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The handwritten signature is perhaps the most accustomed way for the acknowledgement of the consent of an individual or the authentication of the identity of a person in numerous transactions. In addition, the authenticity of a questioned offline or static handwritten signature still poses a case of interest, especially in forensic related applications. A common approach in offline signature verification...
Motivated by the capability of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames be encoded with similar reconstruction coefficients. Then we map the TSC with a special type of stacked Recurrent Neural Network (sRNN). By taking advantage of sRNN in learning all parameters simultaneously, the nontrivial hyper-parameter...
An ultra-wideband metasurface is designed for suppressing the specular electromagnetic wave reflection or backward radar cross section (RCS). Square ring structure is chosen as the basic meta-atoms. A new physical mechanism based on size adjustment of the basic meta-atoms is proposed for ultrawideband manipulation of electromagnetic (EM) waves. Based on hybrid array pattern synthesis (APS) and particle...
In the area of applied optimisation, heuristics are a popular means to address computational problems of high complexity. Modelling the problem and mapping all variations of its solution into a so-called solution space are integral parts of this process. Representing solutions as graphs is common and, for a special type of graph, Prüfer Code (PC) offers a computationally efficient mapping (algorithms...
The problem of increasing noise immunity for sending messages usually solved by introducing the additional redundancy or by re-transmission of distorted fragment of the message. At the same time, the resulting delay is not always acceptable. An original concept for irredundant code efficiency rising is discussed. It is shown that some genetic-like algorithm applying to the class of permutative equivalent...
Ultra-deep sub-micron technology is shifting the design paradigm from area optimization to power optimization. In the context of Network-on-Chip (NoC) based design, energy consumption due to data transfer among network nodes is no longer negligible. Starting from the observation that, among the two brain hemispheres around 1 out of 106 synapses are active at the same time, in this paper we propose...
In this paper, we discuss the architecture exploration of a Neuromorphic Signal Processing Integrated Circuit using Precise Timing. This device is intended to fulfill the role of a Digital Signal Processor in the spiking domain, becoming an essential tool to Spiking Neuromorphic Sensors such as Dynamic Vision Sensors. Our approach is based on the use of Spiking Neural Networks with preset topology...
Recent research in computed tomographic imaging has focused on developing techniques that enable reduction of the X-ray radiation dose without loss of quality of the reconstructed images or volumes. While penalized weighted-least squares (PWLS) approaches have been popular for CT image reconstruction, their performance degrades for very low dose levels due to the inaccuracy of the underlying WLS statistical...
We propose a software-based approach to provide an efficient way for designing unit cells based on the optimization algorithm and commercial electromagnetic software. Unit cells are comprised of discretely random lattice, square sub-blocks. The approach combined binary particle swarm optimization (BPSO) and CST Microwave Studio is used to achieve the optimal arrangement of the square metal sub-blocks...
Data representation plays an important role in performance of machine learning algorithms. Since data usually lacks the desired quality, many efforts have been made to provide a more desirable representation of data. Among many different approaches, sparse data representation has gained popularity in recent years. In this paper, we propose a new sparse autoencoder by imposing the power two of smoothed...
The MPS approach (Minimal Path Selection) has shown in [1] to provide robust and accurate segmentation of cracks within pavement images compared to other algorithms. As a counterpart, MPS suffers from a large computing time. In this paper, we present three different ongoing improvements to reduce the computing time and to improve the overall segmentation performance. Most of the work focuses on the...
In manufacturing, the increasing involvement of autonomous robots in production processes poses new challenges on the production management. In this paper we report on the usage of Optimization Modulo Theories (OMT) to solve certain multi-robot scheduling problems in this area. Whereas currently existing methods are heuristic, our approach guarantees optimality for the computed solution. We do not...
A key problem in large-scale reinforcement learning is to deal with big data, in terms of a very large number of environment states and many possible actions. Function approximation can improve the ability of a reinforcement learner to solve large-scale problems. Tile coding and Kanerva coding are two classical methods for implementing function approximation, but these methods may give poor performance...
Learning to hash has been recognized to accomplish highly efficient storage and retrieval for large-scale visual data. Particularly, ranking-based hashing techniques have recently attracted broad research attention because ranking accuracy among the retrieved data is well explored and their objective is more applicable to realistic search tasks. However, directly optimizing discrete hash codes without...
Most existing binary embedding methods prefer compact binary codes (b-dimensional) to avoid high computational and memory cost of projecting high-dimensional visual features (d-dimensional, b
Numerous methods have been proposed for person re-identification, most of which however neglect the matching efficiency. Recently, several hashing based approaches have been developed to make re-identification more scalable for large-scale gallery sets. Despite their efficiency, these works ignore cross-camera variations, which severely deteriorate the final matching accuracy. To address the above...
Most existing hashing methods resort to binary codes for similarity search, owing to the high efficiency of computation and storage. However, binary codes lack enough capability in similarity preservation, resulting in less desirable performance. To address this issue, we propose an asymmetric multi-valued hashing method supported by two different non-binary embeddings. (1) A real-valued embedding...
Deep convolutional neural networks (CNNs) have proven highly effective for visual recognition, where learning a universal representation from activations of convolutional layer plays a fundamental problem. In this paper, we present Fisher Vector encoding with Variational Auto-Encoder (FV-VAE), a novel deep architecture that quantizes the local activations of convolutional layer in a deep generative...
The location problem of logistics center is the key process of logistics distribution, in order to improve the accuracy of the location of logistics center, this paper according to the parallelism and characteristics of global optimization of genetic algorithm. A method based on genetic algorithm is proposed to solve the problem of logistics center location, and set up with the minimum total cost...
We consider the problem of subset selection in the online setting, where data arrive incrementally. Instead of storing and running subset selection on the entire dataset, we propose an incremental subset selection framework that, at each time instant, uses the previously selected set of representatives and the new batch of data in order to update the set of representatives. We cast the problem as...
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