Service Prototyping Lab Report - 2018 (Y3)

The annual activity report of the Service Prototyping Lab at Zurich University of Applied Sciences. Research trends and initiatives, research projects, transfer to education and local industry, academic community involvement, qualification and scientific development over the period of one year are among the covered topics

DESIGN AND IMPLEMENTATION OF A SMART HOME AUTOMATION SYSTEM

Objective: Due to the rapid development of various technologies and communication devices, the use of the Internet of Things (IoT) for automation has become increasingly common in non-industrial environments. It has integrated well into our day-to-day activities, leading us toward the use of smart home technology. Smart home systems are intelligent system that provide control to home appliances and also security systems. There are limited numbers of intelligent systems that address multiple aspects of the home automation, such as appliances control, security bridge detection, and reducing energy consumption and cost simultaneously. Hence, this research developed a system that solves these problems with an intelligent home automation system. Methods: The designed system was based on Arduino ATMEGA328P microcontroller, MQ2 sensor for gas detection, passive infrared (IR) sensor for motion detection, and flame sensor to detect fire outbreak. Arduino ATMEGA328P was used as a central controlling unit that controls the flow of system operations to achieved smart home automation system. Results: The system sends audible alarms through a buzzer to draw the user’s immediate attention. It also sends a warning message to the user’s mobile phone through the global system for mobile communication module. Conclusion: The system achieved a precision rate of 94.44% and provided a cost-effective platform for interconnecting a variety of devices and various sensors in a home through the IoT

SSO Based Fingerprint Authentication of Cloud Services for Organizations

Access to a pool of programmable resources, such as storage space, applications, services, and on-demand networks, is made possible by cloud computing technology. Involving the cloud with the organization reduces its efforts to meet the needs of its customers. The Single Sign-On (SSO) method, which enables users to access various application services using a single user credential, is one of the key benefits of cloud computing. There are numerous problems and difficulties with cloud computing that need to be highlighted. However, protecting user agent privacy against security assaults is far more challenging. To combat security and privacy assaults, this study suggests SSO-based biometric authentication architecture for cloud computing services. Since end devices are computationally inefficient for processing user information during authentication, biometric authentication is effective for resources controlled by end devices at the time of accessing cloud services. As a result, the proposed design minimizes security attacks in cloud computing. An innovative strategy that establishes a one-to-one interaction between the user agent and the service provider is also included in the suggested design. In this case, user agents can use their fingerprint to access various cloud application services and seek registration. The highlights of the suggested architecture have been offered based on comparison analysis with a number of existing architectures

Framework for comprehensive enhancement of brain tumor images with single-window operation

Usage of grayscale format of radiological images is proportionately more as compared to that of colored one. This format of medical image suffers from all the possibility of improper clinical inference which will lead to error-prone analysis in further usage of such images in disease detection or classification. Therefore, we present a framework that offers single-window operation with a set of image enhancing algorithm meant for further optimizing the visuality of medical images. The framework performs preliminary pre-processing operation followed by implication of linear and non-linear filter and multi-level image enhancement processes. The significant contribution of this study is that it offers a comprehensive mechanism to implement the various enhancement schemes in highly discrete way that offers potential flexibility to physical in order to draw clinical conclusion about the disease being monitored. The proposed system takes the case study of brain tumor to implement to testify the framework

Lightweight Security Mechanism to Mitigate Active Attacks in a Mobile Ad-hoc Network

Mobile Ad hoc Network (MANET) is a type of Ad hoc network. General properties of MANET open the network to various security threats. Network layer-based Active attacks are widespread and destructive. Available security solutions contain complex calculations. Therefore, the objective of this research is to propose a lightweight security mechanism to enhance the security of data communications between source and destination nodes in a MANET from network layer-based active attack. Blackhole is used as a network layer-based Active attack. The network performance is evaluated using Packet Delivery Ratio (PDR), Average End-to-End Delay (AEED), Throughput, and Simulation Processing Time at Intermediate Nodes (SPTIN). The controller network was used to compare the performance of each network. During the experiment due to the impact of the blackhole attack, compared to the controller network, the PDR was found to be 0.28%, AEED was infinity and Throughput was 0.33%. The performance of the proposed security mechanism was compared with that of the controller network, and the values of PDR, AEED, Throughput, and SPTIN were found to be 98.0825%, 100.9346%, 99.9988%, and 96.5660%, respectively. The data packet delivery ratio was 100.00% compared to that of the controller network. The network that was affected by a blackhole attack showed a higher amount of ADDR than the controller network and the lowest amount of PDR. The network that was affected by the blackhole showed underperformance compared to the controller network. The proposed security mechanism performs well in PDR, AEED, and Throughput compared to the controller network. The AEED and SPTIN values prove that the proposed solution is free from complex calculations. The scope of the solution can be expanded into a lightweight Intruder Detection System to handle different types of security attacks in MANETs

Wavelet Transform Analysis to Applications in Electric Power Systems

The wavelet transform has received great importance in the last years on the power system analysis because the multi-resolution analysis presents proprieties good for the transient signal analysis. This chapter presents a review on main application of wavelet transform in electric power systems. The study areas have been classified as power system protection, power quality disturbances, power system transient, partial discharge, load forecasting, faults detection, and power system measurement. The areas in which more works have been developed are the power quality and protections field, where both cover 51% of the articles analyzed

Evidence and Promises of AI Predictions to Understand Student Approaches to Math Learning in Abu Dhabi K12 Public Schools

Transforming the education system and building highly skilled human capital for a sustainable and competitive knowledge economy have been on the UAE’s top policy agendas for the last decade. However, in the UAE, students’ math performance on the Program for International Student Assessment (PISA) has not been promising. To improve the quality of schooling, a series of malleable predictive factors including the contributions of self-system, metacognitive skills, and instructional language skills are selected and categorized under student approaches to math learning. These factors are hypothesized as both predictors and outcomes of K12 schooling. Through the analysis using machine learning technique, XGBoost, a latent relationship between student approaches to math learning and math diagnostic test performance is uncovered and discussed for students from Grade 5 to Grade 9 in Abu Dhabi public schools. This article details how the analysis results are applied for student behavior and performance prediction, precise diagnosis, and targeted intervention design possibilities. The main purpose of this study is to diagnose challenges that hinder student math learning in Abu Dhabi public schools, uncover R&D initiatives in AI-driven prediction and EdTech interventions to bridge learning gaps, and to counsel on national education policy refinement

Estimating Land Subsidence and Gravimetric Anomaly Induced by Aquifer Overexploitation in the Chandigarh Tri-City Region, India by Coupling Remote Sensing with a Deep Learning Neural Network Model

This study utilizes surface displacement data from Persistent Scatterer SAR Interferometry (PSInSAR) of Sentinel-1 satellite and groundwater storage change data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to understand land subsidence in the Chandigarh tri-city region. The satellite datasets are used along with the groundwater level data obtained from wells over the study area. Since the GRACE data are available at a much coarser spatial resolution of 1o by 1o, challenges remain in correlating the dataset with PSInSAR displacement that has been multi-looked at 14 m by 14 m resolution. Therefore, multiple sources of data (i.e., the monthly average of GRACE data, groundwater storage change and monthly average PSInSAR displacement per pixel, and interpolated groundwater level data from wells for 2017 to 2022) have been deployed into a deep learning multi-layer perceptron (DLMLP) model to estimate the groundwater storage change at the urban level. This has an indirect downscaling method that is carried out successfully using the DLMLP model for the estimation of groundwater storage changes at the urban level, which is usually complicated by applying direct downscaling methods on the GRACE data. Thus, the DLMLP model developed here is a distinctive approach considered for estimating the changes in groundwater storage using PSInSAR displacement, groundwater data from wells, and GRACE data. The DLMLP model gives an R2-statistics value of 0.91 and 0.89 in the training and testing phases, respectively, and has a mean absolute error (MAE) of 1.23 and root mean square error (RMSE) of 0.87

Security and accuracy of fingerprint-based biometrics: A review

Biometric systems are increasingly replacing traditional password- and token-based authentication systems. Security and recognition accuracy are the two most important aspects to consider in designing a biometric system. In this paper, a comprehensive review is presented to shed light on the latest developments in the study of fingerprint-based biometrics covering these two aspects with a view to improving system security and recognition accuracy. Based on a thorough analysis and discussion, limitations of existing research work are outlined and suggestions for future work are provided. It is shown in the paper that researchers continue to face challenges in tackling the two most critical attacks to biometric systems, namely, attacks to the user interface and template databases. How to design proper countermeasures to thwart these attacks, thereby providing strong security and yet at the same time maintaining high recognition accuracy, is a hot research topic currently, as well as in the foreseeable future. Moreover, recognition accuracy under non-ideal conditions is more likely to be unsatisfactory and thus needs particular attention in biometric system design. Related challenges and current research trends are also outlined in this paper