In recent years, deep learning has made breakthroughs in medical image segmentation, especially the U-Net architecture, which is becoming a benchmark for various medical image segmentation tasks due to the accuracy of...
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Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backb...
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Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519;and (ii): elliptic curve Diffie-Hellman (ECDH) based mutual authentication technique through a novel lightweight hash function. The proposed scheme attempts to efficiently counter all known perception layer security threats. Moreover, the pre-calculated key generation strategy resulted in cost-effective encryption with 192-bit curve security. It showed comparative efficiency in key strength, and curve strength compared with similar authentication schemes in terms of computational and memory cost, communication performance and encryption robustness.
The advent of the Internet of Things (IoT) has transformed the way devices communicate, with an ever-increasing need for seamless interoperability and energy-efficient communication. This paper presents a unified omni...
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Vehicular Ad-hoc Networks (VANETs) have emerged as a promising technology for enabling efficient communication among vehicles and between vehicles and the infrastructure. The proposed work provides a review of the cur...
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Today, decision support systems are essential in various industries, where vital decisions are made using datasets such as health care systems. So, if the dataset is biased within healthcare systems, then it will hind...
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The Class Imbalance Problem (CIP) is a critical challenge in machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority classes are underrepresented but crucial. This ...
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Brain-machine interface (BMI) holds great promise for restoring the impaired motor functions of individuals. In real-life scenarios, BMI users often face the challenge of quickly learning new tasks to adapt to complex...
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Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,an...
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Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,and *** the course of several decades,extensive research has been dedicated to the generation of homogeneous DBD(H‐DBD),focussing on understanding the transition from H‐DBD to filamentary DBD and exploring strategies to create and sustain H‐*** paper first discusses the in-fluence of various parameters on DBD,including gas flow,dielectric material,surface conductivity,and mesh ***,a chronological literature review is presented,highlighting the development of H‐DBD and the associated understanding of its un-derlying *** encompasses the generation of H‐DBD in helium,nitrogen,and ***,the paper provides a brief overview of multiple‐current‐pulse(MCP)behaviours in H‐*** objective of this article is to provide a chronological un-derstanding of homogeneous dielectric barrier discharge(DBD).This understanding will aid in the design of new experiments aimed at better comprehending the mechanisms behind H‐DBD generation and ultimately assist in achieving large‐volume H‐DBD in an air environment.
Customizing radiation treatments for each patient is a formidable obstacle in the fight against cancer. Because they rely on human intervention and generalization, traditional methods often provide less-than-ideal res...
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Over one billion people worldwide are affected with neurological disorders and their economic impact is approximately $800 billion annually, which constitutes major medical challenge. Using neuromodulation systems cur...
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ISBN:
(纸本)9798331543617
Over one billion people worldwide are affected with neurological disorders and their economic impact is approximately $800 billion annually, which constitutes major medical challenge. Using neuromodulation systems currently available suffers from sensitivity, reaction time as well as energy consumption. The proposal in this research is to address these major issues in closed loop neuromodulation by using a Quantum enhanced Spiking Neural Network (QESNN) architecture. This paper represents the interfacing of two major fields: quantum sensing and neuromorphic computing. The QESNN architecture comprises three core components: This is implemented as an array of quantum sensors, a quantum classical hybrid interface, and a spiking neural network (SNN). Taking advantage of quantum superposition and entanglement principles, the quantum sensor array noninvasively images neural activity at the level of single action potentials using NV centers. These sensors work at ambient temperatures, which is unlike superconducting devices. For processing with neuromorphic processing, quantum-classical hybrid converts quantum sensor data into classical signals with advanced signal process such as quantum state estimation and noise reduction. By modeling biological neurons with leaky integrate and fire neurons, the SNN serves as a low power, timed neural dynamics modulation component that emulates biological event driven behavior. A key innovation in our architecture is adaptive thresholding, which dynamically adjusts detection thresholds based on signal distributions, improving sensitivity and reducing false positives by 45.6%. The system also achieves 20-30% higher power efficiency through techniques like adaptive sensor frequency control and low-power processing. Simulation results that show how the QESNN performs better than classical systems with less false positives and greater energy efficiency are presented. A new platform is demonstrated that integrates quantum sensing with neurom
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