This data set is the data of the downlink signals of the high-orbit maritime communication satellite.

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Datasets of confocal microscopy images of cardiomyocytes aimed at development of image recognition systems. Images of live and healthy cardiomyocytes with fluorescently stained sarcolemma were assigned into 5 classes according to their development stages. The least developed cardiomyocytes were considered to be at stage 1 (class 1) while the most developed ones were assigned stage 5 (class 5). All other images belong to a class 0.

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This zip folder contains audio files for experiments reported in "Acoustic Feedback Cancelation in Hearing Aids Using Dual Adaptive Filtering and Gain-Controlled Probe Signal", submitted for publication in IEEE Access.

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Electroencephalography (EEG) signal data was collected from twelve healthy subjects with no known musculoskeletal or neurological deficits (mean age 25.5 ± 3.7, 11 male, 1 female, 1 left handed, 11 right handed) using an EGI Geodesics© Hydrocel EEG 64-Channel spongeless sensor net. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the University of Wisconsin-Milwaukee (17.352).

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For research purposes, the ECG signals were obtained from the PhysioNet service (http://www.physionet.org) from the MIT-BIH Arrhythmia database. The created database with ECG signals is described below. 1) The ECG signals were from 29 patients: 15 female (age: 23-89) and 14 male (age: 32-89). 2) The ECG signals contained 17 classes: normal sinus rhythm, pacemaker rhythm, and 15 types of cardiac dysfunctions (for each of which at least 10 signal fragments were collected).

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Visual tracking methods have achieved a successful development in recent years. Especially the Discriminative Correlation Filter (DCF) based methods have significantly advanced the state-of-the-art in tracking. The advancement in DCF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, it would come to some troubles if the tracker learns the samples indiscriminately.

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The datasets are composed of the experimental results of my paper submitted to TMM on HELEN and LFW datasets.

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This work presents a novel Anti-Islanding (AI) protection of Photovoltaic (PV) Systems based on monitoring the dc-link voltage of the PV inverter. A PV System equipped with AI protection like frequency relays, a rate of change of frequency (ROCOF) relay, and respectively the proposed dc-link voltage relay is simulated under the conditions of islanding and the detection time for all these AI techniques are compared. The study shows under which conditions our proposed dc-link voltage AI relay is the most efficient.

Instructions: 

1. Open the "Banu_PVarray_Grid_det_AI_UPEC2014.slx" file with Matlab R2014a or a newer Matlab release. 2. Open the "Relay Protection Bus B20 (20 kV)" block to see the Anti-Islanding Protection Scheme, including the new "DC-Link Voltage Protection" Method and its settings.

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This work presents a Matlab/Simulink study on anti-islanding detection algorithms for a 100kW Grid-Connected Photovoltaic (PV) Array. The main focus is on the islanding phenomenon that occurs at the Point of Common Coupling (PCC) between Grid-Connected PV System and the rest of the electric power system (EPS) during various grid fault conditions. The Grid-Connected PV System is simulated under the conditions of islanding, and anti-islanding (AI) relay reaction times are measured through the simulation.

Instructions: 

1. Open the "Fault3_50Hz_Banu_PVarray_Grid_IncCondReg_det_AI_2013_.slx" file with Matlab R2013b or a newer release to simulate the 100kW Grid-Connected PV Array (Detailed Model) with Anti-Islanding Relays. 2. To see the Anti-Islanding Protection Relays and its settings, open the "Relay Protection Bus B20 (20kV)" block.

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