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Smart home devices are becoming more popular over the years. A diverse range of appliances is being created, and Ambient Intelligence is growing in homes. However, there are various producers of these gadgets, different kinds of protocols, and diverse environments. The lack of interoperability reduces comfort of the user and turns into a barrier to smart home adoption. Matter is growing by constructing an open-source application layer protocol that can be compatible with all smart home ecosystems. In this article, a Matter overview is provided (namely, of the Commissioning stage), and a Matter Accessory using ESP32-S3 is developed referring to the manufacturer's SDKs and is inserted into an existent household ecosystem. Its behavior on the network is briefly analyzed, and interactions with the device are carried out. The simplicity of these tasks demonstrates accessibility for developers to create products, especially when it comes to firmware. Additionally, device commissioning and control are straightforward for the consumer. This capacity of gadget incorporation into diverse ecosystems using Matter is already on the market and might result in higher device production and enhanced smart home adoption.

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The extraction of geometric information from the environment may be of interest to localisation and mapping algorithms. Existent literature on extracting geometric features from 2D laser data focuses mainly on detecting lines. Regarding corners, most methodologies use the intersection of line segment features. This paper presents a feature extraction algorithm for corner-like points in the 2D laser scan. The proposed methodol-ogy defines arrival and departure neighbourhoods around each scan point and performs local line fitting evaluated in multiple distance-based scales. Then, a set of indicators based on line fitting error, the angle between arrival and departure lines, and consecutive observation of the same keypoint across different scales determine the existence of a corner-like feature. The experiments evaluated the corner-like features regarding their relative position and observability, achieving standard deviations on the relative position lower than the sensor noise and visibility ratios higher than 75% with very low false positives rates.

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In today's fast-paced global marketplace, businesses grapple with the relentless demand for innovation in marketing strategies. The landscape is ever-evolving, driven by digital advancements and shifting consumer behaviors, leaving companies scrambling to stay relevant. Academic scholars, educators, and managers face a formidable challenge: deciphering the complex web of trends and technologies shaping modern marketing while equipping themselves with actionable strategies to thrive in this dynamic environment. Marketing Innovation Strategies and Consumer Behavior is a comprehensive resource that serves as a beacon of clarity amidst the chaos, offering a structured roadmap for understanding and harnessing the power of innovation in marketing. Through meticulous analysis of current trends and in-depth exploration of cutting-edge strategies, this book provides a holistic understanding of the challenges and opportunities facing businesses today. By delving into topics ranging from digital marketing and sustainability initiatives to data analytics and emerging technologies, Marketing Innovation Strategies and Consumer Behavior equips readers with the tools they need to survive and thrive in the modern marketplace. Its emphasis on real-world case studies ensures practical relevance, while its theoretical underpinnings offer a solid foundation for academic inquiry. This book is an indispensable resource for scholars seeking to deepen their understanding of marketing innovation, providing actionable insights and strategic frameworks to navigate the complexities of the digital age with confidence and agility. © 2024 by IGI Global. All rights reserved.