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Topic Review
Self-Attention-Based 3D Object Detection for Autonomous Driving
Autonomous vehicles (AVs) play a crucial role in enhancing urban mobility within the context of a smarter and more connected urban environment. Three-dimensional object detection in AVs is an essential task for comprehending the driving environment to contribute to their safe use in urban environments. 
  • 205
  • 27 Oct 2023
Topic Review
Federated Meta-Learning for Driver Distraction Detection
Driver distraction detection (3D) is essential in improving the efficiency and safety of transportation systems. Federated learning (FL) is emerging as a feasible solution that can train models without private and sensitive information leaving its local repository. Even though various solutions are proposed by using FL to upgrade the model learning paradigm of 3D, considering the requirements for user privacy and the phenomenon of data growth in real-world scenarios, existing methods are insufficient to address four emerging challenges, i.e., data accumulation, communication optimization, data heterogeneity, and device heterogeneity. 
  • 178
  • 27 Oct 2023
Topic Review
Human Operation Augmentation through Wearable Robotic Limb
The supernumerary robotic limb (SRL) is a new type of wearable robot that improves the human body’s ability to move, perceive, and operate through mechanical and human limbs’ integration, mutual assistance, and cooperation. Unlike traditional collaborative robots, SRLs have a closer human–computer interaction mode and a cooperative mode of moving with the human body.
  • 124
  • 27 Oct 2023
Topic Review
Approaches of Automated Heart Disease Prediction
Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind of disease represents the principal concern of many scientists, and techniques belonging to various fields have been developed to attain accurate predictions. 
  • 213
  • 26 Oct 2023
Topic Review
Skeletal Fracture Detection with Deep Learning
Deep learning models have shown great promise in diagnosing skeletal fractures from X-ray images. Deep learning algorithms are always applied in X-rays and CT image processing, such as assessing the mineral bone density (BMD), detecting bone fractures, and recommending treatment 
  • 145
  • 26 Oct 2023
Topic Review
Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions
See and avoid is a basic procedure that pilots must learn and apply during flight. Various technologies have been introduced to avoid midair collisions, but accidents still occur because they are neither mandatory in all airspaces nor suitable for all aircraft.
  • 172
  • 26 Oct 2023
Topic Review
Particle Swarm Optimisation for Emotion Recognition Systems
Particle Swarm Optimisation (PSO) is a popular technique in the field of Swarm Intelligence (SI) that focuses on optimisation. Researchers have explored multiple algorithms and applications of PSO, including exciting new technologies, such as Emotion Recognition Systems (ERS), which enable computers or machines to understand human emotions.
  • 199
  • 26 Oct 2023
Topic Review
Sign-Language Detection
Sign language is the most commonly used form of communication for persons with disabilities who have hearing or speech difficulties. However, persons without hearing impairment cannot understand these signs in many cases. As a consequence, persons with disabilities experience difficulties while expressing their emotions or needs. Thus, a sign character detection and text generation system is necessary to mitigate this issue.
  • 214
  • 25 Oct 2023
Topic Review
Multicast Joining Node Selection Method
Network layer multicast is a powerful method for transmitting data from sources to multiple group members. When joining a multicast group, a group member first sends a request to a designated router (DR). Then, the DR selects a node in the existing multicast tree (known as a multicast joining node, or MJN) to establish a multicast distribution path from the MJN to itself. The MJN selection method runs on the DR and has a significant impact on the distribution of the multicast tree, that directly affects the load distribution in the network. However, the current MJN selection method cannot effectively detect the load status of the downlink multicast path in the case of asymmetric routing, leading to network congestion and limiting the number of multicast groups that the network can accommodate (multicast capacity). 
  • 164
  • 24 Oct 2023
Topic Review
Neural Network for Dense Non-Rigid Structure from Motion
Non-rigid Structure from Motion (NRSFM) is a significant research direction in computer vision that aims to estimate the 3D shape of non-rigid objects from videos.
  • 301
  • 24 Oct 2023
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