·         Table of Contents (Volume #5, Issue #1)

·         Cover Page (Volume #5, Issue #1)

·         Editorial (Volume #5, Issue #1)

Paper #1

This article (AETiC V5N1 - Paper #1) has been withdrawn based on the request of the author(s). We apologise for any inconvenience caused.

Paper #2

Fuzzy-based Adaptive Framework for Module Advising Expert System

Obada Alhabashneh

Abstract: In the enrolment process, selecting the right module and lecturer is very important for students. The wrong choice may put them in a situation where they may fail the module. This could lead to a more complicated situation, such as receiving an academic warning, being de-graded, as well as withdrawn from the program or the university. However, module advising is time-consuming and requires knowledge of the university legislation, program requirements, modules available, lecturers, modules, and the student's case. Therefore, the creation of effective and efficient systems and tools to support the process is highly needed. This paper discusses the development of a fuzzy-based framework for the expert recommender system for module advising. The proposed framework builds three main spaces which are: student-space (SS), module-space (MS), and lecturer-space (LS). These spaces are used to estimate the risk level associated with each student, module, and lecturer. The framework then associates each abnormal student case in the students’ grade history with the estimated risk level in the SS, MS, and LS involved in that particular case. The fuzzy-based association-rule learning is then used to extract the dominant rules that classify the consequent situation for each eligible module if it is to be taken by the student for a specific semester. The proposed framework was developed and tested using real-life university data which included student enrolment records and student grade records. A five-fold cross-validation process was used for testing and validating the classifying accuracy of the fuzzy rule base. The fuzzy rule base achieved a 92% accuracy level in classifying the risk level for enrolling on a specific module for a specific student case. However, the average classifying accuracy achieved was 89.2% which is acceptable for this problem domain as it involves human behavior modeling and decision making.

Keywords: Intelligent Academic Advisor; Module Adviser; Expert System; Fuzzy Logic; Fuzzy Rule-based.

Download Full Text

Paper #3

Detection of the Hardcoded Login Information from Socket and String Compare Symbols

Minami Yoda, Shuji Sakuraba, Yuichi Sei, Yasuyuki Tahara and Akihiko Ohsuga

Abstract: Internet of Things (IoT) for smart homes enhances convenience; however, it also introduces the risk of the leakage of private data. TOP10 IoT of OWASP 2018 shows that the first vulnerability is ”Weak, easy to predict, or embedded passwords.” This problem poses a risk because a user can not fix, change, or detect a password if it is embedded in firmware because only the developer of the firmware can control an update. In this study, we propose a lightweight method to detect the hardcoded username and password in IoT devices using a static analysis called Socket Search and String Search to protect from first vulnerability from 2018 OWASP TOP 10 for the IoT device. The hardcoded login information can be obtained by comparing the user input with strcmp or strncmp. Previous studies analyzed the symbols of strcmp or strncmp to detect the hardcoded login information. However, those studies required a lot of time because of the usage of complicated algorithms such as symbolic execution. To develop a lightweight algorithm, we focus on a network function, such as the socket symbol in firmware, because the IoT device is compromised when it is invaded by someone via the Internet. We propose two methods to detect the hardcoded login information: string search and socket search. In string search, the algorithm finds a function that uses the strcmp or strncmp symbol. In socket search, the algorithm finds a function that is referenced by the socket symbol. In this experiment, we measured the ability of our proposed method by searching six firmware in the real world that has a backdoor. We ran three methods: string search, socket search, and whole search to compare the two methods. As a result, all methods found login information from five of six firmware and one unexpected password. Our method reduces the analysis time. The whole search generally takes 38 mins to complete, but our methods finish the search in 4-6 min.

Keywords: Backdoor; Internet of Things; Smart Home; Static Analysis.

Download Full Text

Paper #4

A Customized Floating-point Processor Design for FPGA and ASIC based Thermal Compensation in High-precision Sensing

Muhammad Sajjad, Mohd Zuki Yusoff and Muhammad Ahmed

Abstract: There are many types of sensors which require large dynamic range as well as high accuracy at the same time. Barometric altimeter is an example of such sensors. The signal processing techniques in the sensors are normally implemented using Field Programmable Gate Arrays (FPGAs) or Application-Specific Integrated Circuits (ASICs). The sensing variable in such type of the sensors is unwantedly environment dependent. So, for ensuring accuracy of the sensors this environmental dependency is minimized using the modeling and compensation techniques. In this work we have proposed a digital architecture for a programmable high precision computational unit which can be implemented in the FPGA or ASIC running the sensing algorithm of the sensors. This architecture can be used to implement polynomial compensation and it also supports reading and writing of the corresponding calibration coefficients even after the development of the sensors. Moreover, the architecture is platform independent. The architecture have been simulated for different FPGAs and ASIC and it has fulfilled the speed, accuracy and programmability requirements of the type of the sensors. The architecture has also been implemented and verified in a prototype of the barometric pressure sensor on Spartan-6 FPGA.

Keywords: ASIC; digital signal processing; double-precision floating point unit; FPGA; high precision sensors; thermal compensation; VerilogHDL.

Download Full Text

Paper #5

Experimental Study of Various Parameters during Speed Control of Three-phase Induction Motor Using GPIC and LabVIEW

Adnan Ahmed, Abdul Majeed Shaikh, Muhammad Fawad Shaikh, Shoaib Ahmed Shaikh and Jahangir Badar Soomro

Abstract: Induction motors are widely used from home to industrial applications. Speed of induction motor plays important role, so to control the speed of induction motor various techniques are adopted and one of these techniques is V/F control, which is adopted in this paper. This technique helps to control the speed in open control system in RPM. Moreover, Control is designed in LabVIEW, it is quite helpful to develop the circuit graphically and code is automatically written in the background to run on Field Programmable Gate Array (FPGA). The aim of this research is to study the impacts on diverse parameters during speed control of three phase induction machine with manipulation of GPIC. Solar technology is used as input source to drive the General-Purpose Inverter Controller (GPIC). Apart of this, impacts of modulation index and carrier frequency influencing the active, reactive and apparent power, temperature and power quality and current overshoot is analysed. MATLAB/Simulink and LabVIEW tools are used for simulation and results along with GPIC, Induction motor and solar panel as hardware.

Keywords: GPIC; LabVIEW; Modulation Index; Power Quality; Speed control; V/f control.

Download Full Text