Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction. (arXiv:1911.08483v1 [eess.IV])

Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images (mpMRI)is of great clinical importance, which defines tumour size, shape andappearance and provides abundant information for preoperative diag-nosis, treatment planning and survival prediction. Recent developmentson deep learning have significantly improved the performance of auto-mated…

Modern Antennas and Microwave Circuits — A complete master-level course. (arXiv:1911.08484v1 [eess.SP])

Modern antenna systems include passive antenna structures, passive microwave circuits and interconnections to electronics. Therefore, the antenna engineer should have a deep understanding of antenna theory and should be able to apply microwave engineering concepts. This textbook provides all relevant material for Master-level courses in the domain of antenna systems. The book includes comprehensive material…

Action Recognition Using Volumetric Motion Representations. (arXiv:1911.08511v1 [cs.CV])

Traditional action recognition models are constructed around the paradigm of 2D perspective imagery. Though sophisticated time-series models have pushed the field forward, much of the information is still not exploited by confining the domain to 2D. In this work, we introduce a novel representation of motion as a voxelized 3D vector field and demonstrate how…

Supported-BinaryNet: Bitcell Array-based Weight Supports for Dynamic Accuracy-Latency Trade-offs in SRAM-based Binarized Neural Network. (arXiv:1911.08518v1 [cs.NE])

In this work, we introduce bitcell array-based support parameters to improve the prediction accuracy of SRAM-based binarized neural network (SRAM-BNN). Our approach enhances the training weight space of SRAM-BNN while requiring minimal overheads to a typical design. More flexibility of the weight space leads to higher prediction accuracy in our design. We adapt row digital-to-analog…

Deep Motion Blur Removal Using Noisy/Blurry Image Pairs. (arXiv:1911.08541v1 [cs.CV])

Removing spatially variant motion blur from a blurry image is a challenging problem as blur sources are complicated and difficult to model accurately. Recent progress in deep neural networks suggests that kernel free single image deblurring can be efficiently performed, but questions about deblurring performance persist. Thus, we propose to restore a sharp image by…

Six Degree-of-Freedom Hovering using LIDAR Altimetry via Reinforcement Meta-Learning. (arXiv:1911.08553v1 [eess.SY])

We optimize a six degrees of freedom hovering policy using reinforcement meta-learning. The policy maps flash LIDAR measurements directly to on/off spacecraft body-frame thrust commands, allowing hovering at a fixed position and attitude in the asteroid body-fixed reference frame. Importantly, the policy does not require position and velocity estimates, and can operate in environments with…

Robust Sub-Meter Level Indoor Localization With a Single WiFi Access Point-Regression Versus Classification. (arXiv:1911.08563v1 [eess.SP])

Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level…

CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUs. (arXiv:1911.08606v1 [cs.CV])

Fixed-point quantization and binarization are two reduction methods adopted to deploy Convolutional Neural Networks (CNN) on end-nodes powered by low-power micro-controller units (MCUs). While most of the existing works use them as stand-alone optimizations, this work aims at demonstrating there is margin for a joint cooperation that leads to inferential engines with lower latency and…

Robust Adaptive Model Predictive Control with Worst-Case Cost. (arXiv:1911.08607v1 [eess.SY])

A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. The system is characterized by an impulse response model, which is assumed to lie within a bounded set called the feasible system set. Online set-membership identification is used to reduce uncertainty…

Seq2Seq RNN based Gait Anomaly Detection from Smartphone Acquired Multimodal Motion Data. (arXiv:1911.08608v1 [eess.SP])

Smartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications. Wearables are suitable for indoor and outdoor scenarios, can be placed on many parts of the human body and can integrate a large number of sensors capable of gathering physiological and behavioral biometric…