@article{DBLP:journals/nca/MostafaAAD20,

title = {Design of sEMG-based clench force estimator in FPGA using artificial 

 neural networks},

author = {Sheikh Shanawaz Mostafa and Md. Abdul Awal and Mohiuddin Ahmad and Fernando Morgado Dias},

year  = {2020},

date = {2020-01-01},

journal = {Neural Comput. Appl.},

volume = {32},

number = {20},

pages = {15813--15823},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/nca/MostafaMJDR20,

title = {SC3: self-configuring classifier combination for obstructive sleep 

 apnea},

author = {Sheikh Shanawaz Mostafa and Fábio Mendonça and Gabriel Juliá-Serdá and Fernando Morgado Dias and Antonio G. Ravelo-García},

year  = {2020},

date = {2020-01-01},

journal = {Neural Comput. Appl.},

volume = {32},

number = {24},

pages = {17825--17841},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/sensors/MendoncaMDR20,

title = {An Oximetry Based Wireless Device for Sleep Apnea Detection},

author = {Fábio Mendonça and Sheikh Shanawaz Mostafa and Fernando Morgado Dias and Antonio G. Ravelo-García},

year  = {2020},

date = {2020-01-01},

journal = {Sensors},

volume = {20},

number = {3},

pages = {888},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/access/MendoncaMDRP19,

title = {A Review of Approaches for Sleep Quality Analysis},

author = {Fábio Mendonça and Sheikh Shanawaz Mostafa and Fernando Morgado Dias and Antonio G. Ravelo-García and Thomas Penzel},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Access},

volume = {7},

pages = {24527--24546},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/entropy/MendoncaMDR19,

title = {A Portable Wireless Device for Cyclic Alternating Pattern Estimation 

 from an EEG Monopolar Derivation},

author = {Fábio Mendonça and Sheikh Shanawaz Mostafa and Fernando Morgado Dias and Antonio G. Ravelo-García},

year  = {2019},

date = {2019-01-01},

journal = {Entropy},

volume = {21},

number = {12},

pages = {1203},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/sensors/MostafaMRD19,

title = {A Systematic Review of Detecting Sleep Apnea Using Deep Learning},

author = {Sheikh Shanawaz Mostafa and Fábio Mendonça and Antonio G. Ravelo-García and Fernando Morgado Dias},

year  = {2019},

date = {2019-01-01},

journal = {Sensors},

volume = {19},

number = {22},

pages = {4934},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/titb/MendoncaMRDP19,

title = {A Review of Obstructive Sleep Apnea Detection Approaches},

author = {Fábio Mendonça and Sheikh Shanawaz Mostafa and Antonio G. Ravelo-García and Fernando Morgado Dias and Thomas Penzel},

year  = {2019},

date = {2019-01-01},

journal = {IEEE J. Biomed. Health Informatics},

volume = {23},

number = {2},

pages = {825--837},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10645,

title = {Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory},

author = {Dario Baptista and Sheikh Mostafa and Lucas Pereira and Leonel Sousa and Dias F. Morgado},

url = {http://www.mdpi.com/1996-1073/11/9/2460},

doi = {10.3390/en11092460},

issn = {1996-1073},

year  = {2018},

date = {2018-09-01},

journal = {Energies},

volume = {11},

pages = {2460},

abstract = {<p>Specific information about types of appliances and their use in a specific time window could help determining in details the electrical energy consumption information. However, conventional main power meters fail to provide any specific information. One of the best ways to solve these problems is through non-intrusive load monitoring, which is cheaper and easier to implement than other methods. However, developing a classifier for deducing what kind of appliances are used at home is a difficult assignment, because the system should identify the appliance as fast as possible with a higher degree of certainty. To achieve all these requirements, a convolution neural network implemented on hardware was used to identify the appliance through the voltage and current (V-I) trajectory. For the implementation on hardware, a field programmable gate array (FPGA) was used to exploit processing parallelism in order to achieve optimal performance. To validate the design, a publicly available Plug Load Appliance Identification Dataset (PLAID), constituted by 11 different appliances, has been used. The overall average F-score achieved using this classifier is 78.16% for the PLAID 1 dataset. The convolution neural network implemented on hardware has a processing time of approximately 5.7 ms and a power consumption of 1.868 W.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/computing/MendoncaMDNJR18,

title = {A portable wireless device based on oximetry for sleep apnea detection},

author = {Fábio Mendonça and Sheikh Shanawaz Mostafa and Fernando Morgado Dias and Juan L. Navarro-Mesa and Gabriel Juliá-Serdá and Antonio G. Ravelo-García},

year  = {2018},

date = {2018-01-01},

journal = {Computing},

volume = {100},

number = {11},

pages = {1203--1219},

keywords = {},

pubstate = {published},

tppubtype = {article}

}