Wireless Networking

The Army Cyber Institute (ACI) Internet of Things (IoT) Network Traffic Dataset 2023 (ACI-IoT-2023) is a novel dataset tailored for machine learning (ML) applications in the realm of IoT network security. This effort focuses on delivering a distinctive and realistic dataset designed to train and evaluate ML models for IoT network environments. By addressing a gap in existing resources, this dataset aims to propel advancements in ML-based solutions, ultimately fortifying the security of IoT operations.

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Abstract—This paper proposes an enhanced scheme for a traditional phase-locked loop (PLL) frequency synthesizer based on the integration of self-injected locking and in-loop mixing (SIL-MIX) techniques. This approach significantly reduces the overall phase noise of the conventional PLL frequency synthesizer. Additionally, the integration of a programmable phase shifter on the system provides a novel perspective for the on-board implementation of frequency synthesizers.

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This Dataset provides input data for the development of the B-RAN and attacks models for the NANCY framework,to model training and model inference functions. The data collected plays the role of ML algorithm-specific data preparation. The dataset contains time-series, collected transmitting a video content through the Italtel "VTU - video streaming and transcoding application", that can convert audio and video streams from one format to another, at multiple encodings schemes, changing resolution, bitrate, and video parameters.

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The dataset Provides S-parameter measurements of an AI enhanced wireless power transfer system using two ISO/ICE 14443-1 Coils with series capacitance compensation at 13.56 MHz under three configurations of vertical and horizontal misalignment, inter-coil distance, and azimuthal tilt. The structure and components of the system is shown in the Image attached to the Dataset This measured data validates the implementation of the system at the three coil configurations discussed in the publication.

 

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The paper proposes a systematic approach for the  design of a conformal U-shaped series-feed microstrip antenna array (US-SFMA) using true time delay phase shifters. The presented study describes the design procedure of conformal SFMA without the use of additional element to tune its spectral and radiation performance.

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Recent advances in Wi-Fi sensing have ushered in a plethora of pervasive applications in home surveillance, remote healthcare, road safety, and home entertainment, among others. Most of the existing works are limited to the activity classification of a single human subject at a given time. Conversely, a more realistic scenario is to achieve simultaneous, multi-subject activity classification. The first key challenge in that context is that the number of classes grows exponentially with the number of subjects and activities.
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Vehicle-to-Everything (V2X) potential to support Intelligent Transportation System (ITS) is challenged by its inherent high mobility, changing topology and consequently link instability. The quest to minimize the effect of changing topology has led centroid-based clustering algorithms to exploit Cluster Head (CH) longevity approaches to improve stability while compromising on throughput performance. Most K-means based schemes particularly reselect cluster seeds at every reclustering phase.

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Human-robot interaction remains a significant challenge for mobile robots performing as social robots, which are expected to coexist, collaborate, and be cognitive with persons to enhance efficiency and reduce labor strength. Typically, escort robots can be widely employed for accompanying elderly and youth outdoors, guiding visitors in exhibition halls and museums, and sorting goods in warehouses.

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The enhanced dataset is a sophisticated collection of simulated data points, meticulously designed to emulate real-world data as collected from wearable Internet of Things (IoT) devices. This dataset is tailored for applications in safety monitoring, particularly for women, and is ideal for developing machine learning models for distress or danger detection.

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2144 Views

In this paper, we investigate the feasibility of THz

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