A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition

Succetti, Federico; Rosato, Antonello; Luzio, Francesco Di; Ceschini, Andrea; Panella, Massimo

doi:10.2528/pier22042605

Cited by 13 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…43 Finally, the outcomes could have ramifications in a broad range of applications, such as cross-wavelength imaging, energy harvesting, and wireless communication. [44][45][46]…”

Section: Discussionmentioning

confidence: 99%

Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning

Qian,

Jia,

Wang

et al. 2024

Adv. Photon.

View full text Add to dashboard Cite

.Being invisible ad libitum has long captivated the popular imagination, particularly in terms of safeguarding modern high-end instruments from potential threats. Decades ago, the advent of metamaterials and transformation optics sparked considerable interest in invisibility cloaks, which have been mainly demonstrated in ground and waveguide modalities. However, an omnidirectional flying cloak has not been achieved, primarily due to the challenges associated with dynamic synthesis of metasurface dispersion. We demonstrate an autonomous aeroamphibious invisibility cloak that incorporates a suite of perception, decision, and execution modules, capable of maintaining invisibility amidst kaleidoscopic backgrounds and neutralizing external stimuli. The physical breakthrough lies in the spatiotemporal modulation imparted on tunable metasurfaces to sculpt the scattering field in both space and frequency domains. To intelligently control the spatiotemporal metasurfaces, we introduce a stochastic-evolution learning that automatically aligns with the optimal solution through maximum probabilistic inference. In a fully self-driving experiment, we implement this concept on an unmanned drone and showcase adaptive invisibility in three canonical landscapes—sea, land, and air—with a similarity rate of up to 95%. Our work extends the family of invisibility cloaks to flying modality and inspires other research on material discoveries and homeostatic meta-devices.

show abstract

“…43 Finally, the outcomes could have ramifications in a broad range of applications, such as cross-wavelength imaging, energy harvesting, and wireless communication. [44][45][46]…”

Section: Discussionmentioning

confidence: 99%

Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning

Qian,

Jia,

Wang

et al. 2024

Adv. Photon.

View full text Add to dashboard Cite

show abstract

“…With the development of neural networks and deep learning and other fields (Gong et al 2021;Succetti et al 2022), a video-based fire detection method is proposed. Compared with traditional methods, it has the advantages of fast response, non-contact, visualization, intelligence, and easy integration.…”

Section: Introductionmentioning

confidence: 99%

Real-time fire detection algorithms running on small embedded devices based on MobileNetV3 and YOLOv4

et al. 2023

View full text Add to dashboard Cite

Aim Fires are a serious threat to people’s lives and property. Detecting fires quickly and effectively and extinguishing them in the nascent stage is an effective way to reduce fire hazards. Currently, deep learning-based fire detection algorithms are usually deployed on the PC side. Methods After migrating to small embedded devices, the accuracy and speed of recognition are degraded due to the lack of computing power. In this paper, we propose a real-time fire detection algorithm based on MobileNetV3-large and yolov4, replacing CSP Darknet53 in yolov4 with MobileNetV3-large to achieve the initial extraction of flame and smoke features while greatly reducing the computational effort of the network structure. A path connecting PANet was explored on Gbneck(104, 104, 24), while SPP was embedded in the path from MobileNetV3 to PANet to improve the feature extraction capability for small targets; the PANet in yolo4 was improved by combining the BiFPN path fusion method, and the improved PANet further improved the feature extraction capability; the Vision Transformer model is added to the backbone feature extraction network and PANet of the YOLOv4 model to give full play to the model’s multi-headed attention mechanism for pre-processing image features; adding ECA Net to the head network of yolo4 improves the overall recognition performance of the network. Result These algorithms run well on PC and reach 95.14% recognition accuracy on the public dataset BoWFire. Finally, these algorithms were migrated to the Jeston Xavier NX platform, and the entire network was quantized and accelerated with the TensorRT algorithm. With the image propagation function of the fire robot, the overall recognition frame rate can reach about 26.13 with high real-time performance while maintaining a high recognition accuracy. Conclusion Several comparative experiments have also validated the effectiveness of this paper’s improvements to the YOLOv4 algorithm and the superiority of these structures. With the effective integration of these components, the algorithm shows high accuracy and real-time performance.

show abstract

“…It was reported that the biopsy sensitivity of CAG was only 42%, and the diagnostic rate of AIG was only 2% in China [7], [8]. The multiple deep learning [9], [10] models developed by Luo et al [11] have an accuracy of 85.4%-91.6% in identifying CAG. Zhao et al [12] developed a real-time video monitoring model for endoscopic CAG diagnosis based on U-Net deep learning, and the accuracy of distinguishing CAG from CNAG was 90.53%.…”

Section: Introductionmentioning

confidence: 99%

A Computer-Assisted Diagnosis System for the Detection of Chronic Gastritis in Endoscopic Images Using A Novel Convolution and Relative Self-Attention Parallel Network

Gong,

Yan,

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Chronic gastritis mainly includes chronic non-atrophic gastritis(CNAG), autoimmune gastritis (AIG), and type B gastritis. Early detection of AIG and type B gastritis will help identify high-risk groups for gastric cancer and prevent the development of irreversible peripheral neuropathy. We aim to develop a computer-assisted diagnosis (CADx) system by presenting a novel Convolution and Relative Self-Attention Parallel Network (CRSAPNet). We collected 3576 endoscopic images of chronic gastritis from 205 patients. MBConv and Relative Self-Attention Parallel Block (CRSAPB) was proposed to concatenate local features (such as mucosal folds and mucosal vessels extracted by MBConv) and global features (such as atrophied area extracted by Relative Self-Attention) in parallel in the last two stages of CRSAPNet. The CADx system distinguished AIG from type B gastritis and CNAG. The CRSAPNet achieved the highest overall accuracy of 95.44% (94.65% precision, 93.51% recall, 94.08% F1-score for AIG) with the fewest parameters. We used Grad-CAM to visually analyze the heat maps. We only replaced the original blocks of the third stage of ResNet50 and ConvNeXt-T with CRSAPB, resulting in an overall accuracy improvement of 0.37%, and 4.19%, respectively. Furthermore, the CADx system classified the three types of chronic gastritis for the first time. The CRSAPNet achieved an overall accuracy of 91.62%, and the overall accuracies in the location of the gastric body and gastric fundus were 93.43% and 92.51%, respectively. A new state-of-the-art deep learning network is introduced to distinguish AIG from type B gastritis and CNAG, and a classification for three types of chronic gastritis is reported for the first time.

show abstract

A Fast Deep Learning Technique for Wi-Fi-Based Human Activity Recognition

Cited by 13 publications

References 33 publications

Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning

Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning

Real-time fire detection algorithms running on small embedded devices based on MobileNetV3 and YOLOv4

A Computer-Assisted Diagnosis System for the Detection of Chronic Gastritis in Endoscopic Images Using A Novel Convolution and Relative Self-Attention Parallel Network

Contact Info

Product

Resources

About