Results 71 to 80 of about 52,085 (312)

Searching for Exoplanets Using Artificial Intelligence

, 2017
In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify. Here we present a
Griffith, Caitlin A., Palafox, Leon, Pearson, Kyle A.   +2 more
core   +1 more source

Bayesian Topological Convolutional Neural Nets

Convolutional neural networks (CNNs) have been established as the main workhorse in image data processing; nonetheless, they require large amounts of data to train, often produce overconfident predictions, and frequently lack the ability to quantify the uncertainty of their predictions.
Dayton, Sarah Harkins, Everett, Hayden, Schizas, Ioannis, Boothe Jr., David L., Maroulas, Vasileios   +4 more
openaire   +2 more sources

Near‐Sensor Analog Computing via Monolithic 3D Piezoelectric Sensor–FeFET for Tactile Sensing System

Advanced Functional Materials, EarlyView.
A monolithic 3D‐integrated tactile system combines a piezoelectric sensor and ferroelectric field‐effect transistor (FeFET) to process both static and dynamic pressure signals directly at the sensor node. The system enables in‐sensor analog noise filtering with multi‐level memory states, achieving high sensitivity and ultra‐low power operation (≈10 nW),
Woongjin Kim, Seungyeob Kim, Jinwook Ha, Taeseung Jung, Yunjeong Kim, Jinho Ahn, Sanghun Jeon   +6 more
wiley   +1 more source

Real‐time semantic segmentation network for crops and weeds based on multi‐branch structure

IET Computer Vision
Weed recognition is an inevitable problem in smart agriculture, and to realise efficient weed recognition, complex background, insufficient feature information, varying target sizes and overlapping crops and weeds are the main problems to be solved.
Yufan Liu, Muhua Liu, Xuhui Zhao, Junlong Zhu, Lin Wang, Hao Ma, Mingchuan Zhang   +6 more
doaj   +1 more source

Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review

Diagnostics, 2022
Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep ...
Athanasios G. Pantelis, Panagiota A. Panagopoulou, Dimitris P. Lapatsanis   +2 more
doaj   +1 more source

What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification

, 2017
Matching pedestrians across disjoint camera views, known as person re-identification (re-id), is a challenging problem that is of importance to visual recognition and surveillance.
Gao, Junbin, Li, Xue, Wang, Yang, Wu, Lin   +3 more
core   +1 more source

DA$^{\textbf{2}}$-Net : Diverse & Adaptive Attention Convolutional Neural Network

, 2021
Standard Convolutional Neural Network (CNN) designs rarely focus on the importance of explicitly capturing diverse features to enhance the network's performance. Instead, most existing methods follow an indirect approach of increasing or tuning the networks' depth and width, which in many cases significantly increases the computational cost.
Girma, Abenezer, Homaifar, Abdollah, Mahmoud, M Nabil, Yan, Xuyang, Sarkar, Mrinmoy   +4 more
openaire   +2 more sources

Crack‐Growing Interlayer Design for Deep Crack Propagation and Ultrahigh Sensitivity Strain Sensing

Advanced Functional Materials, EarlyView.
A crack‐growing semi‐cured polyimide interlayer enabling deep cracks for ultrahigh sensitivity in low‐strain regimes is presented. The sensor achieves a gauge factor of 100 000 at 2% strain and detects subtle deformations such as nasal breathing, highlighting potential for minimally obstructive biomedical and micromechanical sensing applications ...
Minho Kim, Taewi Kim, Jieun Park, Insic Hong, Myungrae Hong, Seonggyeong Park, Beomjin Kim, Sehyuk Ahn, Siwoo Jang, Seungyong Han, Je‐Sung Koh, Daeshik Kang   +11 more
wiley   +1 more source

Non‐stationary financial time series forecasting based on meta‐learning

Electronics Letters, 2023
In this letter, the authors address the challenge in forecasting non‐stationary financial time series by proposing a meta‐learning based forecasting model equipped with a convolution neural network (CNN) predictor and a long short‐term memory (LSTM) meta‐
Anqi Hong, Minghan Gao, Qiang Gao, Xiao‐Hong Peng   +3 more
doaj   +1 more source

Universal Neuromorphic Element: NbOx Memristor with Co‐Existing Volatile, Non‐Volatile, and Threshold Switching

Advanced Functional Materials, EarlyView.
A W/NbOx/Pt memristor demonstrates the coexistence of volatile, non‐volatile, and threshold switching characteristics. Volatile switching serves as a reservoir computing layer, providing dynamic short‐term processing. Non‐volatile switching, stabilized through ISPVA, improves reliable long‐term readout. Threshold switching operates as a leaky integrate
Ungbin Byun, Hyesung Na, Sungjun Kim
wiley   +1 more source