Optimizing event-based neural networks on digital neuromorphic architecture: A comprehensive design space exploration

Neuromorphic processors promise low-latency and energy-efficient processing by adopting novel brain-inspired design methodologies. Yet, current neuromorphic solutions still struggle to rival conventional deep learning accelerators' performance and area efficiency in practical applications. Event-driven data-flow processing and near/in-memory...

Fusion Framework for Coaxial Catheter Tracking

Minimally invasive catheter-based interventions normally take place under the guidance of fluoroscopy. However, fluoroscopy is harmful to both patients and clinicians. Moreover, it only offers 2-D shape visualization of flexible devices. To solve the problem of harmful radiation and offer 3-D pose and shape information, recent studies propose...

Sensor Fusion for Shape Reconstruction Using Electromagnetic Tracking Sensors and Multi-Core Optical Fiber

Optical fiber-based shape sensing is gaining popularity in cardiac catheterization lately. Typically, these procedures are taking place under the guidance of fluoroscopy. However, fluoroscopy has several disadvantages. Thanks to fiber optic shape sensing and Electromagnetic Tracking (EMT), the 3D catheter shape can now be tracked in real-time...

GEM: Glare or Gloom, I Can Still See You - End-to-End Multi-Modal Object Detection

Deep neural networks designed for vision tasks are often prone to failure when they encounter environmental conditions not covered by the training data. Single-modal strategies are insufficient when the sensor fails to acquire information due to malfunction or its design limitations. Multi-sensor configurations are known to provide redundancy...

CNN Based Road User Detection Using the 3D Radar Cube

This letter presents a novel radar based, single-frame, multi-class detection method for moving road users ( pedestrian, cyclist, car ), which utilizes low-level radar cube data. The method provides class information both on the radar target- and object-level. Radar targets are classified individually after extending the target features with a...

Drift-Free Inertial Sensor-Based Joint Kinematics for Long-Term Arbitrary Movements

The ability to capture joint kinematics in outside-laboratory environments is clinically relevant. In order to estimate kinematics, inertial measurement units can be attached to body segments and their absolute orientations can be estimated. However, the heading part of such orientation estimates is known to drift over time, resulting in...

Sequential Human Gait Classification with Distributed Radar Sensor Fusion

This paper presents different information fusion approaches to classify human gait patterns and falls in a radar sensors network. The human gaits classified in this work are both individual and sequential, continuous gait collected by a FMCW radar and three UWB pulse radar placed at different spatial locations. Sequential gaits are those...

Unsupervised Tuning of Filter Parameters Without Ground-Truth Applied to Aerial Robots

Autonomous robots heavily rely on well-tuned state estimation filters for successful control. This letter presents a novel automatic tuning strategy for learning filter parameters by minimizing the innovation, i.e., the discrepancy between expected and received signals from all sensors. The optimization process only requires the inputs and...

Color-Based Proprioception of Soft Actuators Interacting with Objects

Actuators using soft materials feature a large number of degrees of freedom. This tremendous flexibility allows a soft actuator to passively adapt its shape to the objects under interaction. In this paper, we propose a novel proprioception method for soft actuators during real-time interaction with previously unknown objects. First, we design...

Integrating state representation learning into deep reinforcement learning

Most deep reinforcement learning techniques are unsuitable for robotics, as they require too much interaction time to learn useful, general control policies. This problem can be largely attributed to the fact that a state representation needs to be learned as a part of learning control policies, which can only be done through fitting expected...