AIMC Topic: Explosive Agents
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In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfO-Based Neuristor Array.
ACS sensors
Sep 14, 2023
With the evolution of artificial intelligence, the explosive growth of data from sensory terminals gives rise to severe energy-efficiency bottleneck issues due to cumbersome data interactions among sensory, memory, and computing modules. Heterogeneou...
Facing the phase problem.
IUCrJ
Sep 1, 2023
The marvel of X-ray crystallography is the beauty and precision of the atomic structures deduced from diffraction patterns. Since these patterns record only amplitudes, phases for the diffracted waves must also be evaluated for systematic structure d...
Performance of the SABAT Neutron-Based Explosives Detector Integrated with an Unmanned Ground Vehicle: A Simulation Study.
Sensors (Basel, Switzerland)
Dec 19, 2022
The effective and safe detection of illicit materials, explosives in particular, is currently of growing importance taking into account the geopolitical situation and increasing risk of a terrorist attack. The commonly used methods of detection are b...
Deep learning approach to overcome signal fluctuations in SERS for efficient On-Site trace explosives detection.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Dec 9, 2022
Surface-enhanced Raman spectroscopy (SERS) is an improved Raman spectroscopy technique to identify the analyte under study uniquely. At the laboratory scale, SERS has realised a huge potential to detect trace analytes with promising applications acro...
Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime.
Journal of chemical information and modeling
Aug 23, 2022
Motivated by the challenging of deep learning on the low data regime and the urgent demand for intelligent design on highly energetic materials, we explore a correlated deep learning framework, which consists of three recurrent neural networks (RNNs)...
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