cnn architecture Search Results


cnn architecture hgnet  (Baidu Inc)
90
Baidu Inc cnn architecture hgnet
Cnn Architecture Hgnet, supplied by Baidu Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/cnn architecture hgnet/product/Baidu Inc
Average 90 stars, based on 1 article reviews
cnn architecture hgnet - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
a convolutional neural network (cnn) with a 3d u-net architecture  (Impath Inc)
90
Impath Inc a convolutional neural network (cnn) with a 3d u-net architecture
A Convolutional Neural Network (Cnn) With A 3d U Net Architecture, supplied by Impath Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/a convolutional neural network (cnn) with a 3d u-net architecture/product/Impath Inc
Average 90 stars, based on 1 article reviews
a convolutional neural network (cnn) with a 3d u-net architecture - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
3d cnn with a u-net like encoder–decoder architecture  (Jung Diagnostics GmbH)
90
Jung Diagnostics GmbH 3d cnn with a u-net like encoder–decoder architecture
3d Cnn With A U Net Like Encoder–Decoder Architecture, supplied by Jung Diagnostics GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/3d cnn with a u-net like encoder–decoder architecture/product/Jung Diagnostics GmbH
Average 90 stars, based on 1 article reviews
3d cnn with a u-net like encoder–decoder architecture - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
1d cnn architecture  (Altera Corp)
90
Altera Corp 1d cnn architecture
1d Cnn Architecture, supplied by Altera Corp, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/1d cnn architecture/product/Altera Corp
Average 90 stars, based on 1 article reviews
1d cnn architecture - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
optimized hardware streaming architecture for the cnn  (Xilinx Inc)
90
Xilinx Inc optimized hardware streaming architecture for the cnn
The architecture of <t>Convolutional</t> <t>Neural</t> <t>Network</t> <t>(CNN)-based</t> hand pose estimation algorithm. The CNN takes a 128 × 128 input preprocessed image. It consists of 3 convolution layers each followed by an ReLU activation and max pooling. Afterwards, there are 2 fully connected layers with ReLU activation. A third fully connected layer (joint regression layer) regresses 3D joint positions. Conv stands for a convolution layer. Each conv is followed by a ReLU activation. FC denotes a fully connected layer.
Optimized Hardware Streaming Architecture For The Cnn, supplied by Xilinx Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/optimized hardware streaming architecture for the cnn/product/Xilinx Inc
Average 90 stars, based on 1 article reviews
optimized hardware streaming architecture for the cnn - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
cnn (self-developed architecture)  (Optovue)
90
Optovue cnn (self-developed architecture)
The architecture of <t>Convolutional</t> <t>Neural</t> <t>Network</t> <t>(CNN)-based</t> hand pose estimation algorithm. The CNN takes a 128 × 128 input preprocessed image. It consists of 3 convolution layers each followed by an ReLU activation and max pooling. Afterwards, there are 2 fully connected layers with ReLU activation. A third fully connected layer (joint regression layer) regresses 3D joint positions. Conv stands for a convolution layer. Each conv is followed by a ReLU activation. FC denotes a fully connected layer.
Cnn (Self Developed Architecture), supplied by Optovue, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/cnn (self-developed architecture)/product/Optovue
Average 90 stars, based on 1 article reviews
cnn (self-developed architecture) - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
multi-resolution cnn architecture  (Panoptes Pharma GmbH)
90
Panoptes Pharma GmbH multi-resolution cnn architecture
The architecture of <t>Convolutional</t> <t>Neural</t> <t>Network</t> <t>(CNN)-based</t> hand pose estimation algorithm. The CNN takes a 128 × 128 input preprocessed image. It consists of 3 convolution layers each followed by an ReLU activation and max pooling. Afterwards, there are 2 fully connected layers with ReLU activation. A third fully connected layer (joint regression layer) regresses 3D joint positions. Conv stands for a convolution layer. Each conv is followed by a ReLU activation. FC denotes a fully connected layer.
Multi Resolution Cnn Architecture, supplied by Panoptes Pharma GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/multi-resolution cnn architecture/product/Panoptes Pharma GmbH
Average 90 stars, based on 1 article reviews
multi-resolution cnn architecture - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
1d cnn model architecture  (BioSignal Group)
90
BioSignal Group 1d cnn model architecture
The <t>1D</t> <t>CNN</t> architecture for the pretext task.
1d Cnn Model Architecture, supplied by BioSignal Group, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/1d cnn model architecture/product/BioSignal Group
Average 90 stars, based on 1 article reviews
1d cnn model architecture - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
custom cnn architectures  (Photonics Inc)
90
Photonics Inc custom cnn architectures
The <t>1D</t> <t>CNN</t> architecture for the pretext task.
Custom Cnn Architectures, supplied by Photonics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/custom cnn architectures/product/Photonics Inc
Average 90 stars, based on 1 article reviews
custom cnn architectures - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
lstm-cnn architecture  (IEEE Access)
90
IEEE Access lstm-cnn architecture
The <t>1D</t> <t>CNN</t> architecture for the pretext task.
Lstm Cnn Architecture, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/lstm-cnn architecture/product/IEEE Access
Average 90 stars, based on 1 article reviews
lstm-cnn architecture - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
lstm-cnn architecture for human activity recognition  (IEEE Access)
90
IEEE Access lstm-cnn architecture for human activity recognition
The <t>1D</t> <t>CNN</t> architecture for the pretext task.
Lstm Cnn Architecture For Human Activity Recognition, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/lstm-cnn architecture for human activity recognition/product/IEEE Access
Average 90 stars, based on 1 article reviews
lstm-cnn architecture for human activity recognition - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier
cnn-based architecture remora  (Oxford Nanopore)
90
Oxford Nanopore cnn-based architecture remora
The <t>1D</t> <t>CNN</t> architecture for the pretext task.
Cnn Based Architecture Remora, supplied by Oxford Nanopore, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/cnn-based architecture remora/product/Oxford Nanopore
Average 90 stars, based on 1 article reviews
cnn-based architecture remora - by Bioz Stars, 2026-03
90/100 stars
  Buy from Supplier

Image Search Results


The architecture of Convolutional Neural Network (CNN)-based hand pose estimation algorithm. The CNN takes a 128 × 128 input preprocessed image. It consists of 3 convolution layers each followed by an ReLU activation and max pooling. Afterwards, there are 2 fully connected layers with ReLU activation. A third fully connected layer (joint regression layer) regresses 3D joint positions. Conv stands for a convolution layer. Each conv is followed by a ReLU activation. FC denotes a fully connected layer.

Journal: Sensors (Basel, Switzerland)

Article Title: Real-Time Energy Efficient Hand Pose Estimation: A Case Study

doi: 10.3390/s20102828

Figure Lengend Snippet: The architecture of Convolutional Neural Network (CNN)-based hand pose estimation algorithm. The CNN takes a 128 × 128 input preprocessed image. It consists of 3 convolution layers each followed by an ReLU activation and max pooling. Afterwards, there are 2 fully connected layers with ReLU activation. A third fully connected layer (joint regression layer) regresses 3D joint positions. Conv stands for a convolution layer. Each conv is followed by a ReLU activation. FC denotes a fully connected layer.

Article Snippet: Afterwards, we provided an optimized hardware streaming architecture for the CNN which was then implemented on Xilinx UltraScale+ MPSoC FPGA.

Techniques: Activation Assay

Design process overview; the first box illustrates the software-level design phase, while the other boxes illustrate the hardware related design phase. The first stage is quantization-aware training (QAT) in which we decrease the CNN memory demand as well as the computation time on the hardware. The second stage is hardware streaming architecture (HSA) where the underlying hardware structure is designed for the CNN. In system integration (SI) stage, the programmable logic PL and the processing system PS are brought together and the interface with the memory is configured through the hardware system integration sub-stage. Furthermore, the on-Chip Software is developed for preprocessing and interfacing with the peripherals.

Journal: Sensors (Basel, Switzerland)

Article Title: Real-Time Energy Efficient Hand Pose Estimation: A Case Study

doi: 10.3390/s20102828

Figure Lengend Snippet: Design process overview; the first box illustrates the software-level design phase, while the other boxes illustrate the hardware related design phase. The first stage is quantization-aware training (QAT) in which we decrease the CNN memory demand as well as the computation time on the hardware. The second stage is hardware streaming architecture (HSA) where the underlying hardware structure is designed for the CNN. In system integration (SI) stage, the programmable logic PL and the processing system PS are brought together and the interface with the memory is configured through the hardware system integration sub-stage. Furthermore, the on-Chip Software is developed for preprocessing and interfacing with the peripherals.

Article Snippet: Afterwards, we provided an optimized hardware streaming architecture for the CNN which was then implemented on Xilinx UltraScale+ MPSoC FPGA.

Techniques: Software

Streaming Architecture. Each CNN layer is mapped into a hardware block, and the hardware blocks are connected to each others via stream channels. The bitwidth of each stream is shown on this figure.

Journal: Sensors (Basel, Switzerland)

Article Title: Real-Time Energy Efficient Hand Pose Estimation: A Case Study

doi: 10.3390/s20102828

Figure Lengend Snippet: Streaming Architecture. Each CNN layer is mapped into a hardware block, and the hardware blocks are connected to each others via stream channels. The bitwidth of each stream is shown on this figure.

Article Snippet: Afterwards, we provided an optimized hardware streaming architecture for the CNN which was then implemented on Xilinx UltraScale+ MPSoC FPGA.

Techniques: Blocking Assay

Hardware system integration; AXI-Lite interface provides the interconnection between the PS and the PL. DMA module is integrated in the PL. This module is responsible for converting the memory mapped input to AXI stream CNN input, as well as converting the AXI stream CNN output to a memory mapped output.

Journal: Sensors (Basel, Switzerland)

Article Title: Real-Time Energy Efficient Hand Pose Estimation: A Case Study

doi: 10.3390/s20102828

Figure Lengend Snippet: Hardware system integration; AXI-Lite interface provides the interconnection between the PS and the PL. DMA module is integrated in the PL. This module is responsible for converting the memory mapped input to AXI stream CNN input, as well as converting the AXI stream CNN output to a memory mapped output.

Article Snippet: Afterwards, we provided an optimized hardware streaming architecture for the CNN which was then implemented on Xilinx UltraScale+ MPSoC FPGA.

Techniques:

The 1D CNN architecture for the pretext task.

Journal: Applied sciences (Basel, Switzerland)

Article Title: Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training

doi: 10.3390/app132112035

Figure Lengend Snippet: The 1D CNN architecture for the pretext task.

Article Snippet: They demonstrated that the 1D CNN model architecture can be successfully applied towards making predictions from biosignal data without manual feature engineering and feature selection [ ].

Techniques:

The 1D CNN architecture for fine-tuning the pre-trained network towards the downstream stress-prediction task.

Journal: Applied sciences (Basel, Switzerland)

Article Title: Individualized Stress Mobile Sensing Using Self-Supervised Pre-Training

doi: 10.3390/app132112035

Figure Lengend Snippet: The 1D CNN architecture for fine-tuning the pre-trained network towards the downstream stress-prediction task.

Article Snippet: They demonstrated that the 1D CNN model architecture can be successfully applied towards making predictions from biosignal data without manual feature engineering and feature selection [ ].

Techniques: