Gaze Estimation on Spresense

Gaze estimation is a valuable technology with numerous applications in fields such as human-computer interaction, virtual reality, and medicine. This report presents the implementation of a gaze estimation system using the Sony Spresense microcontroller board and explores its performance in latency, MAC/cycle, and power consumption. The report also provides insights into the system's architecture, including the gaze estimation model used. Additionally, a demonstration of the system is presented, showcasing its functionality and performance. Our lightweight model TinyTrackerS is a mere 169Kb in size, using 85.8k parameters and runs on the Spresense platform at 3 FPS

Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO

Smart glasses are rapidly gaining advanced functionality thanks to cutting-edge computing technologies, accelerated hardware architectures, and tiny AI algorithms. Integrating AI into smart glasses featuring a small form factor and limited battery capacity is still challenging when targeting full-day usage for a satisfactory user experience. This paper illustrates the design and implementation of tiny machine-learning algorithms exploiting novel low-power processors to enable prolonged continuous operation in smart glasses. We explore the energy- and latency-efficient of smart glasses in the case of real-time object detection. To this goal, we designed a smart glasses prototype as a research platform featuring two microcontrollers, including a novel milliwatt-power RISC-V parallel processor with a hardware accelerator for visual AI, and a Bluetooth low-power module for communication. The smart glasses integrate power cycling mechanisms, including image and audio sensing interfaces. Furthermore, we developed a family of novel tiny deep-learning models based on YOLO with sub-million parameters customized for microcontroller-based inference dubbed TinyissimoYOLO v1.3, v5, and v8, aiming at benchmarking object detection with smart glasses for energy and latency. Evaluations on the prototype of the smart glasses demonstrate TinyissimoYOLO's 17ms inference latency and 1.59mJ energy consumption per inference while ensuring acceptable detection accuracy. Further evaluation reveals an end-to-end latency from image capturing to the algorithm's prediction of 56ms or equivalently 18 fps, with a total power consumption of 62.9mW, equivalent to a 9.3 hours of continuous run time on a 154mAh battery. These results outperform MCUNet (TinyNAS+TinyEngine), which runs a simpler task (image classification) at just 7.3 fps per second

La tavolozza dei pigmenti nelle pitture dell'Insula del Centenario

Vengono descritti i più importanti ritrovamenti di pigmenti dell'Insula del Centenario, le loro alterazioni, l'effetto del fuoco, le tecniche impiegate nella pittura murale. Si rilevano alterazioni cromatiche e la formazione di nuovi composti. Vengono discusse le conoscenze cromatiche degli antichi artigiani e la presenza di tracce di sostanze organich

identificazione delle sostanze organiche nelle pitture murali dell'Insula del Centenario

sono stati analizzati con varie tecniche analitiche e spettroscopiche i campioni prelevati sulle pareti dell?Insula del Centenario per identificare le tracce di leganti differenti dalla calcite dell'affresco. I risultati mostrano solo tracce di leganti

Materiali pittorici e pigmenti

Le pitture di Pompei e le indagini scientifiche sui materiali e le tecniche, con i rinvenimenti pi\uf9 comuni e pi\uf9 insolit

Analytical characterization of Roman plasters of the 'Domus Farini' in Modena

The paper refers to the analytical characterization of Roman painted plasters dating back to the second century AD. The following techniques were used: optical microscopy (OM), scanning electron microscopy (SEM-EDS), micro-Raman and Fourier transform infrared spectroscopies (mu-Raman and FT-IR), X-ray diffraction (XRPD), colorimetry and thermal analyses (TG/DTA). The investigation analysed the chemical composition and structure of the plasters, the chemical composition of the pigment layers, the use of binders and any chemical alteration of pigments as well as deterioration of the samples. Stratigraphic analysis of plasters allowed identification of their individual components, which proved helpful in finding out more about the mural painting technique employed