Smart Face Masks for Covid-19 Pandemic Management: A Concise Review of Emerging Architectures, Challenges and Future Research Directions

Smart sensing technology has been playing tremendous roles in digital healthcare management over time with great impacts. Lately, smart sensing has awoken the world by the advent of Smart Face Masks (SFM) in the global fight against the deadly Coronavirus (Covid-19) pandemic. In turn, a number of research studies on innovative SFM architectures and designs are emerging. However, there is currently no study that has systematically been conducted to identify and comparatively analyze the emerging architectures and designs of SFMs, their contributions, socio-technological implications, and current challenges. In this paper, we investigate the emerging SFMs in response to Covid-19 pandemic and provide a concise review of their key features and characteristics, design, smart technologies, and architectures. We also highlight and discuss the socio-technological opportunities posed by the use of SFMs and finally present directions for future research. Our findings reveal four key features that can be used to evaluate SFMs to include reusability, self-power generation ability, energy awareness and aerosol filtration efficiency. We discover that SFM has potential for effective use in human tracking, contact tracing, disease detection and diagnosis or in monitoring asymptotic populations in future pandemics. Some SFMs have also been carefully designed to provide comfort and safety when used by patients with other respiratory diseases or comorbidities. However, some identified challenges include standards and quality control, ethical, security and privacy concerns

Computer-Based Test (Cbt) System For University Academic Enterprise Examination

Abstract: As flexible and online learning mediated by ICT becomes more pervasive, there is a growing need for educators to consider modes of assessment using similar tools. Computer Based Test (CBT) is an effective solution for mass education evaluation. Though, a variety of e-assessment approaches and systems have been developed in recent times, yet lack of flexible timing functionality to automatically log-off candidates upon expiration of allotted time, result integrity comprise, stand-alone deployment, lack of flexibility, robustness and scalability as well as human error are major limitations of the existing platforms. In this paper, a web-based online examination system is developed to address these aforementioned drawbacks. The system is designed to facilitate the examination processes and manage challenges surrounding the conduct of examination, auto-submission, automarking and examination result report generation. The conceptual design including the Data Flow Diagram (DFD), the Use Cases and the Entity-Relationship Model (ERM) for the system developed is also presented. The programming tools used for the front-end development of the system are Hypertext Markup Language (HTML) and Microsoft Visual Studio 2012 integrated development environment while Microsoft SQL Server 2008 is used as the database backend. The CBT system was evaluated at the Federal University, Oye-Ekiti, Ekiti State prometric centre. Performance assessment was carried out by two-hundred and fifty (250) volunteer users of the CBT system and the average performance scoring indicate that the system scores high in terms of reliability, robustness and flexibility with easy to use graphical user interface. The volunteers comprise of software developers, students, lecturers and network engineers. The test proved the validity of using this web-based CBT system to evaluate a large mass of students in various institutions of learning across the globe

Review of Technical Approaches to Face Recognition with Varying Pose and Illumination in Unconstrained Scenes

Difficulties inherent in human face recognition from unconstrained and motion characterized scenes are usually accounted for due to varying illumination and pose of subjects in the scenes. However, a number of technical approaches have been developed to manage these nuisance factors to make recognition possible and optimal. In this paper, a review of some technical approaches to face recognition challenged with varying illumination and pose is conducted. The logical soundness of each approach and limitations are investigated while a basis for a unified, more efficient and technically optimal approach is established

HISTOGRAM NORMALIZATION TECHNIQUE FOR PREPROCESSING MAMMOGRAPHIC IMAGES

images requires high computational capabilities. Pre-processing is one of the most important step in the mammogram analysis due to poor captured mammographic image qualities. Pre-processing is basically used to correct and adjust the mammogram image for further study and classification. Many image pre-processing techniques have been developed over the past decades to help radiologists in diagnosing breast cancer. Most studies conducted have proven that a pre-processed image is easier for radiologist to accurately detect breast cancer especially for dense breast. Different types of techniques are available for preprocessing of mammograms, which are used to improve image quality, remove noise, adjust contrast, enhance the image and preserve the edges within the image. This paper acquired 20 digital mammograms from Mammographic Image Analysis Society (MIAS) database and uses Histogram Normalization algorithm for pre-processing of the mammograms. A percentage of 95% was obtained. It was observed that the pre-processed mammographic images displayed breast abnormalities clearer with little or no noise