Dataset of IoT-based energy and environmental parameters in a smart building infrastructure.

This data article presents a detailed dataset collected as part of the University of Sharjah's (UoS) strategic initiative towards transforming into a smart campus by 2030. Collected from January 1st, 2024, to June 20nd, 2024, from key facilities including offices, labs, and communal spaces, the dataset encompasses precise energy consumption metrics and environmental conditions monitored via Internet of Things (IoT) sensors. It features appliance-specific power data (watts, voltage, kWh) alongside environmental parameters such as temperature, humidity, and occupancy rates.

EEG dataset for energy data visualizations.

User behavior plays a substantial role in shaping household energy use. Nevertheless, the methodologies employed by researchers to examine user behavior exhibit certain limitations in terms of their reach. The present article introduces an openly accessible collection of electroencephalography (EEG) recordings, comprising EEG data collected from individuals who were subjected to energy data visualizations.

Novel domestic building energy consumption dataset: 1D timeseries and 2D Gramian Angular Fields representation.

This data article describes a dataset collected in 2022 in a domestic household in the UK. The data provides appliance-level power consumption data and ambient environmental conditions as a timeseries and as a collection of 2D images created using Gramian Angular Fields (GAF). The importance of the dataset lies in (a) providing the research community with a dataset that combines appliance-level data coupled with important contextual information for the surrounding environment; (b) presents energy data summaries as 2D images to help obtain novel insights using data visualization and Machine Learning (ML).

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives.

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.

Deep learning with multiresolution handcrafted features for brain MRI segmentation.

The segmentation of magnetic resonance (MR) images is a crucial task for creating pseudo computed tomography (CT) images which are used to achieve positron emission tomography (PET) attenuation correction. One of the main challenges of creating pseudo CT images is the difficulty to obtain an accurate segmentation of the bone tissue in brain MR images. Deep convolutional neural networks (CNNs) have been widely and efficiently applied to perform MR image segmentation.

Lightweight Gramian Angular Field classification for edge internet of energy applications.

Unlabelled: With adverse industrial effects on the global landscape, climate change is imploring the global economy to adopt sustainable solutions. The ongoing evolution of energy efficiency targets massive data collection and Artificial Intelligence (AI) for big data analytics. Besides, emerging on the Internet of Energy (IoE) paradigm, edge computing is playing a rising role in liberating private data from cloud centralization.

Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study.

Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption.

A novel gateway-based solution for remote elderly monitoring.

Internet of Things (IoT) technologies have been applied to various fields such as manufacturing, automobile industry and healthcare. IoT-based healthcare has a significant impact on real-time remote monitoring of patients' health and consequently improving treatments and reducing healthcare costs. In fact, IoT has made healthcare more reliable, efficient, and accessible.

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation.

Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image.

Lattice-Boltzmann interactive blood flow simulation pipeline.

Purpose: Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges.

Moving object tracking in clinical scenarios: application to cardiac surgery and cerebral aneurysm clipping.

Background And Objectives: Surgical procedures such as laparoscopic and robotic surgeries are popular since they are invasive in nature and use miniaturized surgical instruments for small incisions. Tracking of the instruments (graspers, needle drivers) and field of view from the stereoscopic camera during surgery could further help the surgeons to remain focussed and reduce the probability of committing any mistakes. Tracking is usually preferred in computerized video surveillance, traffic monitoring, military surveillance system, and vehicle navigation.

A skills acquisition study on ECMOjo: a screen-based simulator for extracorporeal membrane oxygenation.

Background: Extracorporeal membrane oxygenation relies heavily on didactic teaching, emphasizing on essential cognitive skills, but overlooking core behavioral skills such as leadership and communication. Therefore, simulation-based training has been adopted to instill clinical knowledge through immersive experiences. Despite simulation-based training's effectiveness, training opportunities are lessened due to high costs.

Extracorporeal membrane oxygenation simulation-based training: methods, drawbacks and a novel solution.

Introduction: Patients under the error-prone and complication-burdened extracorporeal membrane oxygenation (ECMO) are looked after by a highly trained, multidisciplinary team. Simulation-based training (SBT) affords ECMO centers the opportunity to equip practitioners with the technical dexterity required to manage emergencies. The aim of this article is to review ECMO SBT activities and technology followed by a novel solution to current challenges.

Using thermochromism to simulate blood oxygenation in extracorporeal membrane oxygenation.

Introduction: Extracorporeal membrane oxygenation (ECMO) training programs employ real ECMO components, causing them to be extremely expensive while offering little realism in terms of blood oxygenation and pressure. To overcome those limitations, we are developing a standalone modular ECMO simulator that reproduces ECMO's visual, audio and haptic cues using affordable mechanisms. We present a central component of this simulator, capable of visually reproducing blood oxygenation color change using thermochromism.

Addressing the challenges of ECMO simulation.

Introduction/aim: The patient's condition and high-risk nature of extracorporeal membrane oxygenation (ECMO) therapy force clinical services to ensure clinicians are properly trained and always ready to deal effectively with critical situations. Simulation-based education (SBE), from the simplest approaches to the most immersive modalities, helps promote optimum individual and team performance. The risks of SBE are negative learning, inauthenticity in learning and over-reliance on the participants' suspension of disbelief.

A stochastic modelling framework for the reconstruction of cardiovascular signals.

This paper presents a common stochastic modelling framework for physiological signals which allows patient simulation following a synthesis-by-analysis approach. Within this framework, we propose a general model-based methodology able to reconstruct missing or artifacted signal intervals in cardiovascular monitoring applications. The proposed model consists of independent stages which provide high flexibility to incorporate signals of different nature in terms of shape, cross-correlation and variability.

Continuous-time ΣΔ ADC with implicit variable gain amplifier for CMOS image sensor.

This paper presents a column-parallel continuous-time sigma delta (CTSD) ADC for mega-pixel resolution CMOS image sensor (CIS). The sigma delta modulator is implemented with a 2nd order resistor/capacitor-based loop filter. The first integrator uses a conventional operational transconductance amplifier (OTA), for the concern of a high power noise rejection.

Two-step single slope/SAR ADC with error correction for CMOS image sensor.

Conventional two-step ADC for CMOS image sensor requires full resolution noise performance in the first stage single slope ADC, leading to high power consumption and large chip area. This paper presents an 11-bit two-step single slope/successive approximation register (SAR) ADC scheme for CMOS image sensor applications. The first stage single slope ADC generates a 3-bit data and 1 redundant bit.

Artificial Neural Network-Based System for PET Volume Segmentation.

Tumour detection, classification, and quantification in positron emission tomography (PET) imaging at early stage of disease are important issues for clinical diagnosis, assessment of response to treatment, and radiotherapy planning. Many techniques have been proposed for segmenting medical imaging data; however, some of the approaches have poor performance, large inaccuracy, and require substantial computation time for analysing large medical volumes. Artificial intelligence (AI) approaches can provide improved accuracy and save decent amount of time.

Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model.

The widespread application of positron emission tomography (PET) in clinical oncology has driven this imaging technology into a number of new research and clinical arenas. Increasing numbers of patient scans have led to an urgent need for efficient data handling and the development of new image analysis techniques to aid clinicians in the diagnosis of disease and planning of treatment. Automatic quantitative assessment of metabolic PET data is attractive and will certainly revolutionize the practice of functional imaging since it can lower variability across institutions and may enhance the consistency of image interpretation independent of reader experience.