1,828 research outputs found
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
and 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
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