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Recent advances in the area of Deep Convolutional Neural Networks have led to steady progress, mainly observed in the field of object classification and localization. Extensive testing helped generate frameworks guaranteeing the initiation of successful network architectures. For this reason, the authors focus on bringing added value on specific nodes of a generic network configuration. We propose...
Identifying arbitrary power grid topologies in real time based on measurements in the grid is studied. A learning based approach is developed: binary classifiers are trained to approximate the maximum a-posteriori probability (MAP) detectors that each identifies the status of a distinct line. An efficient neural network architecture in which features are shared for inferences of all line statuses...
A supervised learning system requires labeled data during the training phase. Obtaining labels can be an expensive process, especially in medical imaging applications where a qualified expert may be needed to carefully analyze images and annotate them. This constrains the amount of labeled data available. This study explores the possibility of incorporating labeling behavior (viz., labeling latency)...
As different staining patterns of HEp-2 cells indicate different diseases, the classification of Indirect Immune Fluorescence (IIF) images on Human Epithelial-2 (HEp-2) cell is important for clinical applications. Different from traditional pattern recognition techniques, we use CNN to extract more high-level features for cell images classification. Compared to the existing CNN based HEp-2 classification...
This paper aims to present a methodology and a set of tools that could be used in order to check the validity of an Intelligent Transport System. These tools will consider each ITS component, i.e. C-ITS- R (Road Side Units) RSU, C-ITS-V (On-Board Unit) OBUU, C-ITS-C (Central Server) with its specific fea- tures as location precision for an OBU and adequate forwarding of dangerous events for an RSU...
The growing popularity of high performance computing has led to a new focus on bypassing or eliminating traditional I/O operations that are usually the bottlenecks for fast processing of large data volumes. One such solution uses a new network communication protocol called InfiniBand (IB) which supports remote direct memory access without making two copies of data (one in user space and the other...
The idea that a concept is properly learned by an agent when the agent is able to generate examples and non-examples of the concept, has motivated research on generative models. Generative models are trained with the aim of improving performance of tasks such as classification. In this paper, a Long Short Term Memory (LSTM) architecture for simultaneous generation-classification is presented. The...
A proper strategy to alleviate overfitting is critical to a deep neural network (DNN). In this paper, we introduce the cross-loss-function regularization for boosting the generalization capability of the DNN, which results in the multi-loss regularized DNN (ML-DNN) framework. For a particular learning task, e.g., image classification, only a single-loss function is used for all previous DNNs, and...
Decreasing hardware reliability makes robust firmware imperative for safety-critical applications. Hence, ensuring correct handling of errors in peripherals is a key objective during firmware design. To adequately support robustness considerations of firmware designers during implementation, an efficient qualitative fault injection method is required. This paper presents a high-speed fault injection...
The contemporary electric power system is highly dependent on Information and Communication Technologies which results in its exposure to new types of threats, such as Advanced Persistent Threats (APT) or Distributed-Denial-of-Service (DDoS) attacks. The most exposed components are Industrial Control Systems in substations and Distributed Control Systems in power plants. Therefore, it is necessary...
Connections form between neurons during neural development guided by growth cones, highly dynamic structures at the tips of growing axons. Understanding the biological mechanisms underlying this guidance requires understanding the dynamic morphology of growth cones, and this requires the segmentation of growth cone outlines from potentially very long timelapse movies. Previous approaches to this problem...
IEEE Std. 1687, or IJTAG, defines flexible serial scan-based architectures for accessing embedded instruments efficiently. In this paper, we present a novel test architecture that employs IEEE Std. 1687 together with an efficient test controller to carry out 3D-IC testing autonomously. The test controller can deliver parallel test data for the IEEE Std. 1687 structures and the cores under test, and...
Driving test is critical to the deployment of autonomous vehicles. It is necessary to review the related works since the methodologies summaries are rare, which will help to set up an integrated method for autonomous driving test in different development stages, and help to provide a reliable, quick, safe, low cost and reproducible method and accelerate the development of autonomous vehicle. In this...
Modern devices often include several embedded instruments, such as BISTs, sensors, and other analogcomponents. New standards, such as IEEE Std. 1687, providevehicles to access these instruments. In approaches based onreconfigurable scan networks, instruments are coupled withscan registers, connected into chains and interleaved withreconfigurable multiplexers, permitting a selective access todifferent...
This paper is an industrial experience report of applying the "Specification by Example" methodology and test-driven development to the development of a core component of a healthcare product. The methods are mapped to the four quadrants of technical debt introduced by Martin Fowler in order to show how they can help to avoid the accumulation of technical debt. The resulting data show that...
Many systems make use of concurrent tasks, however it is often difficult to test concurrent design. Therefore, many test cases are simplified and do not fully test all concurrency aspects of the system. We encountered this problem when analyzing test cases for concurrent flight software at NASA. To address this problem, we developed and evaluated a model based testing (MBT) technique for testing of...
The use of CubeSats has increased tremendously over the 15 years since the standard creation because the low cost and reduced project development cycle. However, one of the most concerns in reducing a project delivery time is the collateral effect in test process, resulting in failures in the mission operation. This paper proposes the combined use of the Model-Driven Engineering (MDE) and Model-Base...
While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no a priori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS)...
Repositories of educational resources currently offer various services, including searching and storing learning resources, these services are considered basic in the repositories of educational resources. Universities are producing educational resources, but the reality is that usually these resources generated by students and teachers of the university are often lost. This is mainly because these...
In the case of building large convolutional neural networks, signal propagation speed is one of priority factors. Training large neural structures requires enormous time for achieving satisfying accuracy. In addition, the networks need to be learn by very large sets of good quality training images, which is another time-consuming factor. The paper presents a fast computing framework with some methods...
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