Editor's Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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Article

Article
Efficient Retrieval of Music Recordings Using Graph-Based Index Structures
Signals 2021, 2(2), 336-352; https://doi.org/10.3390/signals2020021 - 17 May 2021
Abstract
Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In [...] Read more.
Flexible retrieval systems are required for conveniently browsing through large music collections. In a particular content-based music retrieval scenario, the user provides a query audio snippet, and the retrieval system returns music recordings from the collection that are similar to the query. In this scenario, a fast response from the system is essential for a positive user experience. For realizing low response times, one requires index structures that facilitate efficient search operations. One such index structure is the K-d tree, which has already been used in music retrieval systems. As an alternative, we propose to use a modern graph-based index, denoted as Hierarchical Navigable Small World (HNSW) graph. As our main contribution, we explore its potential in the context of a cross-version music retrieval application. In particular, we report on systematic experiments comparing graph- and tree-based index structures in terms of the retrieval quality, disk space requirements, and runtimes. Despite the fact that the HNSW index provides only an approximate solution to the nearest neighbor search problem, we demonstrate that it has almost no negative impact on the retrieval quality in our application. As our main result, we show that the HNSW-based retrieval is several orders of magnitude faster. Furthermore, the graph structure also works well with high-dimensional index items, unlike the tree-based structure. Given these merits, we highlight the practical relevance of the HNSW graph for music information retrieval (MIR) applications. Full article
(This article belongs to the Special Issue Advances in Processing and Understanding of Music Signals)
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Article
Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins
Signals 2021, 2(2), 304-335; https://doi.org/10.3390/signals2020020 - 13 May 2021
Abstract
This study investigates the potential of fluorescence imaging in conjunction with an original, fused segmentation framework for enhanced detection and delineation of brain tumor margins. By means of a test bed optical microscopy system, autofluorescence is utilized to capture gray level images of [...] Read more.
This study investigates the potential of fluorescence imaging in conjunction with an original, fused segmentation framework for enhanced detection and delineation of brain tumor margins. By means of a test bed optical microscopy system, autofluorescence is utilized to capture gray level images of brain tumor specimens through slices, obtained at various depths from the surface, each of 10 µm thickness. The samples used in this study originate from tumor cell lines characterized as Gli36ϑEGRF cells expressing a green fluorescent protein. An innovative three-step biomedical image analysis framework is presented aimed at enhancing the contrast and dissimilarity between the malignant and the remaining tissue regions to allow for enhanced visualization and accurate extraction of tumor boundaries. The fluorescence image acquisition system implemented with an appropriate unsupervised pipeline of image processing and fusion algorithms indicates clear differentiation of tumor margins and increased image contrast. Establishing protocols for the safe administration of fluorescent protein molecules, these would be introduced into glioma tissues or cells either at a pre-surgery stage or applied to the malignant tissue intraoperatively; typical applications encompass areas of fluorescence-guided surgery (FGS) and confocal laser endomicroscopy (CLE). As a result, this image acquisition scheme could significantly improve decision-making during brain tumor resection procedures and significantly facilitate brain surgery neuropathology during operation. Full article
(This article belongs to the Special Issue Biosignals Processing and Analysis in Biomedicine)
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Article
A Numerical Study on Computational Time Reversal for Structural Health Monitoring
Signals 2021, 2(2), 225-244; https://doi.org/10.3390/signals2020017 - 22 Apr 2021
Cited by 1
Abstract
Structural health monitoring problems are studied numerically with the time reversal method (TR). The dynamic output of the structure is applied, time reversed, as an external loading and its propagation within the deformable medium is followed backwards in time. Unknown loading sources or [...] Read more.
Structural health monitoring problems are studied numerically with the time reversal method (TR). The dynamic output of the structure is applied, time reversed, as an external loading and its propagation within the deformable medium is followed backwards in time. Unknown loading sources or damages can be discovered by means of this method, focused by the reversed signal. The method is theoretically justified by the time-reversibility of the wave equation. Damage identification problems relevant to structural health monitoring for truss and frame structures are studied here. Beam structures are used for the demonstration of the concept, by means of numerical experiments. The influence of the signal-to-noise ratio (SNR) on the results was investigated, since this quantity influences the applicability of the method in real-life cases. The method is promising, in view of the increasing availability of distributed intelligent sensors and actuators. Full article
(This article belongs to the Special Issue Advanced Signal/Data Processing for Structural Health Monitoring)
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Article
Trajectory Optimisation for Cooperative Target Tracking with Passive Mobile Sensors
Signals 2021, 2(2), 174-188; https://doi.org/10.3390/signals2020014 - 07 Apr 2021
Cited by 1
Abstract
The paper considers the problem of tracking a moving target using a pair of cooperative bearing-only mobile sensors. Sensor trajectory optimisation plays the central role in this problem, with the objective to minimize the estimation error of the target state. Two approximate closed-form [...] Read more.
The paper considers the problem of tracking a moving target using a pair of cooperative bearing-only mobile sensors. Sensor trajectory optimisation plays the central role in this problem, with the objective to minimize the estimation error of the target state. Two approximate closed-form statistical reward functions, referred to as the Expected Rényi information divergence (RID) and the Determinant of the Fisher Information Matrix (FIM), are analysed and discussed in the paper. Being available analytically, the two reward functions are fast to compute and therefore potentially useful for longer horizon sensor trajectory planning. The paper demonstrates, both numerically and from the information geometric viewpoint, that the Determinant of the FIM is a superior reward function. The problem with the Expected RID is that the approximation involved in its derivation significantly reduces the correlation between the target state estimates at two sensors, and consequently results in poorer performance. Full article
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Article
Development of the First Portuguese Radar Tracking Sensor for Space Debris
Signals 2021, 2(1), 122-137; https://doi.org/10.3390/signals2010011 - 09 Mar 2021
Cited by 2
Abstract
Currently, space debris represents a threat for satellites and space-based operations, both in-orbit and during the launching process. The yearly increase in space debris represents a serious concern to major space agencies leading to the development of dedicated space programs to deal with [...] Read more.
Currently, space debris represents a threat for satellites and space-based operations, both in-orbit and during the launching process. The yearly increase in space debris represents a serious concern to major space agencies leading to the development of dedicated space programs to deal with this issue. Ground-based radars can detect Earth orbiting debris down to a few square centimeters and therefore constitute a major building block of a space debris monitoring system. New radar sensors are required in Europe to enhance capabilities and availability of its small radar network capable of tracking and surveying space objects and to respond to the debris increase expected from the New Space economy activities. This article presents ATLAS, a new tracking radar system for debris detection located in Portugal. It starts by an extensive technical description of all the system components followed by a study that estimates its future performance. A section dedicated to waveform design is also presented, since the system allows the usage of several types of pulse modulation schemes such as LFM and phase coded modulations while enabling the development and testing of more advanced ones. By presenting an architecture that is highly modular with fully digital signal processing, ATLAS establishes a platform for fast and easy development, research, and innovation. The system follows the use of Commercial-Off-The-Shelf technologies and Open Systems which is unique among current radar systems. Full article
(This article belongs to the Special Issue Signal Processing in Modern Radars)
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Article
On the Synergy between Nonconvex Extensions of the Tensor Nuclear Norm for Tensor Recovery
Signals 2021, 2(1), 108-121; https://doi.org/10.3390/signals2010010 - 18 Feb 2021
Abstract
Low-rank tensor recovery has attracted much attention among various tensor recovery approaches. A tensor rank has several definitions, unlike the matrix rank—e.g., the CP rank and the Tucker rank. Many low-rank tensor recovery methods are focused on the Tucker rank. Since the Tucker [...] Read more.
Low-rank tensor recovery has attracted much attention among various tensor recovery approaches. A tensor rank has several definitions, unlike the matrix rank—e.g., the CP rank and the Tucker rank. Many low-rank tensor recovery methods are focused on the Tucker rank. Since the Tucker rank is nonconvex and discontinuous, many relaxations of the Tucker rank have been proposed, e.g., the sum of nuclear norm, weighted tensor nuclear norm, and weighted tensor schatten-p norm. In particular, the weighted tensor schatten-p norm has two parameters, the weight and p, and the sum of nuclear norm and weighted tensor nuclear norm are special cases of these parameters. However, there has been no detailed discussion of whether the effects of the weighting and p are synergistic. In this paper, we propose a novel low-rank tensor completion model using the weighted tensor schatten-p norm to reveal the relationships between the weight and p. To clarify whether complex methods such as the weighted tensor schatten-p norm are necessary, we compare them with a simple method using rank-constrained minimization. It was found that the simple methods did not outperform the complex methods unless the rank of the original tensor could be accurately known. If we can obtain the ideal weight, p=1 is sufficient, although it is necessary to set p<1 when using the weights obtained from observations. These results are consistent with existing reports. Full article
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Article
3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds
Signals 2021, 2(1), 98-107; https://doi.org/10.3390/signals2010009 - 12 Feb 2021
Cited by 1
Abstract
Three-dimensional (3D) object detection is essential in autonomous driving. Three-dimensional (3D) Lidar sensor can capture three-dimensional objects, such as vehicles, cycles, pedestrians, and other objects on the road. Although Lidar can generate point clouds in 3D space, it still lacks the fine resolution [...] Read more.
Three-dimensional (3D) object detection is essential in autonomous driving. Three-dimensional (3D) Lidar sensor can capture three-dimensional objects, such as vehicles, cycles, pedestrians, and other objects on the road. Although Lidar can generate point clouds in 3D space, it still lacks the fine resolution of 2D information. Therefore, Lidar and camera fusion has gradually become a practical method for 3D object detection. Previous strategies focused on the extraction of voxel points and the fusion of feature maps. However, the biggest challenge is in extracting enough edge information to detect small objects. To solve this problem, we found that attention modules are beneficial in detecting small objects. In this work, we developed Frustum ConvNet and attention modules for the fusion of images from a camera and point clouds from a Lidar. Multilayer Perceptron (MLP) and tanh activation functions were used in the attention modules. Furthermore, the attention modules were designed on PointNet to perform multilayer edge detection for 3D object detection. Compared with a previous well-known method, Frustum ConvNet, our method achieved competitive results, with an improvement of 0.27%, 0.43%, and 0.36% in Average Precision (AP) for 3D object detection in easy, moderate, and hard cases, respectively, and an improvement of 0.21%, 0.27%, and 0.01% in AP for Bird’s Eye View (BEV) object detection in easy, moderate, and hard cases, respectively, on the KITTI detection benchmarks. Our method also obtained the best results in four cases in AP on the indoor SUN-RGBD dataset for 3D object detection. Full article
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Article
Convolutional Neural Network for Roadside Barriers Detection: Transfer Learning versus Non-Transfer Learning
Signals 2021, 2(1), 72-86; https://doi.org/10.3390/signals2010007 - 01 Feb 2021
Abstract
Increasingly more governmental organizations in the U.S. have started to implement artificial intelligence to enhance the asset management process with an objective of controlling the costs of data collection. To help the Wyoming Department of Transportation (WYDOT) to automate the data collections process, [...] Read more.
Increasingly more governmental organizations in the U.S. have started to implement artificial intelligence to enhance the asset management process with an objective of controlling the costs of data collection. To help the Wyoming Department of Transportation (WYDOT) to automate the data collections process, related to various assets in the state, an automated assets management data collection was proposed. As an example, the automated traffic barriers asset dataset would collect geometric characteristics, and barriers’ materials’ conditions, e.g., being rusty or not. The information would be stored and accessed for asset-management-decision-making and optimization process to fulfill various objectives such as traffic safety improvement, or assets’ enhancement. For instance, the State of Wyoming has more than a million feet of roadside barriers, worth more than 100 million dollars. One-time collection of various characteristics of those barriers has cost the state more than half a million dollars. Thus, this study, as a first step for comprehensive data collection, proposed a novel approach in identification of roadside barrier types. Pre-trained inception v3, denseNet 121, and VGG 19 were implemented in this study. Transfer learning was used as there were only 250 images for training of the dataset for each category. For that method, the topmost layers were removed, along with adding two more new layers while freezing the remaining layers. This study achieved an accuracy of 97% by the VGG 19 network, training only the few last layers of the model along with adding two dense layers for top layers. The results indicated that although there are not enough observations related to traffic barrier images, a transfer learning application could be considered in data collection. A simple architecture non-transfer model was also implemented. This model achieved an accuracy of 85%, being better that the two other transfer learning techniques. It should be reiterated that although non-transfer learning technique outperformed inception and denseNet networks, it comes short significantly when it come to the VGG network. Full article
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Article
Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
Signals 2021, 2(1), 55-71; https://doi.org/10.3390/signals2010006 - 23 Jan 2021
Cited by 5
Abstract
This paper investigates eye behaviour through blinks activity during stress conditions. Although eye blinking is a semi-voluntary action, it is considered to be affected by one’s emotional states such as arousal or stress. The blinking rate provides information towards this direction, however, the [...] Read more.
This paper investigates eye behaviour through blinks activity during stress conditions. Although eye blinking is a semi-voluntary action, it is considered to be affected by one’s emotional states such as arousal or stress. The blinking rate provides information towards this direction, however, the analysis on the entire eye aperture timeseries and the corresponding blinking patterns provide enhanced information on eye behaviour during stress conditions. Thus, two experimental protocols were established to induce affective states (neutral, relaxed and stress) systematically through a variety of external and internal stressors. The study populations included 24 and 58 participants respectively performing 12 experimental affective trials. After the preprocessing phase, the eye aperture timeseries and the corresponding features were extracted. The behaviour of inter-blink intervals (IBI) was investigated using the Markovian Analysis to quantify incidence dynamics in sequences of blinks. Moreover, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network models were employed to discriminate stressed versus neutral tasks per cognitive process using the sequence of IBI. The classification accuracy reached a percentage of 81.3% which is very promising considering the unimodal analysis and the noninvasiveness modality used. Full article
(This article belongs to the Special Issue Biosignals Processing and Analysis in Biomedicine)
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Article
Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
Signals 2020, 1(2), 188-208; https://doi.org/10.3390/signals1020011 - 04 Dec 2020
Cited by 3
Abstract
Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to [...] Read more.
Wearable devices continuously measure multiple physiological variables to inform users of health and behavior indicators. The computed health indicators must rely on informative signals obtained by processing the raw physiological variables with powerful noise- and artifacts-filtering algorithms. In this study, we aimed to elucidate the effects of signal processing techniques on the accuracy of detecting and discriminating physical activity (PA) and acute psychological stress (APS) using physiological measurements (blood volume pulse, heart rate, skin temperature, galvanic skin response, and accelerometer) collected from a wristband. Data from 207 experiments involving 24 subjects were used to develop signal processing, feature extraction, and machine learning (ML) algorithms that can detect and discriminate PA and APS when they occur individually or concurrently, classify different types of PA and APS, and estimate energy expenditure (EE). Training data were used to generate feature variables from the physiological variables and develop ML models (naïve Bayes, decision tree, k-nearest neighbor, linear discriminant, ensemble learning, and support vector machine). Results from an independent labeled testing data set demonstrate that PA was detected and classified with an accuracy of 99.3%, and APS was detected and classified with an accuracy of 92.7%, whereas the simultaneous occurrences of both PA and APS were detected and classified with an accuracy of 89.9% (relative to actual class labels), and EE was estimated with a low mean absolute error of 0.02 metabolic equivalent of task (MET).The data filtering and adaptive noise cancellation techniques used to mitigate the effects of noise and artifacts on the classification results increased the detection and discrimination accuracy by 0.7% and 3.0% for PA and APS, respectively, and by 18% for EE estimation. The results demonstrate the physiological measurements from wristband devices are susceptible to noise and artifacts, and elucidate the effects of signal processing and feature extraction on the accuracy of detection, classification, and estimation of PA and APS. Full article
(This article belongs to the Special Issue Signals in Health Care)
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Article
Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks
Signals 2020, 1(2), 170-187; https://doi.org/10.3390/signals1020010 - 18 Nov 2020
Cited by 6
Abstract
A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to [...] Read more.
A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO2 emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO2 reduction gains of up to 31.83%. Full article
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Article
Improving Speech Quality for Hearing Aid Applications Based on Wiener Filter and Composite of Deep Denoising Autoencoders
Signals 2020, 1(2), 138-156; https://doi.org/10.3390/signals1020008 - 21 Oct 2020
Cited by 1
Abstract
In hearing aid devices, speech enhancement techniques are a critical component to enable users with hearing loss to attain improved speech quality under noisy conditions. Recently, the deep denoising autoencoder (DDAE) was adopted successfully for recovering the desired speech from noisy observations. However, [...] Read more.
In hearing aid devices, speech enhancement techniques are a critical component to enable users with hearing loss to attain improved speech quality under noisy conditions. Recently, the deep denoising autoencoder (DDAE) was adopted successfully for recovering the desired speech from noisy observations. However, a single DDAE cannot extract contextual information sufficiently due to the poor generalization in an unknown signal-to-noise ratio (SNR), the local minima, and the fact that the enhanced output shows some residual noise and some level of discontinuity. In this paper, we propose a hybrid approach for hearing aid applications based on two stages: (1) the Wiener filter, which attenuates the noise component and generates a clean speech signal; (2) a composite of three DDAEs with different window lengths, each of which is specialized for a specific enhancement task. Two typical high-frequency hearing loss audiograms were used to test the performance of the approach: Audiogram 1 = (0, 0, 0, 60, 80, 90) and Audiogram 2 = (0, 15, 30, 60, 80, 85). The hearing-aid speech perception index, the hearing-aid speech quality index, and the perceptual evaluation of speech quality were used to evaluate the performance. The experimental results show that the proposed method achieved significantly better results compared with the Wiener filter or a single deep denoising autoencoder alone. Full article
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Article
ScientIST: Biomedical Engineering Experiments Supported by Mobile Devices, Cloud and IoT
Signals 2020, 1(2), 110-120; https://doi.org/10.3390/signals1020006 - 07 Sep 2020
Cited by 2
Abstract
Currently, mobile devices such as smartphones or tablets are widespread within the student community. However, their potential to be used in classrooms is yet to be fully explored. Our work proposes an approach that benefits from the ease of access to mobile devices, [...] Read more.
Currently, mobile devices such as smartphones or tablets are widespread within the student community. However, their potential to be used in classrooms is yet to be fully explored. Our work proposes an approach that benefits from the ease of access to mobile devices, and combines it with state-of-the-art software and hardware. This approach builds upon previous developments from our team on biosignal acquisition and analysis, and is designed towards the enrichment of the teaching experience for students, namely in what concerns laboratory activities in the field of biomedical engineering. The implementation of such methodology aims at involving students more actively in the learning process, using case studies and emerging educational approaches such as project-based, active and research-based learning. It also provides an effective option for remote teaching, as recently required by the COVID-19 outbreak. In our approach (ScientIST) we explore the use of the Arduino MKR WIFI 1010, a variant of the popular electronic platform, recently launched for prototyping Internet of Things (IoT) applications, and the Google Science Journal (GSJ), a digital notebook created by Google, to support laboratory activities using mobile devices. This approach has shown promising results in two case studies, namely, documenting a Histology laboratory class and a Photoplethysmography (PPG) data acquisition and processing experiment. The System Usability Scale (SUS) was used in the evaluation of the students’ experience, revealing an overall score of 78.68%. Full article
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Article
Dynamic Model Averaging in Economics and Finance with fDMA: A Package for R
Signals 2020, 1(1), 47-99; https://doi.org/10.3390/signals1010004 - 06 Jul 2020
Cited by 2
Abstract
The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and [...] Read more.
The described R package allows to estimate Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model. The original methods, and additionally, some selected modifications of these methods are implemented. For example the user can choose between recursive moment estimation and exponentially moving average for variance updating in the base DMA. Moreover, inclusion probabilities can be computed in a way using “Google Trends” data. The code is written with respect to minimise the computational burden, which is quite an obstacle for DMA algorithm if numerous variables are used. For example, this package allows for parallel computations and implementation of the Occam’s window approach. However, clarity and readability of the code, and possibility for an R-familiar user to make his or her own small modifications in reasonably small time and with low effort are also taken under consideration. Except that, some alternative (benchmark) forecasts can also be quickly performed within this package. Indeed, this package is designed in a way that is hoped to be especially useful for practitioners and researchers in economics and finance. Full article
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Article
The Effect of Data Transformation on Singular Spectrum Analysis for Forecasting
Signals 2020, 1(1), 4-25; https://doi.org/10.3390/signals1010002 - 07 May 2020
Cited by 6
Abstract
Data transformations are an important tool for improving the accuracy of forecasts from time series models. Historically, the impact of transformations have been evaluated on the forecasting performance of different parametric and nonparametric forecasting models. However, researchers have overlooked the evaluation of this [...] Read more.
Data transformations are an important tool for improving the accuracy of forecasts from time series models. Historically, the impact of transformations have been evaluated on the forecasting performance of different parametric and nonparametric forecasting models. However, researchers have overlooked the evaluation of this factor in relation to the nonparametric forecasting model of Singular Spectrum Analysis (SSA). In this paper, we focus entirely on the impact of data transformations in the form of standardisation and logarithmic transformations on the forecasting performance of SSA when applied to 100 different datasets with different characteristics. Our findings indicate that data transformations have a significant impact on SSA forecasts at particular sampling frequencies. Full article
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Review

Review
An Educational Guide through the FMP Notebooks for Teaching and Learning Fundamentals of Music Processing
Signals 2021, 2(2), 245-285; https://doi.org/10.3390/signals2020018 - 30 Apr 2021
Cited by 4
Abstract
This paper provides a guide through the FMP notebooks, a comprehensive collection of educational material for teaching and learning fundamentals of music processing (FMP) with a particular focus on the audio domain. Organized in nine parts that consist of more than 100 individual [...] Read more.
This paper provides a guide through the FMP notebooks, a comprehensive collection of educational material for teaching and learning fundamentals of music processing (FMP) with a particular focus on the audio domain. Organized in nine parts that consist of more than 100 individual notebooks, this collection discusses well-established topics in music information retrieval (MIR) such as beat tracking, chord recognition, music synchronization, audio fingerprinting, music segmentation, and source separation, to name a few. These MIR tasks provide motivating and tangible examples that students can hold onto when studying technical aspects in signal processing, information retrieval, or pattern analysis. The FMP notebooks comprise detailed textbook-like explanations of central techniques and algorithms combined with Python code examples that illustrate how to implement the methods. All components, including the introductions of MIR scenarios, illustrations, sound examples, technical concepts, mathematical details, and code examples, are integrated into a unified framework based on Jupyter notebooks. Providing a platform with many baseline implementations, the FMP notebooks are suited for conducting experiments and generating educational material for lectures, thus addressing students, teachers, and researchers. While giving a guide through the notebooks, this paper’s objective is to yield concrete examples on how to use the FMP notebooks to create an enriching, interactive, and interdisciplinary supplement for studies in science, technology, engineering, and mathematics. The FMP notebooks (including HTML exports) are publicly accessible under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Full article
(This article belongs to the Special Issue Advances in Processing and Understanding of Music Signals)
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Review
Wireless Power Transfer Approaches for Medical Implants: A Review
Signals 2020, 1(2), 209-229; https://doi.org/10.3390/signals1020012 - 16 Dec 2020
Cited by 18
Abstract
Wireless power transmission (WPT) is a critical technology that provides an alternative for wireless power and communication with implantable medical devices (IMDs). This article provides a study concentrating on popular WPT techniques for IMDs including inductive coupling, microwave, ultrasound, and hybrid wireless power [...] Read more.
Wireless power transmission (WPT) is a critical technology that provides an alternative for wireless power and communication with implantable medical devices (IMDs). This article provides a study concentrating on popular WPT techniques for IMDs including inductive coupling, microwave, ultrasound, and hybrid wireless power transmission (HWPT) systems. Moreover, an overview of the major works is analyzed with a comparison of the symmetric and asymmetric design elements, operating frequency, distance, efficiency, and harvested power. In general, with respect to the operating frequency, it is concluded that the ultrasound-based and inductive-based WPTs have a low operating frequency of less than 50 MHz, whereas the microwave-based WPT works at a higher frequency. Moreover, it can be seen that most of the implanted receiver’s dimension is less than 30 mm for all the WPT-based methods. Furthermore, the HWPT system has a larger receiver size compared to the other methods used. In terms of efficiency, the maximum power transfer efficiency is conducted via inductive-based WPT at 95%, compared to the achievable frequencies of 78%, 50%, and 17% for microwave-based, ultrasound-based, and hybrid WPT, respectively. In general, the inductive coupling tactic is mostly employed for transmission of energy to neuro-stimulators, and the ultrasonic method is used for deep-seated implants. Full article
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Other

Systematic Review
Cyclic Voltammetry in Biological Samples: A Systematic Review of Methods and Techniques Applicable to Clinical Settings
Signals 2021, 2(1), 138-158; https://doi.org/10.3390/signals2010012 - 16 Mar 2021
Cited by 7
Abstract
Oxidative stress plays a pivotal role in the pathogenesis of many diseases, but there is no accurate measurement of oxidative stress or antioxidants that has utility in the clinical setting. Cyclic Voltammetry is an electrochemical technique that has been widely used for analyzing [...] Read more.
Oxidative stress plays a pivotal role in the pathogenesis of many diseases, but there is no accurate measurement of oxidative stress or antioxidants that has utility in the clinical setting. Cyclic Voltammetry is an electrochemical technique that has been widely used for analyzing redox status in industrial and research settings. It has also recently been applied to assess the antioxidant status of in vivo biological samples. This systematic review identified 38 studies that used cyclic voltammetry to determine the change in antioxidant status in humans and animals. It focusses on the methods for sample preparation, processing and storage, experimental setup and techniques used to identify the antioxidants responsible for the voltammetric peaks. The aim is to provide key information to those intending to use cyclic voltammetry to measure antioxidants in biological samples in a clinical setting. Full article
(This article belongs to the Special Issue Biosignals and the Development of Novel Biosensors)
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