPSegSys can help doctors identify brachial
plexus trunks more accurately, with IoU improvement up to 27%, which has
significant clinical application value.Comment: 9 page
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