18,792 research outputs found
A Hierachical Infrastrucutre for SOC Test Management
HD2BIST - a complete hierarchical framework for BIST scheduling, data-patterns delivery, and diagnosis of complex systems - maximizes and simplifies the reuse of built-in test architectures. HD2BIST optimizes the flexibility for chip designers in planning an overall SoC test strategy by defining a test access method that provides direct virtual access to each core of the system
SCAN: Learning Hierarchical Compositional Visual Concepts
The seemingly infinite diversity of the natural world arises from a
relatively small set of coherent rules, such as the laws of physics or
chemistry. We conjecture that these rules give rise to regularities that can be
discovered through primarily unsupervised experiences and represented as
abstract concepts. If such representations are compositional and hierarchical,
they can be recombined into an exponentially large set of new concepts. This
paper describes SCAN (Symbol-Concept Association Network), a new framework for
learning such abstractions in the visual domain. SCAN learns concepts through
fast symbol association, grounding them in disentangled visual primitives that
are discovered in an unsupervised manner. Unlike state of the art multimodal
generative model baselines, our approach requires very few pairings between
symbols and images and makes no assumptions about the form of symbol
representations. Once trained, SCAN is capable of multimodal bi-directional
inference, generating a diverse set of image samples from symbolic descriptions
and vice versa. It also allows for traversal and manipulation of the implicit
hierarchy of visual concepts through symbolic instructions and learnt logical
recombination operations. Such manipulations enable SCAN to break away from its
training data distribution and imagine novel visual concepts through
symbolically instructed recombination of previously learnt concepts
Plug & Test at System Level via Testable TLM Primitives
With the evolution of Electronic System Level (ESL) design methodologies, we are experiencing an extensive use of Transaction-Level Modeling (TLM). TLM is a high-level approach to modeling digital systems where details of the communication among modules are separated from the those of the implementation of functional units. This paper represents a first step toward the automatic insertion of testing capabilities at the transaction level by definition of testable TLM primitives. The use of testable TLM primitives should help designers to easily get testable transaction level descriptions implementing what we call a "Plug & Test" design methodology. The proposed approach is intended to work both with hardware and software implementations. In particular, in this paper we will focus on the design of a testable FIFO communication channel to show how designers are given the freedom of trading-off complexity, testability levels, and cos
Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies
Robots are increasingly entering uncertain and unstructured environments.
Within these, robots are bound to face unexpected external disturbances like
accidental human or tool collisions. Robots must develop the capacity to
respond to unexpected events. That is not only identifying the sudden anomaly,
but also deciding how to handle it. In this work, we contribute a recovery
policy that allows a robot to recovery from various anomalous scenarios across
different tasks and conditions in a consistent and robust fashion. The system
organizes tasks as a sequence of nodes composed of internal modules such as
motion generation and introspection. When an introspection module flags an
anomaly, the recovery strategy is triggered and reverts the task execution by
selecting a target node as a function of a state dependency chart. The new
skill allows the robot to overcome the effects of the external disturbance and
conclude the task. Our system recovers from accidental human and tool
collisions in a number of tasks. Of particular importance is the fact that we
test the robustness of the recovery system by triggering anomalies at each node
in the task graph showing robust recovery everywhere in the task. We also
trigger multiple and repeated anomalies at each of the nodes of the task
showing that the recovery system can consistently recover anywhere in the
presence of strong and pervasive anomalous conditions. Robust recovery systems
will be key enablers for long-term autonomy in robot systems. Supplemental info
including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl
Point cloud segmentation using hierarchical tree for architectural models
Recent developments in the 3D scanning technologies have made the generation
of highly accurate 3D point clouds relatively easy but the segmentation of
these point clouds remains a challenging area. A number of techniques have set
precedent of either planar or primitive based segmentation in literature. In
this work, we present a novel and an effective primitive based point cloud
segmentation algorithm. The primary focus, i.e. the main technical contribution
of our method is a hierarchical tree which iteratively divides the point cloud
into segments. This tree uses an exclusive energy function and a 3D
convolutional neural network, HollowNets to classify the segments. We test the
efficacy of our proposed approach using both real and synthetic data obtaining
an accuracy greater than 90% for domes and minarets.Comment: 9 pages. 10 figures. Submitted in EuroGraphics 201
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