Search results for "A* algorithm"

Using interactive evolutionary algorithms to help fit cochlear implants

2010

Efficiency analysis of burst transmissions with block ACK in contention-based 802.11e WLANs

2005

The channel utilization efficiency of the standard 802.11 networks is severely compromised when high data transmission rates are employed, since physical layer headers and control frames are transmitted at low rate, thus wasting more channel time, proportionally. The extensions defined in the emerging 802.11e for quality-of-service (QoS) provisioning include some new mechanisms developed in order to improve the efficiency. Those include data transmission bursting (referred to as TXOP operation) and acknowledgment aggregation (referred to as block ACK). These two features allow it to offer new data transmission services, in which the data delivery and acknowledgment unit is not a single fram…

Optimal and Greedy Heuristic Approaches for Scheduling and Mapping of Hardware Tasks to Reconfigurable Computing Devices

2020

Executing real-time tasks on dynamically reconfigurable FPGAs requires us to solve the challenges of scheduling and placement. In the past, many approaches have been presented to address these challenges. Still, most of them rely on idealized assumptions about the reconfigurability of FPGAs and the capabilities of commercial tool flows. In our work, we aim at solving these problems leveraging a practically useful 2D slot-based FPGA area model. We present optimal approaches for reconfigurable slot creation, hardware task assignment, and placement creation. We quantitatively compare optimal and heuristics algorithms through simulation experiments and show that the heuristics are rather close …

Clustering categorical data: A stability analysis framework

2011

Clustering to identify inherent structure is an important first step in data exploration. The k-means algorithm is a popular choice, but K-means is not generally appropriate for categorical data. A specific extension of k-means for categorical data is the k-modes algorithm. Both of these partition clustering methods are sensitive to the initialization of prototypes, which creates the difficulty of selecting the best solution for a given problem. In addition, selecting the number of clusters can be an issue. Further, the k-modes method is especially prone to instability when presented with ‘noisy’ data, since the calculation of the mode lacks the smoothing effect inherent in the calculation …

Detection of TV commercials

2004

This paper presents a system that labels TV shots either as commercial or program shots. The system uses two observations: logo presence and shot duration. These observations are modeled using HMMs, and a Viterbi decoder is finally used for shot labeling. The system has been tested on several hours of real video, achieving more than 99% correct labeling.

Advances in the statistical methodology for the selection of image descriptors for visual pattern representation and classification

1995

Recent advances in the statistical methodology for selecting optimal subsets of features (image descriptors) for visual pattern representation and classification are presented. The paper attempts to provide a guideline about which approach to choose with respect to the a priori knowledge of the problem. Two basic approaches are reviewed and the conditions under which they should be used are specified. References to more detailed material about each one of the methods are given and experimental results supporting the main conclusions are briefly outlined.

Indexing a sequence for mapping reads with a single mismatch

2014

Mapping reads against a genome sequence is an interesting and useful problem in computational molecular biology and bioinformatics. In this paper, we focus on the problem of indexing a sequence for mapping reads with a single mismatch. We first focus on a simpler problem where the length of the pattern is given beforehand during the data structure construction. This version of the problem is interesting in its own right in the context of the next generation sequencing. In the sequel, we show how to solve the more general problem. In both cases, our algorithm can construct an efficient data structure in time and space and can answer subsequent queries in time. Here, n is the length of the s…

Assessment of Deep Learning Methodology for Self-Organizing 5G Networks

2019

In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of …

Fast Implementation of Double-coupled Nonnegative Canonical Polyadic Decomposition

2019

Real-world data exhibiting high order/dimensionality and various couplings are linked to each other since they share some common characteristics. Coupled tensor decomposition has become a popular technique for group analysis in recent years, especially for simultaneous analysis of multi-block tensor data with common information. To address the multiblock tensor data, we propose a fast double-coupled nonnegative Canonical Polyadic Decomposition (FDC-NCPD) algorithm in this study, based on the linked CP tensor decomposition (LCPTD) model and fast Hierarchical Alternating Least Squares (Fast-HALS) algorithm. The proposed FDCNCPD algorithm enables simultaneous extraction of common components, i…

Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition

2021

One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocardiogram (ECG). To increase diagnostic sensitivity, the ECG might need to be acquired using an ambulatory system, as symptoms may occur during a patient’s daily life. In this paper, we propose using an ambulatory ECG (aECG) recording device with a low number of leads and then estimating the views that would have been obtained with a standard ECG location, reconstructing the complete Standard 12-Lead System, the most widely used system for diagnosis by cardiologists. Four approaches have been explored, including Linear Regression with ECG segmentation and Artificial Neural Networks (ANN). The b…