696 research outputs found
Functional Nanomaterials and Polymer Nanocomposites: Current Uses and Potential Applications
This book covers a broad range of subjects, from smart nanoparticles and polymer nanocomposite synthesis and the study of their fundamental properties to the fabrication and characterization of devices and emerging technologies with smart nanoparticles and polymer integration
The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Modern meat: the next generation of meat from cells
Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community.
The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World.
The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia
Deep Physics-Guided Unrolling Generalization for Compressed Sensing
By absorbing the merits of both the model- and data-driven methods, deep
physics-engaged learning scheme achieves high-accuracy and interpretable image
reconstruction. It has attracted growing attention and become the mainstream
for inverse imaging tasks. Focusing on the image compressed sensing (CS)
problem, we find the intrinsic defect of this emerging paradigm, widely
implemented by deep algorithm-unrolled networks, in which more plain iterations
involving real physics will bring enormous computation cost and long inference
time, hindering their practical application. A novel deep
hysics-guided unolled recovery earning
() framework is proposed by generalizing the traditional
iterative recovery model from image domain (ID) to the high-dimensional feature
domain (FD). A compact multiscale unrolling architecture is then developed to
enhance the network capacity and keep real-time inference speeds. Taking two
different perspectives of optimization and range-nullspace decomposition,
instead of building an algorithm-specific unrolled network, we provide two
implementations: and . Experiments exhibit
the significant performance and efficiency leading of PRL networks over other
state-of-the-art methods with a large potential for further improvement and
real application to other inverse imaging problems or optimization models.Comment: Accepted by International Journal of Computer Vision (IJCV) 202
Proceedings XXIII Congresso SIAMOC 2023
Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma.
Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica.
Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale.
Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società . Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione
Frequency Domain-based Dataset Distillation
This paper presents FreD, a novel parameterization method for dataset
distillation, which utilizes the frequency domain to distill a small-sized
synthetic dataset from a large-sized original dataset. Unlike conventional
approaches that focus on the spatial domain, FreD employs frequency-based
transforms to optimize the frequency representations of each data instance. By
leveraging the concentration of spatial domain information on specific
frequency components, FreD intelligently selects a subset of frequency
dimensions for optimization, leading to a significant reduction in the required
budget for synthesizing an instance. Through the selection of frequency
dimensions based on the explained variance, FreD demonstrates both theoretical
and empirical evidence of its ability to operate efficiently within a limited
budget, while better preserving the information of the original dataset
compared to conventional parameterization methods. Furthermore, based on the
orthogonal compatibility of FreD with existing methods, we confirm that FreD
consistently improves the performances of existing distillation methods over
the evaluation scenarios with different benchmark datasets. We release the code
at https://github.com/sdh0818/FreD.Comment: Accepted at NeurIPS 202
AI for time-resolved imaging: from fluorescence lifetime to single-pixel time of flight
Time-resolved imaging is a field of optics which measures the arrival time of light on the camera. This thesis looks at two time-resolved imaging modalities: fluorescence lifetime imaging and time-of-flight measurement for depth imaging and ranging. Both of these applications require temporal accuracy on the order of pico- or nanosecond (10−12 − 10−9s) scales.
This demands special camera technology and optics that can sample light-intensity extremely quickly, much faster than an ordinary video camera. However, such detectors can be very expensive compared to regular cameras while offering lower image quality. Further, information of interest is often hidden (encoded) in the raw temporal data. Therefore, computational imaging algorithms are used to enhance, analyse and extract information from time-resolved images.
"A picture is worth a thousand words". This describes a fundamental blessing and curse of image analysis: images contain extreme amounts of data. Consequently, it is very difficult to design algorithms that encompass all the possible pixel permutations and combinations that can encode this information. Fortunately, the rise of AI and machine learning (ML) allow us to instead create algorithms in a data-driven way. This thesis demonstrates the application of ML to time-resolved imaging tasks, ranging from parameter estimation in noisy data and decoding of overlapping information, through super-resolution, to inferring 3D information from 1D (temporal) data
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