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Huang X, Kachole S, Ayyad A, Naeini FB, Makris D, Zweiri Y. A neuromorphic dataset for tabletop object segmentation in indoor cluttered environment. Sci Data 2024; 11:127. [PMID: 38272894 PMCID: PMC10810887 DOI: 10.1038/s41597-024-02920-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 01/05/2024] [Indexed: 01/27/2024] Open
Abstract
Event-based cameras are commonly leveraged to mitigate issues such as motion blur, low dynamic range, and limited time sampling, which plague conventional cameras. However, a lack of dedicated event-based datasets for benchmarking segmentation algorithms, especially those offering critical depth information for occluded scenes, has been observed. In response, this paper introduces a novel Event-based Segmentation Dataset (ESD), a high-quality event 3D spatial-temporal dataset designed for indoor object segmentation within cluttered environments. ESD encompasses 145 sequences featuring 14,166 manually annotated RGB frames, along with a substantial event count of 21.88 million and 20.80 million events from two stereo-configured event-based cameras. Notably, this densely annotated 3D spatial-temporal event-based segmentation benchmark for tabletop objects represents a pioneering initiative, providing event-wise depth, and annotated instance labels, in addition to corresponding RGBD frames. By releasing ESD, our aim is to offer the research community a challenging segmentation benchmark of exceptional quality.
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Affiliation(s)
- Xiaoqian Huang
- Advanced Research and Innovation Center (ARIC), Khalifa University, Abu Dhabi, UAE
- Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University, Abu Dhabi, UAE
| | - Sanket Kachole
- School of Computer Science and Mathematics, Kingston University, London, UK
| | - Abdulla Ayyad
- Advanced Research and Innovation Center (ARIC), Khalifa University, Abu Dhabi, UAE
| | | | - Dimitrios Makris
- School of Computer Science and Mathematics, Kingston University, London, UK
| | - Yahya Zweiri
- Advanced Research and Innovation Center (ARIC), Khalifa University, Abu Dhabi, UAE.
- Department of Aerospace Engineering, Khalifa University, Abu Dhabi, UAE.
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Alansari M, Abdul Hay O, Alansari S, Javed S, Shoufan A, Zweiri Y, Werghi N. Drone-Person Tracking in Uniform Appearance Crowd: A New Dataset. Sci Data 2024; 11:15. [PMID: 38167525 PMCID: PMC10762134 DOI: 10.1038/s41597-023-02810-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
Drone-person tracking in uniform appearance crowds poses unique challenges due to the difficulty in distinguishing individuals with similar attire and multi-scale variations. To address this issue and facilitate the development of effective tracking algorithms, we present a novel dataset named D-PTUAC (Drone-Person Tracking in Uniform Appearance Crowd). The dataset comprises 138 sequences comprising over 121 K frames, each manually annotated with bounding boxes and attributes. During dataset creation, we carefully consider 18 challenging attributes encompassing a wide range of viewpoints and scene complexities. These attributes are annotated to facilitate the analysis of performance based on specific attributes. Extensive experiments are conducted using 44 state-of-the-art (SOTA) trackers, and the performance gap between the visual object trackers on existing benchmarks compared to our proposed dataset demonstrate the need for a dedicated end-to-end aerial visual object tracker that accounts the inherent properties of aerial environment.
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Affiliation(s)
- Mohamad Alansari
- Department of Electrical Engineering and Computer Science, Abu Dhabi, 00000, UAE.
| | - Oussama Abdul Hay
- Department of Aerospace Engineering, Abu Dhabi, 00000, UAE
- Advanced Research and Innovation Center (ARIC), Abu Dhabi, 00000, UAE
| | - Sara Alansari
- Department of Electrical Engineering and Computer Science, Abu Dhabi, 00000, UAE
| | - Sajid Javed
- Department of Electrical Engineering and Computer Science, Abu Dhabi, 00000, UAE
- Center for Autonomous Robotic Systems, Abu Dhabi, 00000, UAE
| | - Abdulhadi Shoufan
- Department of Electrical Engineering and Computer Science, Abu Dhabi, 00000, UAE
- Center for Cyber-Physical Systems, Abu Dhabi, 00000, UAE
| | - Yahya Zweiri
- Department of Aerospace Engineering, Abu Dhabi, 00000, UAE
- Advanced Research and Innovation Center (ARIC), Abu Dhabi, 00000, UAE
| | - Naoufel Werghi
- Department of Electrical Engineering and Computer Science, Abu Dhabi, 00000, UAE
- Center for Autonomous Robotic Systems, Abu Dhabi, 00000, UAE
- Center for Cyber-Physical Systems, Abu Dhabi, 00000, UAE
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Alfalahi H, Dias SB, Khandoker AH, Chaudhuri KR, Hadjileontiadis LJ. A scoping review of neurodegenerative manifestations in explainable digital phenotyping. NPJ Parkinsons Dis 2023; 9:49. [PMID: 36997573 PMCID: PMC10063633 DOI: 10.1038/s41531-023-00494-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, University of Lisbon, Lisbon, Portugal
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
| | - Kallol Ray Chaudhuri
- Parkinson Foundation, International Center of Excellence, King's College London, Denmark Hills, London, UK
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London, UK
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Abstract
Here we demonstrate, a facile in-situ strategy for the synthesis of environmentally benign and scalable graphene sand hybrid using date syrup as a sustainable carbon source through pyrolysis at 750 °C. Raman and SEM images revealed that the as-prepared date syrup-based graphene sand hybrid (D-GSH) had imperfections with macroporous 2-D graphene sheet-like structures stacked on the inorganic sand support. The applicability of the D-GSH for decontaminating the water from cationic (Methyl Violet, MV) and anionic (Congo Red, CR) dye and heavy metals (Pb2+ and Cd2+) was tested. Batch experiments demonstrated that D-GSH showcased exceptional capability for both dye and heavy metals removal with fast adsorption following pseudo-second-order kinetics. The adsorption capacities for MV, Pb2+, and Cd2+ were respectively 2564, 781 and 793 mg/g at 25 °C, the highest capacity graphene-based adsorbent reported in the literature to date. In addition, D-GSH also exhibited high adsorption capacity for anionic dye, CR (333 mg g-1) and good recyclability (3 cycles) for all the contaminants. The thermodynamic studies further confirmed that the adsorption of all contaminants was thermodynamically feasible, spontaneous and endothermic with ∆H° of 48.38, 89.10, 16.89 and 14.73 kJ/mol for MV, CR, Pb2+ and Cd2+, respectively. Thus, utilization of a simple one-step strategy to produce graphenic sand hybrid using date syrup helped in developing a cost-effective and environmentally friendly dye and heavy metal scavenger that can be used as a one-step solution for water decontamination.
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Affiliation(s)
- Shaihroz Khan
- Department of Chemical Engineering, Khalifa University - SAN Campus, PO Box: 127788, Abu Dhabi, United Arab Emirates
| | - Anjali Achazhiyath Edathil
- Department of Chemical Engineering, Khalifa University - SAN Campus, PO Box: 127788, Abu Dhabi, United Arab Emirates.
| | - Fawzi Banat
- Department of Chemical Engineering, Khalifa University - SAN Campus, PO Box: 127788, Abu Dhabi, United Arab Emirates.
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Janeiro R, Flores R, Viegas J. Silicon photonics waveguide array sensor for selective detection of VOCs at room temperature. Sci Rep 2019; 9:17099. [PMID: 31745099 PMCID: PMC6863817 DOI: 10.1038/s41598-019-52264-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 10/10/2019] [Indexed: 11/30/2022] Open
Abstract
We report on the fabrication and characterization of a volatile organic compound sensor architecture addressing common drawbacks of photonic integrated sensors such as reusability and specificity. The proposed sensor, built on a silicon-on-insulator platform and based on arrayed waveguide interference, has a chemically selective polydimethylsiloxane polymer cladding, which encapsulates the waveguides and provides an expandable and permeable low refractive index material. This cladding material acts as the chemical transducer element, changing its optical properties when in contact with specific volatile organic compounds, whose presence in the context of environmental and public health protection is important to monitor. The sensor operates at room temperature and its selectivity was confirmed by multiple tests with water, toluene, chlorobenzene, and hexane, through which the sturdiness of the sensor was verified. A maximum spectral shift of about 22.8 nm was measured under testing with chlorobenzene, at a central wavelength of 1566.7 nm. In addition, a sensitivity of 234.8 pm/% was obtained for chlorobenzene mass percent concentrations, with a limit of detection of 0.24%m/m. The thermal sensitivity of the sensor has been found to be 0.9 nm/°C.
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Affiliation(s)
- Ricardo Janeiro
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Masdar City Campus, Abu Dhabi, United Arab Emirates.
| | - Raquel Flores
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Masdar City Campus, Abu Dhabi, United Arab Emirates
| | - Jaime Viegas
- Department of Electrical Engineering and Computer Science, Khalifa University of Science and Technology, Masdar City Campus, Abu Dhabi, United Arab Emirates
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Abunahla H, Mohammad B, Alazzam A, Jaoude MA, Al-Qutayri M, Abdul Hadi S, Al-Sarawi SF. MOMSense: Metal-Oxide-Metal Elementary Glucose Sensor. Sci Rep 2019; 9:5524. [PMID: 30940837 PMCID: PMC6445282 DOI: 10.1038/s41598-019-41892-w] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 03/18/2019] [Indexed: 02/06/2023] Open
Abstract
In this paper, we present a novel Pt/CuO/Pt metal-oxide-metal (MOM) glucose sensor. The devices are fabricated using a simple, low-cost standard photolithography process. The unique planar structure of the device provides a large electrochemically active surface area, which acts as a nonenzymatic reservoir for glucose oxidation. The sensor has a linear sensing range between 2.2 mM and 10 mM of glucose concentration, which covers the blood glucose levels for an adult human. The distinguishing property of this sensor is its ability to measure glucose at neutral pH conditions (i.e. pH = 7). Furthermore, the dilution step commonly needed for CuO-based nonenzymatic electrochemical sensors to achieve an alkaline medium, which is essential to perform redox reactions in the absence of glucose oxidase, is eliminated, resulting in a lower-cost and more compact device.
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Affiliation(s)
- Heba Abunahla
- Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Baker Mohammad
- Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE.
| | - Anas Alazzam
- Department of Mechanical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Maguy Abi Jaoude
- Department of Chemistry, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Mahmoud Al-Qutayri
- Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Sabina Abdul Hadi
- Department of Electrical and Computer Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
| | - Said F Al-Sarawi
- Centre for Biomedical Engineering, School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
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