13,997 research outputs found
Linux kernel compaction through cold code swapping
There is a growing trend to use general-purpose operating systems like Linux in embedded systems. Previous research focused on using compaction and specialization techniques to adapt a general-purpose OS to the memory-constrained environment, presented by most, embedded systems. However, there is still room for improvement: it has been shown that even after application of the aforementioned techniques more than 50% of the kernel code remains unexecuted under normal system operation. We introduce a new technique that reduces the Linux kernel code memory footprint, through on-demand code loading of infrequently executed code, for systems that support virtual memory. In this paper, we describe our general approach, and we study code placement algorithms to minimize the performance impact of the code loading. A code, size reduction of 68% is achieved, with a 2.2% execution speedup of the system-mode execution time, for a case study based on the MediaBench II benchmark suite
Pathways to clinical CLARITY: volumetric analysis of irregular, soft, and heterogeneous tissues in development and disease
AbstractThree-dimensional tissue-structural relationships are not well captured by typical thin-section histology, posing challenges for the study of tissue physiology and pathology. Moreover, while recent progress has been made with intact methods for clearing, labeling, and imaging whole organs such as the mature brain, these approaches are generally unsuitable for soft, irregular, and heterogeneous tissues that account for the vast majority of clinical samples and biopsies. Here we develop a biphasic hydrogel methodology, which along with automated analysis, provides for high-throughput quantitative volumetric interrogation of spatially-irregular and friable tissue structures. We validate and apply this approach in the examination of a variety of developing and diseased tissues, with specific focus on the dynamics of normal and pathological pancreatic innervation and development, including in clinical samples. Quantitative advantages of the intact-tissue approach were demonstrated compared to conventional thin-section histology, pointing to broad applications in both research and clinical settings.</jats:p
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
We propose a novel method for learning convolutional neural image
representations without manual supervision. We use motion cues in the form of
optical flow, to supervise representations of static images. The obvious
approach of training a network to predict flow from a single image can be
needlessly difficult due to intrinsic ambiguities in this prediction task. We
instead propose a much simpler learning goal: embed pixels such that the
similarity between their embeddings matches that between their optical flow
vectors. At test time, the learned deep network can be used without access to
video or flow information and transferred to tasks such as image
classification, detection, and segmentation. Our method, which significantly
simplifies previous attempts at using motion for self-supervision, achieves
state-of-the-art results in self-supervision using motion cues, competitive
results for self-supervision in general, and is overall state of the art in
self-supervised pretraining for semantic image segmentation, as demonstrated on
standard benchmarks
Reductie van het geheugengebruik van besturingssysteemkernen Memory Footprint Reduction for Operating System Kernels
In ingebedde systemen is er vaak maar een beperkte hoeveelheid geheugen beschikbaar. Daarom wordt er veel aandacht besteed aan het produceren van compacte programma's voor deze systemen, en zijn er allerhande technieken ontwikkeld die automatisch het geheugengebruik van programma's kunnen verkleinen. Tot nu toe richtten die technieken zich voornamelijk op de toepassingssoftware die op het systeem draait, en werd het besturingssysteem over het hoofd gezien. In dit proefschrift worden een aantal technieken beschreven die het mogelijk maken om op een geautomatiseerde manier het geheugengebruik van een besturingssysteemkern gevoelig te verkleinen. Daarbij wordt in eerste instantie gebruik gemaakt van compactietransformaties tijdens het linken. Als we de hardware en software waaruit het systeem samengesteld is kennen, is het mogelijk om nog verdere reducties te bekomen. Daartoe wordt de kern gespecialiseerd voor een bepaalde hardware-software combinatie. Overbodige functionaliteit wordt opgespoord en uit de kern verwijderd, terwijl de resterende functionaliteit wordt aangepast aan de specifieke gebruikspatronen die uit de hardware en software kunnen afgeleid worden. Als laatste worden technieken voorgesteld die het mogelijk maken om weinig of niet uitgevoerde code (bijvoorbeeld code voor het afhandelen van slechts zeldzaam optredende foutcondities) uit het geheugen te verwijderen. Deze code wordt dan enkel ingeladen op het moment dat ze effectief nodig is. Voor ons testsysteem kunnen we met de gecombineerde technieken het geheugengebruik van een Linux 2.4 kern met meer dan 48% verminderen
Ship recognition on the sea surface using aerial images taken by Uav : a deep learning approach
Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesOceans are very important for mankind, because they are a very important source of
food, they have a very large impact on the global environmental equilibrium, and it is
over the oceans that most of the world commerce is done. Thus, maritime surveillance
and monitoring, in particular identifying the ships used, is of great importance to
oversee activities like fishing, marine transportation, navigation in general, illegal
border encroachment, and search and rescue operations. In this thesis, we used images
obtained with Unmanned Aerial Vehicles (UAVs) over the Atlantic Ocean to identify
what type of ship (if any) is present in a given location. Images generated from UAV
cameras suffer from camera motion, scale variability, variability in the sea surface and
sun glares. Extracting information from these images is challenging and is mostly done
by human operators, but advances in computer vision technology and development of
deep learning techniques in recent years have made it possible to do so automatically.
We used four of the state-of-art pretrained deep learning network models, namely
VGG16, Xception, ResNet and InceptionResNet trained on ImageNet dataset, modified
their original structure using transfer learning based fine tuning techniques and then
trained them on our dataset to create new models. We managed to achieve very high
accuracy (99.6 to 99.9% correct classifications) when classifying the ships that appear
on the images of our dataset. With such a high success rate (albeit at the cost of high
computing power), we can proceed to implement these algorithms on maritime patrol
UAVs, and thus improve Maritime Situational Awareness
XMM-Newton evidence of shocked ISM in SN 1006: indications of hadronic acceleration
Shock fronts in young supernova remnants are the best candidates for being
sites of cosmic ray acceleration up to a few PeV, though conclusive
experimental evidence is still lacking. Hadron acceleration is expected to
increase the shock compression ratio, providing higher postshock densities, but
X-ray emission from shocked ambient medium has not firmly been detected yet in
remnants where particle acceleration is at work. We exploited the deep
observations of the XMM-Newton Large Program on SN 1006 to verify this
prediction. We performed spatially resolved spectral analysis of a set of
regions covering the southeastern rim of SN 1006. We studied the spatial
distribution of the thermodynamic properties of the ambient medium and
carefully verified the robustness of the result with respect to the analysis
method. We detected the contribution of the shocked ambient medium. We also
found that the postshock density of the interstellar medium significantly
increases in regions where particle acceleration is efficient. Under the
assumption of uniform preshock density, we found that the shock compression
ratio reaches a value of ~6 in regions near the nonthermal limbs. Our results
support the predictions of shock modification theory and indicate that effects
of acceleration of cosmic ray hadrons on the postshock plasma can be observed
in supernova remnants.Comment: Accepted for publication in A&
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