Using Machine Learning to Detect Ghost Images in Automotive Radar

Radar sensors are an important part of driver assistance systems and intelligent vehicles due to their robustness against all kinds of adverse conditions, e.g., fog, snow, rain, or even direct sunlight. This robustness is achieved by a substantially larger wavelength compared to light-based sensors such as cameras or lidars. As a side effect, many surfaces act like mirrors at this wavelength, resulting in unwanted ghost detections. In this article, we present a novel approach to detect these ghost objects by applying data-driven machine learning algorithms. For this purpose, we use a large-scale automotive data set with annotated ghost objects. We show that we can use a state-of-the-art automotive radar classifier in order to detect ghost objects alongside real objects. Furthermore, we are able to reduce the amount of false positive detections caused by ghost images in some settings

Towards Packaging Unit Detection for Automated Palletizing Tasks

For various automated palletizing tasks, the detection of packaging units is a crucial step preceding the actual handling of the packaging units by an industrial robot. We propose an approach to this challenging problem that is fully trained on synthetically generated data and can be robustly applied to arbitrary real world packaging units without further training or setup effort. The proposed approach is able to handle sparse and low quality sensor data, can exploit prior knowledge if available and generalizes well to a wide range of products and application scenarios. To demonstrate the practical use of our approach, we conduct an extensive evaluation on real-world data with a wide range of different retail products. Further, we integrated our approach in a lab demonstrator and a commercial solution will be marketed through an industrial partner

Stochastic resonance effects in quantum channels

We provide some examples of quantum channels where the addition of noise is able to enhance the information transmission rate. This may happen for both quantum and classical uses and realizes stochastic resonance effects.Comment: 4 pages, 3 figure

Peliosis lienalis with atraumatic splenic rupture in a patient with chronic myelomonocytic leukemia: A case report.

INTRODUCTION Atraumatic splenic rupture is a rare but life-threatening condition which may be associated with hematological malignancies. PRESENTATION OF CASE We present the case of a 63-year-old male patient with a history of chronic myelomonocytic leukemia and sarcoidosis under therapy with prednisone, who suffered an atraumatic splenic rupture with hemodynamic instability. He was managed with proximal splenic artery embolization and secondary open splenectomy. On pathology the diagnosis of peliosis lienalis was established. DISCUSSION Peliosis is a rare pathological entity, which presents with multiple blood-filled cavities within parenchymatous organs and is of unknown etiology and pathogenesis. In retrospect a rapid increase in splenomegaly and inhomogeneous parenchyma of the spleen on sonography was realized. CONCLUSION Sonographic changes in size and parenchyma of the spleen in patients with hematological malignancies might help suspecting peliosis lienalis with impending splenic rupture and could alter clinical management towards a prophylactic splenectomy

A class of 2^N x 2^N bound entangled states revealed by non-decomposable maps

We use some general results regarding positive maps to exhibit examples of non-decomposable maps and 2^N x 2^N, N >= 2, bound entangled states, e.g. non distillable bipartite states of N + N qubits.Comment: 19 pages, 1 figur

Quantum Channels with Memory

We present a general model for quantum channels with memory, and show that it is sufficiently general to encompass all causal automata: any quantum process in which outputs up to some time t do not depend on inputs at times t' > t can be decomposed into a concatenated memory channel. We then examine and present different physical setups in which channels with memory may be operated for the transfer of (private) classical and quantum information. These include setups in which either the receiver or a malicious third party have control of the initializing memory. We introduce classical and quantum channel capacities for these settings, and give several examples to show that they may or may not coincide. Entropic upper bounds on the various channel capacities are given. For forgetful quantum channels, in which the effect of the initializing memory dies out as time increases, coding theorems are presented to show that these bounds may be saturated. Forgetful quantum channels are shown to be open and dense in the set of quantum memory channels.Comment: 21 pages with 5 EPS figures. V2: Presentation clarified, references adde

Separability of Mixed States: Necessary and Sufficient Conditions

We provide necessary and sufficient conditions for separability of mixed states. As a result we obtain a simple criterion of separability for $2\times2$ and $2\times3$ systems. Here, the positivity of the partial transposition of a state is necessary and sufficient for its separability. However, it is not the case in general. Some examples of mixtures which demonstrate the utility of the criterion are considered.Comment: Revtex, 13 pages, replaced with minor typos corrected and some examples adde

The Quantum No-Stretching: A geometrical interpretation of the no-cloning theorem

We consider the ideal situation in which a space rotation is transferred from a quantum spin j to a quantum spin l different from j. Quantum-information theoretical considerations lead to the conclusion that such operation is possible only for lj. For l>j the optimal stretching transformation is derived. We show that for qubits the present no-stretching theorem is equivalent to the usual no-cloning theorem

Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather

The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs. While existing methods exploit redundant information in good environmental conditions, they fail in adverse weather where the sensory streams can be asymmetrically distorted. These rare "edge-case" scenarios are not represented in available datasets, and existing fusion architectures are not designed to handle them. To address this challenge we present a novel multimodal dataset acquired in over 10,000km of driving in northern Europe. Although this dataset is the first large multimodal dataset in adverse weather, with 100k labels for lidar, camera, radar, and gated NIR sensors, it does not facilitate training as extreme weather is rare. To this end, we present a deep fusion network for robust fusion without a large corpus of labeled training data covering all asymmetric distortions. Departing from proposal-level fusion, we propose a single-shot model that adaptively fuses features, driven by measurement entropy. We validate the proposed method, trained on clean data, on our extensive validation dataset. Code and data are available here https://github.com/princeton-computational-imaging/SeeingThroughFog