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20 pages, 3669 KiB

Open AccessArticle

Active Contour Model Using Fast Fourier Transformation for Salient Object Detection

by Umer Sadiq Khan, Xingjun Zhang and Yuanqi Su

Electronics 2021, 10(2), 192; https://doi.org/10.3390/electronics10020192 - 15 Jan 2021

Viewed by 3072

Abstract

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active [...] Read more.

The active contour model is a comprehensive research technique used for salient object detection. Most active contour models of saliency detection are developed in the context of natural scenes, and their role with synthetic and medical images is not well investigated. Existing active contour models perform efficiently in many complexities but facing challenges on synthetic and medical images due to the limited time like, precise automatic fitted contour and expensive initialization computational cost. Our intention is detecting automatic boundary of the object without re-initialization which further in evolution drive to extract salient object. For this, we propose a simple novel derivative of a numerical solution scheme, using fast Fourier transformation (FFT) in active contour (Snake) differential equations that has two major enhancements, namely it completely avoids the approximation of expansive spatial derivatives finite differences, and the regularization scheme can be generally extended more. Second, FFT is significantly faster compared to the traditional solution in spatial domain. Finally, this model practiced Fourier-force function to fit curves naturally and extract salient objects from the background. Compared with the state-of-the-art methods, the proposed method achieves at least a 3% increase of accuracy on three diverse set of images. Moreover, it runs very fast, and the average running time of the proposed methods is about one twelfth of the baseline. Full article

(This article belongs to the Special Issue Multidimensional Digital Signal Processing)

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12 pages, 7362 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Design and Implementation of a Digital Dual Orthogonal Outputs Chaotic Oscillator

by Yves Berviller, Etienne Tisserand, Philippe Poure and Hassan Rabah

Electronics 2020, 9(2), 264; https://doi.org/10.3390/electronics9020264 - 5 Feb 2020

Cited by 1 | Viewed by 4432

Abstract

Discrete time dynamical chaotic systems obey a set of recurrence equations involving one or more variables. Many chaotic maps have been proposed. None that simultaneously provides two sine–cosine outputs has stationary mean and standard deviation, or is quite robust with respect to the [...] Read more.

Discrete time dynamical chaotic systems obey a set of recurrence equations involving one or more variables. Many chaotic maps have been proposed. None that simultaneously provides two sine–cosine outputs has stationary mean and standard deviation, or is quite robust with respect to the data format used in the hardware implementation. Here, we propose a chaotic oscillator based on a complex phasor whose angular argument evolves according to a geometric progression that is independent of the instantaneous amplitude. In order to maintain the oscillations, the phasor magnitude is normalized at each iteration using an approximation factor. The statistical characteristics of this oscillator are stationary in the short term, and do not depend on the initial conditions. The mean and standard deviation of the two orthogonal sequences quickly approach 0 and

1 / \sqrt{2}

, respectively. The resulting distribution is similar to that of a digital sine with a constant angular step. We also present an FPGA architecture and its implementation results. This oscillator can be used in modulation schemes, such as the chaotic shift keying one or for data and image encryption. Finally, we show an original application that exploits the orthogonality of the two chaotic signals for the simultaneous encryption of two digital images. Full article

(This article belongs to the Special Issue Multidimensional Digital Signal Processing)

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14 pages, 2405 KiB

Open AccessFeature PaperArticle

High-Density EEG Signal Processing Based on Active-Source Reconstruction for Brain Network Analysis in Alzheimer’s Disease

by Fabio La Foresta, Francesco Carlo Morabito, Silvia Marino and Serena Dattola

Electronics 2019, 8(9), 1031; https://doi.org/10.3390/electronics8091031 - 13 Sep 2019

Cited by 15 | Viewed by 5616

Abstract

Alzheimer’s Disease (AD) is a neurological disorder characterized by a progressive deterioration of brain functions that affects, above all, older adults. It can be difficult to make an early diagnosis because its first symptoms are often associated with normal aging. Electroencephalography (EEG) can [...] Read more.

Alzheimer’s Disease (AD) is a neurological disorder characterized by a progressive deterioration of brain functions that affects, above all, older adults. It can be difficult to make an early diagnosis because its first symptoms are often associated with normal aging. Electroencephalography (EEG) can be used for evaluating the loss of brain functional connectivity in AD patients. The purpose of this paper is to study the brain network parameters through the estimation of Lagged Linear Connectivity (LLC), computed by eLORETA software, applied to High-Density EEG (HD-EEG) for 84 regions of interest (ROIs). The analysis involved three groups of subjects: 10 controls (CNT), 21 Mild Cognitive Impairment patients (MCI) and 9 AD patients. In particular, the purpose is to compare the results obtained using a 256-channel EEG, the corresponding 10–10 system 64-channel EEG and the corresponding 10–20 system 18-channel EEG, both of which are extracted from the 256-electrode configuration. The computation of the Characteristic Path Length, the Clustering Coefficient, and the Connection Density from HD-EEG configuration reveals a weakening of small-world properties of MCI and AD patients in comparison to healthy subjects. On the contrary, the variation of the network parameters was not detected correctly when we employed the standard 10–20 configuration. Only the results from HD-EEG are consistent with the expected behavior of the AD brain network. Full article

(This article belongs to the Special Issue Multidimensional Digital Signal Processing)

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15 pages, 731 KiB

Open AccessFeature PaperArticle

Marginal Component Analysis of ECG Signals for Beat-to-Beat Detection of Ventricular Late Potentials

by Cataldo Guaragnella, Maria Rizzi and Agostino Giorgio

Electronics 2019, 8(9), 1000; https://doi.org/10.3390/electronics8091000 - 6 Sep 2019

Cited by 14 | Viewed by 9362

Abstract

Heart condition diagnosis based on electrocardiogram signal analysis is the basic method used in prevention of cardiovascular diseases, which are recognized as the leading cause of death globally. To anticipate the occurrence of ventricular arrhythmia, the detection of Ventricular Late Potentials (VLPs) is [...] Read more.

Heart condition diagnosis based on electrocardiogram signal analysis is the basic method used in prevention of cardiovascular diseases, which are recognized as the leading cause of death globally. To anticipate the occurrence of ventricular arrhythmia, the detection of Ventricular Late Potentials (VLPs) is clinically worthwhile. VLPs are low-amplitude and high-frequency signals appearing at the end part of QRS complexes in the electrocardiogram, which can be considered as a robust feature for arrhythmia risk stratification in patients with cardiac diseases. This paper proposes a beat-to-beat VLP detection method based on the the marginal component analysis and investigates its performance taking into account different ratios between QRS and VLP power. After a denoising phase, performed adopting the singular vector decomposition technique, heartbeats characterized by VLP onsets are identified and extracted taking into account the vector magnitude of each high resolution ECG (HR-ECG) record. To evaluate the proposed method performance, a 15-lead HR-ECG database consisting of real VLP-negative and simulated VLP-positive patterns was used. The achieved results highlight the method validity for VLP detection. Full article

(This article belongs to the Special Issue Multidimensional Digital Signal Processing)

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13 pages, 1149 KiB

Open AccessArticle

FT-GAN: Face Transformation with Key Points Alignment for Pose-Invariant Face Recognition

by Weiwei Zhuang, Liang Chen, Chaoqun Hong, Yuxin Liang and Keshou Wu

Electronics 2019, 8(7), 807; https://doi.org/10.3390/electronics8070807 - 19 Jul 2019

Cited by 9 | Viewed by 8084

Abstract

Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. [...] Read more.

Face recognition has been comprehensively studied. However, face recognition in the wild still suffers from unconstrained face directions. Frontal face synthesis is a popular solution, but some facial features are missed after synthesis. This paper presents a novel method for pose-invariant face recognition. It is based on face transformation with key points alignment based on generative adversarial networks (FT-GAN). In this method, we introduce CycleGAN for pixel transformation to achieve coarse face transformation results, and these results are refined by key point alignment. In this way, frontal face synthesis is modeled as a two-task process. The results of comprehensive experiments show the effectiveness of FT-GAN. Full article

(This article belongs to the Special Issue Multidimensional Digital Signal Processing)

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22 pages, 963 KiB

Open AccessArticle

A Robust Semi-Blind Receiver for Joint Symbol and Channel Parameter Estimation in Multiple-Antenna Systems

by Jianhe Du, Meng Han, Yan Hua, Yuanzhi Chen and Heyun Lin

Electronics 2019, 8(5), 550; https://doi.org/10.3390/electronics8050550 - 16 May 2019

Cited by 5 | Viewed by 2875

Abstract

For multiple-antenna systems, the technologies of joint symbol and channel parameter estimation have been developed in recent works. However, existing technologies have a number of problems, such as performance degradation and the large cost of prior information. In this paper, a tensor space-time [...] Read more.

For multiple-antenna systems, the technologies of joint symbol and channel parameter estimation have been developed in recent works. However, existing technologies have a number of problems, such as performance degradation and the large cost of prior information. In this paper, a tensor space-time coding scheme in multiple-antenna systems was considered. This scheme allowed spreading, multiplexing, and allocating information symbols associated with multiple transmitted data streams. We showed that the received signal was formulated as a third-order tensor satisfying a Tucker-2 model, and then a robust semi-blind receiver was developed based on the optimized Levenberg–Marquardt (LM) algorithm. Under the assumption that the instantaneous channel state information (CSI) is unknown at the receiving end, the proposed semi-blind receiver jointly estimates the information symbol and channel parameters efficiently. The proposed receiver had a better estimation performance compared with existing semi-blind receivers, and still performed well when the channel became strongly correlated. Moreover, the proposed semi-blind receiver could be extended to the multi-user massive multiple-input multiple-output (MIMO) system for joint symbol and channel estimation. Computer simulation results were shown to demonstrate the effectiveness of the proposed receiver. Full article

(This article belongs to the Special Issue Multidimensional Digital Signal Processing)

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Review

Jump to: Research

20 pages, 6242 KiB

Open AccessReview

Sensor System: A Survey of Sensor Type, Ad Hoc Network Topology and Energy Harvesting Techniques

by Phuoc Duc Nguyen and Lok-won Kim

Electronics 2021, 10(2), 219; https://doi.org/10.3390/electronics10020219 - 19 Jan 2021

Cited by 11 | Viewed by 3172

Abstract

People nowadays are entering an era of rapid evolution due to the generation of massive amounts of data. Such information is produced with an enormous contribution from the use of billions of sensing devices equipped with in situ signal processing and communication capabilities [...] Read more.

People nowadays are entering an era of rapid evolution due to the generation of massive amounts of data. Such information is produced with an enormous contribution from the use of billions of sensing devices equipped with in situ signal processing and communication capabilities which form wireless sensor networks (WSNs). As the number of small devices connected to the Internet is higher than 50 billion, the Internet of Things (IoT) devices focus on sensing accuracy, communication efficiency, and low power consumption because IoT device deployment is mainly for correct information acquisition, remote node accessing, and longer-term operation with lower battery changing requirements. Thus, recently, there have been rich activities for original research in these domains. Various sensors used by processing devices can be heterogeneous or homogeneous. Since the devices are primarily expected to operate independently in an autonomous manner, the abilities of connection, communication, and ambient energy scavenging play significant roles, especially in a large-scale deployment. This paper classifies wireless sensor nodes into two major categories based the types of the sensor array (heterogeneous/homogeneous). It also emphasizes on the utilization of ad hoc networking and energy harvesting mechanisms as a fundamental cornerstone to building a self-governing, sustainable, and perpetually-operated sensor system. We review systems representative of each category and depict trends in system development. Full article

(This article belongs to the Special Issue Multidimensional Digital Signal Processing)

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18 pages, 604 KiB

Open AccessFeature PaperReview

Findings about LORETA Applied to High-Density EEG—A Review

by Serena Dattola, Francesco Carlo Morabito, Nadia Mammone and Fabio La Foresta

Electronics 2020, 9(4), 660; https://doi.org/10.3390/electronics9040660 - 17 Apr 2020

Cited by 20 | Viewed by 9311

Abstract

Electroencephalography (EEG) is a non-invasive diagnostic technique for recording brain electric activity. The EEG source localization has been an area of research widely explored during the last decades because it provides helpful information about brain physiology and abnormalities. Source localization consists in solving [...] Read more.

Electroencephalography (EEG) is a non-invasive diagnostic technique for recording brain electric activity. The EEG source localization has been an area of research widely explored during the last decades because it provides helpful information about brain physiology and abnormalities. Source localization consists in solving the so-called EEG inverse problem. Over the years, one of the most employed method for solving it has been LORETA (Low Resolution Electromagnetic Tomography). In particular, in this review, we focused on the findings about the LORETA family algorithms applied to high-density EEGs (HD-EEGs), used for improving the low spatial resolution deriving from the traditional EEG systems. The results were classified according to their clinical application and some aspects arisen from the analyzed papers were discussed. Finally, suggestions were provided for future improvement. In this way, the combination of LORETA with HD-EEGs could become an even more valuable tool for noninvasive clinical evaluation in the field of applied neuroscience. Full article

(This article belongs to the Special Issue Multidimensional Digital Signal Processing)

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