Digital Twin(DT) is at the forefront of the Industry 4.0 revolution facilitated through advanced data analytics and the Internet of Things (IoT) connectivity. With this research, we intend to explore the applications of digital twins in the consumer electronics(CE) industry. This study delves into different realms within the consumer electronics sector to explore how digital twin technology could be beneficial while considering the potential limitations. Some CE fields that could benefit from digital twins include product design and development, predictive maintenance, personalization, virtual user manuals and training, product performance optimization, remote support and troubleshooting, supply chain optimization, and sustainability. This research also focuses on various aspects of the crucial role played by consumer electronics in implementing digital twins in the industry, including hardware integration, data collection methods and establishing reliable connectivity, software development, standards and interoperability, user experience and interaction design, data security and privacy, and feedback-driven improvements. It also discusses challenges like data accuracy, complexity, cost, limited scope of representation, data privacy and security concerns, user engagement and adoption, and integration and compatibility issues faced while implementing this technology.

In today's digital landscape, video streaming holds an important role in internet traffic, driven by the pervasive use of mobile devices and the surge in streaming platform popularity. In light of this, the imperative to gauge energy consumption takes center stage, paving the way for eco-conscious and sustainable video streaming solutions with a minimal Carbon Dioxide (CO2) footprint. This paper meticulously examines the energy consumption and CO2 emissions of five popular open-source and fast video encoders: x264, x265, VVenC, libvpx-vp9, and SVT-AV1. These encoders are optimized software implementations of three video coding standards (AVC/H.264, HEVC/H.265, VVC/H.266) and two video formats (VP9andAV1).Toensureafaircomparison,wealsoassesscodingefficiencyacross these encoders at four distinct presets, applying three objective quality metrics. Additional factors like computing density and memory usage are considered. Our experiments employ the JVET-CTC video dataset, encompassing video sequences of diverse content and resolutions. Encoding is executed on an Intel x86 multi-core processor, while CO2 emissions are computed based on the energy mix data from a server situated in France, reflecting an average emission rate of 101 g/kWh. Our findings underscore that the x264 and SVT-AV1 encoders, especially at fast and faster presets, exhibit the lowest energy consumption and CO2 emissions. Notably, x264 boasts the most energy-efficient performance, yielding CO2 emissions of 0.28 g, 0.91 g, 2.07 g, and 9.74 g when encoding videos using faster, fast, medium, and slower presets, respectively. Furthermore, SVT-AV1 and VVenC encoders operating at a slower preset demonstrate superior coding efficiency, albeit at the cost of higher computational complexity and CO2 emissions of 60.5 g and 406 g, respectively. A salient observation from our study is that resolution and encoder presets serve as crucial parameters for curbing energy consumption and CO2 emissions, albeit with an inherent trade-off in video quality. Comprehensive results from this research are publicly accessible <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://chachoutaieb.github.io/encoding</uri> energy co2.

Digital manufacturing (DM) is actively adopted to the production lifecycles of a variety of critical industries, and this rapid growth has resulted in exponential increase of 3D computer-aided design (CAD) models. Unfortunately, counterfeiting of intellectual property becomes a prominent threat as many 3D designs are accessible online, combined with the proliferation of cheap consumer 3D printers that enable malicious actors to produce non-authentic parts. State-of-the-art techniques to secure manufacturing processes mostly rely on watermarking, which embeds hidden information inside CAD models to prove ownership and authenticity. Nevertheless, such techniques tamper with the model itself, while existing attacks allow removing such watermarks altogether. To address these shortcomings, we integrate signal processing and cryptographic techniques and describe a tailored solution for CAD model ownership and supply chain management. Our approach generates unique identifiers for 3D designs using frequency-domain transforms and employs non-fungible tokens (NFTs) that persist on public distributed ledgers. Our NFTs are implemented on the Ethereum blockchain using smart contracts and their functionality is twofold: (a) authenticate the owner of a CAD model, and (b) enable ownership transfer. To validate our technique, we deployed our smart contract on Ethereum's proof-of-work Ropsten network and demonstrated the applicability of our methodology.