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Hamzah S. AlZu'bi

Bio: Hamzah S. AlZu'bi is an academic researcher from University of Liverpool. The author has contributed to research in topics: Open-loop controller & Bolus (medicine). The author has an hindex of 8, co-authored 18 publications receiving 191 citations. Previous affiliations of Hamzah S. AlZu'bi include University of Jordan & Masdar Institute of Science and Technology.

Papers
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Journal ArticleDOI
TL;DR: An automated method was devised and used for testing the impact of laboratory procedures and efficacy of analgesic drugs in the model species, the zebrafish, and could be adopted across a wide range of biological disciplines.
Abstract: Fish are used in a variety of experimental contexts often in high numbers. To maintain their welfare and ensure valid results during invasive procedures it is vital that we can detect subtle changes in behaviour that may allow us to intervene to provide pain-relief. Therefore, an automated method, the Fish Behaviour Index (FBI), was devised and used for testing the impact of laboratory procedures and efficacy of analgesic drugs in the model species, the zebrafish. Cameras with tracking software were used to visually track and quantify female zebrafish behaviour in real time after a number of laboratory procedures including fin clipping, PIT tagging, and nociceptor excitation via injection of acetic acid subcutaneously. The FBI was derived from activity and distance swum measured before and after these procedures compared with control and sham groups. Further, the efficacy of a range of drugs with analgesic properties to identify efficacy of these agents was explored. Lidocaine (5 mg/L), flunixin (8 mg/L) and morphine (48 mg/L) prevented the associated reduction in activity and distance swum after fin clipping. From an ethical perspective, the FBI represents a significant refinement in the use of zebrafish and could be adopted across a wide range of biological disciplines.

45 citations

09 Aug 2010
TL;DR: In this paper, the authors developed an optimal design for a hybrid solar-wind energy plant, where the variables that are optimized over include the number of photovoltaic modules, the wind turbine height, number of wind turbines, and the turbine rotor diameter.
Abstract: Although solar and wind energy are two of the most viable renewable energy sources, little research has been done on operating both energy sources alongside one another in order to take advantage of their complementary characters. In this paper, we develop an optimal design for a hybrid solar-wind energy plant, where the variables that are optimized over include the number of photovoltaic modules, the wind turbine height, the number of wind turbines, and the turbine rotor diameter, and the goal is to minimize costs. Simulation studies and sensitivity analysis reveal that the hybrid plant is able to exploit the complementary nature of the two energy sources, and deliver energy reliably throughout the year.

29 citations

Proceedings ArticleDOI
16 Dec 2013
TL;DR: A system to detect fatigue based on Electroencephalogram (EEG) signal is proposed, implemented and tested on locally collected dataset for a car simulation driver in different drowsiness levels.
Abstract: Fatigue is a gradual process leads to a slower reaction time. Fatigue is the major cause of road accidents around the globe. This paper proposes, implements and tests a system to detect fatigue based on Electroencephalogram (EEG) signal. The system produces fatigue index which is relevant to the level of subject's drowsiness. The input to the system is EEG signal which is measured by inexpensive single electrode neuro-signal acquisition device. The system was tested on locally collected dataset for a car simulation driver in different drowsiness levels. The system was able to detect the fatigue level for all subjects in different levels of tiredness.

25 citations

Proceedings ArticleDOI
20 Oct 2014
TL;DR: The feasibility of using a group of fatigue symptoms (such as pupil response, gaze patterns, steering reaction and EEG) to build a robust fatigue detection algorithm that can be used in a real-life system for the early prediction and avoidance of fatigue development is investigated.
Abstract: Fatigue is a mental process that grows gradually and affects human reaction time and the consciousness. It is one of the causes of road fatal accidents around the globe. Although it is now generally accepted that fatigue plays an important role in road safety, it is still largely left to individual drivers to manage. The recent research in this area focuses on fatigue detection and the existing systems alert the drivers in severe fatigued stage. These systems use either physiological signs of the fatigue or the behavioural reaction to generate alerts. This research investigates the feasibility of using a group of fatigue symptoms (such as pupil response, gaze patterns, steering reaction and EEG) to build a robust fatigue detection algorithm that can be used in a reallife system for the early prediction and avoidance of fatigue development. Intensive testing and validation stages are required to ensure the reliability and the suitability of the system that should be able to detect fatigue levels at different degrees of tiredness. Moreover, the proposed system predicts subsequent stages of fatigue and generates an approximate behavioural model for each individual driver to enable more personalised and effective intervention.

24 citations

Proceedings ArticleDOI
21 Mar 2016
TL;DR: Adaptive smart fish feeder based on fish behaviours is proposed in this paper in order to minimize the effect of the traditional feeding mechanisms and minimize food waste and maximize the food conversion ratio (FCR).
Abstract: Aquaculture is a growing multi-billion pound industry facing many challenges. Traditional fish feeding mechanism in today's aquaculture farms stands behind a variety of challenges, including fish welfare, fish growth distribution, environmental effect especially in open ocean cage fish farms, and production cost efficiency. Adaptive smart fish feeder based on fish behaviours is proposed in this paper in order to minimize the effect of the traditional feeding mechanisms. The proposed feeding mechanism interacts, recognizes and responses to fish activities. The proposed smart fish feeder feeds fish based on their request regardless the time of the day. The smart fish feeding aims to minimize food waste and maximize the food conversion ratio (FCR). The proposed system is expected to cause uniform fish growth among individuals within the tank as the feeding depends on fish requests. Fish welfare is expected to be enhanced since there is no food competition and food waste is expected to be less making water good quality last for longer. This paper proposes hardware design of the smart feeder and smart software algorithm. Preliminary results will be discussed in this paper.

22 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors provide a detailed analysis of such optimum sizing approaches in the literature that can make significant contributions to wider renewable energy penetration by enhancing the system applicability in terms of economy.
Abstract: Public awareness of the need to reduce global warming and the significant increase in the prices of conventional energy sources have encouraged many countries to provide new energy policies that promote the renewable energy applications. Such renewable energy sources like wind, solar, hydro based energies, etc. are environment friendly and have potential to be more widely used. Combining these renewable energy sources with back-up units to form a hybrid system can provide a more economic, environment friendly and reliable supply of electricity in all load demand conditions compared to single-use of such systems. One of the most important issues in this type of hybrid system is to optimally size the hybrid system components as sufficient enough to meet all load requirements with possible minimum investment and operating costs. There are many studies about the optimization and sizing of hybrid renewable energy systems since the recent popular utilization of renewable energy sources. In this concept, this paper provides a detailed analysis of such optimum sizing approaches in the literature that can make significant contributions to wider renewable energy penetration by enhancing the system applicability in terms of economy.

635 citations

Journal ArticleDOI
TL;DR: In this article, an extensive review on various issues related to Integrated Renewable Energy System (IRES) based power generation is presented, including integration configurations, storage options, sizing methodologies and system control for energy flow management.
Abstract: Uneconomical extension of the grid has led to generation of electric power at the end user facility and has been proved to be cost effective and to an extent efficient. With augmented significance on eco-friendly technologies the use of renewable energy sources such as micro-hydro, wind, solar, biomass and biogas is being explored. This paper presents an extensive review on various issues related to Integrated Renewable Energy System (IRES) based power generation. Issues related to integration configurations, storage options, sizing methodologies and system control for energy flow management are discussed in detail. For stand-alone applications integration of renewable energy sources, performed through DC coupled, AC coupled or hybrid DC–AC coupled configurations, are studied in detail. Based on the requirement of storage duration in isolated areas, storage technology options can be selected for integrated systems. Uncertainties involved in designing an effective IRES based power generation system for isolated areas is accounted due to highly dynamic nature of availability of sources and the demand at site. Different methodologies adopted and reported in literature for sizing of the system components are presented. Distributed control, centralized and hybrid control schemes for energy flow management in IRES have also been discussed.

611 citations

Journal ArticleDOI
TL;DR: The focus of this review is to provide in-depth and comprehensive analysis of data fusion and multiple classifier systems techniques for human activity recognition with emphasis on mobile and wearable devices.

262 citations

Journal ArticleDOI
TL;DR: Overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.
Abstract: Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving and alarm the drivers if they are not concentrating on driving. The relevant features can be extracted from facial expressions such as yawning, eye closure, and head movements for inferring the level of drowsiness. The biological condition of the drivers' body, as well as vehicle behavior, is analyzed for driver drowsiness detection. This paper presents a comprehensive analysis of the existing methods of driver drowsiness detection and presents a detailed analysis of widely used classification techniques in this regard. First, in this paper, we classify the existing techniques into three categories: behavioral, vehicular, and physiological parameters-based techniques. Second, top supervised learning techniques used for drowsiness detection are reviewed. Third, the pros and cons and comparative study of the diverse method are discussed. In addition, the research frameworks are elaborated in diagrams for better understanding. In the end, overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.

174 citations

Journal ArticleDOI
TL;DR: A systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems found that even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsness.
Abstract: Drowsiness is a leading cause of traffic and industrial accidents, costing lives and productivity. Electroencephalography (EEG) signals can reflect awareness and attentiveness, and low-cost consumer EEG headsets are available on the market. The use of these devices as drowsiness detectors could increase the accessibility of safety and productivity-enhancing devices for small businesses and developing countries. We conducted a systemic review of currently available, low-cost, consumer EEG-based drowsiness detection systems. We sought to determine whether consumer EEG headsets could be reliably utilized as rudimentary drowsiness detection systems. We included documented cases describing successful drowsiness detection using consumer EEG-based devices, including the Neurosky MindWave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI. Of 46 relevant studies, ~27 reported an accuracy score. The lowest of these was the Neurosky Mindwave, with a minimum of 31%. The second lowest accuracy reported was 79.4% with an OpenBCI study. In many cases, algorithmic optimization remains necessary. Different methods for accuracy calculation, system calibration, and different definitions of drowsiness made direct comparisons problematic. However, even basic features, such as the power spectra of EEG bands, were able to consistently detect drowsiness. Each specific device has its own capabilities, tradeoffs, and limitations. Widely used spectral features can achieve successful drowsiness detection, even with low-cost consumer devices; however, reliability issues must still be addressed in an occupational context.

86 citations