The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed,configured,and managed. Recent advancements in...
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The rapid evolution of wireless technologies and the growing complexity of network infrastructures necessitate a paradigm shift in how communication networks are designed,configured,and managed. Recent advancements in large language models (LLMs) have sparked interest in their potential to revolutionize wireless communication systems. However, existing studies on LLMs for wireless systems are limited to a direct application for telecom language understanding. To empower LLMs with knowledge and expertise in the wireless domain, this paper proposes WirelessLLM, a comprehensive framework for adapting and enhancing LLMs to address the unique challenges and requirements of wireless communication networks. We first identify three foundational principles that underpin WirelessLLM:knowledge alignment, knowledge fusion, and knowledge evolution. Then,we investigate the enabling technologies to build WirelessLLM, including prompt engineering, retrieval augmented generation, tool usage, multi-modal pre-training, and domain-specific fine-tuning. Moreover, we present three case studies to demonstrate the practical applicability and benefits of WirelessLLM for solving typical problems in wireless networks. Finally, we conclude this paper by highlighting key challenges and outlining potential avenues for future research.
Internet of Thing based healthcare ecosystems are extremely popular as they are built on a network of devices that are connected directly to one another in order to collect, share, and use important contextual informa...
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We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed dat...
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The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device **...
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The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.
The most common preventable cause of blindness in working-age adults worldwide is diabetic retinopathy (DR). Accurate detection of DR by machine learning (ML) approaches is generally limited to pre-selected features. ...
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Images Quality in the Transportation industry is considered the most significant term and gains more attention among the higher authorities and researchers. Therefore, the accuracy level of the image has been evaluate...
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Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have m...
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Inpatient falls from beds in hospitals are a common *** falls may result in severe *** problem can be addressed by continuous monitoring of patients using *** advancements in deep learning-based video analytics have made this task of fall detection more effective and *** with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their *** computation-intensive models are required to monitor every action of the patient *** requirement limits the applicability of such ***,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall *** by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their *** whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition *** compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human ***,the activity recognition network classifies the patients’activities based on their *** proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.
A novel cluster-based traffic offloading and user association (UA) algorithm alongside a multi-agent deep reinforcement learning (DRL) based base station (BS) activation mechanism is proposed in this paper. Our design...
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Integration of phase-change materials(PCMs)created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target *** PCMs such as Ge-Sb-Te(GST)and Ge-Sb...
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Integration of phase-change materials(PCMs)created a unique opportunity to implement reconfigurable photonics devices that their performance can be tuned depending on the target *** PCMs such as Ge-Sb-Te(GST)and Ge-Sb-Se-Te(GSST)rely on melt-quench and high temperature annealing processes to change the organization of the molecules in the materials’*** a reorganization leads to different optical,electrical,and thermal properties which can be exploited to implement photonic memory cells that are able to store the data at different resistance or optical transmission *** the great promise of conventional PCMs for realizing reconfigurable photonic memories,their slow and extremely power-hungry thermal mechanisms make scaling the systems based on such devices *** addition,such materials do not offer a stable multi-level response over a long period of *** address these shortcomings,the research carried out in this study shows the proof of concept to implement next-generation photonic memory cells based on two-dimensional(2D)birefringence PCMs such as SnSe,which offer anisotropic optical properties that can be switched *** demonstrate that by leveraging the ultrafast and low-power crystallographic direction change of the material,the optical polarization state of the input optical signal can be *** enables the implementation of next-generation high-speed polarization-encodable photonic memory cells for future photonic computing *** to the conventional PCMs,the proposed SnSe-based photonic memory cells offer an ultrafast switching and low-loss optical response relying on ferroelectric property of SnSe to encode the data on the polarization state of the input optical *** a polarization encoding scheme also reduces memory read-out errors and alleviates the scalability limitations due to the optical insertion loss often seen in optical transmission encoding.
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