This review article delves into the conceptual framework of digital twins and their diverse applications across research domains, highlighting the pivotal
Digital Signal Processing is not a recent research field, but has become a powerful technology to solve engineering problems in the last few decades due to the introduction by Texas Instruments in 1982 of the Digital Signal Processor. Fast digital signal
The deep spectral features were represented using a sparse-auto-encoder (SAE) and CNN was applied to extract image features. After fusing the extracted features, machine learning algorithms such as k-nearest neighbors (KNN) and SVM are used for classification. The results showed that the SAE-...
Predicting drug-target interactions (DTI) is a complex task. With the introduction of artificial intelligence (AI) methods such as machine learning and deep learning, AI-based DTI prediction can significantly enhance speed, reduce costs, and screen poten
A novel 4D dual-memristor chaotic system (4D-DMCS) is constructed by concurrently introducing two types of memristors: an ideal quadratic smooth memristor and a memristor with an absolute term, into a newly designed jerk chaotic system. The excellent nonlinear properties of the system are investigat...
To bridge this gap, we propose a new method, a similarity-assisted variational autoencoder (saVAE), which adopts similarity information in the framework of the VAE. We pursue this by adding pull-push regularization to the evident lower bound of the likelihood function. Our method produces a pow...
In Depth 4.7.3 Device List 4.7.4 Hardware Platforms and Software Examples 4.7.5 Documentation 5Interface Key Technologies 5.1 Direct Host Control of C2000 Peripherals 5.1.1 Value Proposition 5.1.2 In Depth 5.1.2.1 HIC Bridge for FSI Applications 5.1.2.2 HIC Bridge for Position Encoder ...
The design methodology has been demonstrated using two real application benchmarks, a GSM speech encoder and an MPEG II video encoder. Further, the thesis also examines the problem of compiling an application onto such a dynamically reconfigurable datapath.; The high performance and high flexibility...
[93] was proposed as a hybrid transformer-ConvNet model that utilizes Swin transformers [100] in the encoder and convolutional layers in the decoder. The authors demonstrated that positional embedding can be disregarded, leading to a flatter loss landscape for registration. The following year, Chen...
(Fig.7). Some of the most widely used models include long-short term memory (LSTM) with 7 mentions, autoencoder with 10 mentions, XGBoost with 13 mentions,k-nearest neighbors (KNN) with 14 mentions, artificial neural network (ANN) with 17 mentions, NB with 19 mentions, SVM with 29 ...