Search results for: 'time services databas'

Real-time location solutions (RTLS) provide accurate information on the location of materials, goods, assets, persons, or vehicles. The system includes tags, readers, and a software solution. This study covers different RTLS technologies that assets send radio signals to, such as wireless fidelity (Wi-Fi), RFID, ultra-wideband (UWB), Bluetooth Low ...

This study analyzes the global self-service kiosks market. Increased demand for automation and customer service/satisfaction is driving the growth of self-service kiosks globally. The market will witness key growth opportunities in the retail, transportation & logistics, and hospitality verticals. North America and EMEA will be the leading contribu...

Multimodal sensor fusion is an advanced perception technology that combines data from multiple sensors, including cameras, LiDAR, radar, ultrasonic sensors, thermal cameras, and inertial measurement units (IMUs), to give robots and autonomous systems a more accurate, reliable, and comprehensive understanding of their environment. It overcomes the l...

The era of robotics is becoming a disruptive force in the food and beverage (F&B) industry, most notably in production, services, and delivery. By leveraging advanced AI, sensing technologies, and adaptive automation, robotic technologies are delivering groundbreaking advancements in operational precision, performance, and reliability. This transfo...

The October 2025 edition of the CST Technology Opportunity Engine highlights transformative innovations shaping the future of edge computing. The study explores the evolution of decentralized data processing models that enable ultra-low latency, localized intelligence, and real-time decision-making across industries.Driven by the convergence of IoT...

Future of Retail 4.0 marks a pivotal shift in the retail landscape, driven by intelligent systems, automation, and ethical consumerism. Businesses are rapidly adopting models like Retail as a Service and Real-Time AI Pricing to enhance operational efficiency and dynamic market responsiveness. IoT-powered smart store infrastructure and AI-driven los...

This study focuses on heterogeneous computing architectures for automotive and robotics applications, examining how centralized and zonal compute architectures, heterogeneous SoCs integrating CPUs, GPUs, NPUs, DSPs, FPGAs, and dedicated safety islands, as well as robotics edge-AI controllers and mixed-criticality computing platforms, are enabling t...

This study examines the emerging landscape of neuromorphic computing and its role in enabling low-power, real-time, brain-inspired intelligence across edge and autonomous systems. It reviews advancements in spiking neural networks, memristor-based computing in memory, event-driven vision sensors, and hybrid neuromorphic and conventional architectur...

The escalating compute and power demands of edge AI are exposing the limits of conventional von Neumann architectures. Manufacturers deploying always-on sensing, real-time inference, and autonomous decision-making at the edge are constrained by the energy, latency, and memory-bandwidth ceilings of standard NPUs and MCUs. Traditionally, edge AI work...