Identification of a quadcopter autopilot system via Box–Jenkins structure

This paper presents a method to precisely model a four rotor unmanned aerial vehicle, widely known as quadcopter autopilot system. Common system identification methods limit quadcopter models into first or second order systems, and do not count for noise characteristics. This leads to poor prediction accuracy of its longitudinal and lateral motion dynamics that ultimately affects the aircraft stabilization during flight and landing. To improve the quality of the estimated models, we utilized a statistically suitable discrete-time linear Box–Jenkins structure to model the plant and noise characteristics of the horizontal subsystems of a quadcopter autopilot system. The models were estimated using flight data acquired when the system were provided with pseudo-random binary sequence input. In this proposed method, by employing the prediction error method and least squares approach, the aircraft dynamics could be modeled up until the fifth order. The normalized root mean square fitness value showed that the predicted model output matches the experimental flight data by 94.72% in the one-step-ahead prediction test, and 84.52% in the infinite-step-ahead prediction test. These prediction results demonstrated an improvement of 52.8% when compared with a first and second order model structures proposed in previous works for the same quadcopter model. The output from this research works confirmed the effectiveness of the proposed method to adequately capture the autopilot dynamics and accurately predict the quadcopter outputs. These would greatly assist in designing robust flight controllers for the autopilot system

Magnetically plucked piezoelectric energy harvester via hybrid kinetic motion

Piezoelectric energy harvesting is a possible breakthrough to reduce the global issue of electronic waste as they can efficiently convert the ambient vibration to the electrical energy without any additional power. This work presents the design and development of a piezoelectric energy harvester that is capable of transforming vibration from ambient sources into electricity. It focuses on a magnetically plucked piezoelectric beam as an alternative to the mechanically induced harvesters, as the latter are subjected to wear and tear. A prototype comprising of a 40 mm PZT-5H piezoelectric beam with a permanent magnet mounted at one end of the beam, as well as a series of permanent magnets of same types attached on an eccentric rotor was developed along with a National Instruments® data acquisition device. Mean output voltages of 2.98 V, 1.76 V and 0.34 V were recorded when the eccentric rotors were slowly rotated at 8.4 rad/s with increasing distances of 5 mm, 7.5 mm and 10 mm respectively, between the magnets on the rotor and the beam. These results have proven that voltage could also be generated by magnetically plucking the piezoelectric beam, and by reducing the distance between magnets, the amount of voltage generated will be higher. The outcome of this work signifies the possibility for implementation of energy harvesters that are capable of powering electronic devices from hybrid kinetic motion, with a reduced risk of equipment fatigue. © 2019, International Islamic University Malaysia-IIUM

Multiple zones surveillance system using RFID

Monitoring and tracking the activities of individuals (or objects) in multiple zones or areas simultaneously is quite a challenging task. It is a very common practice to use observation cameras or to have security personnel to guard the specified areas. However, area surveillances using these common methods may become tedious when the activities to watch out for increases. More cameras as well as manpower are needed to cater for the increment of activities for each zone. On top of that, should anyone (or objects) vacates or trespasses the designated areas, it would take some time to identify them, hence resulting complexity in tracing their whereabouts. To overcome these challenges, we propose a surveillance system for multiple zones using RFID technology. Individuals or objects to be monitored are tagged using RFID tags that hold unique identification. Each zone is allocated with one RFID reader, which will transmit the information of activities of the respective zones to the host computer in the control room. The security personnel in the control room would be able to identify which individual or objects that are out of their designated zones or trespassing to another zone based on the tags that has been detected by the reader. Should the tags are removed without authorization, alarm will be generated. Every activity transactions are recorded in a database for future references or actions. A case study of inmate tracking system is conducted and demonstrated to prove the capability of the proposed method

Simulation and control of sensory-mode interaction

Haptics, the sensation of physical touch to the virtual objects, is the most recent enhancement to virtual environment. With haptic simulation, virtual objects with different properties could be created to touch using haptic device. In current medical practice, haptics technology is being used to aid surgeons to perform surgical procedures such as needle insertion. It is vital that the penetration of the needle does not cause injury to the patients. However, the available technology does not address issues such as tissue texture and the depth of penetration. This project is about the simulation of sensory mode interaction of virtual objects of different stiffness and friction using PHANToM Haptic device. The penetration depth and force exerted into the objects should be within limit to avoid any deformity to the objects. PID controller is incorporated into the system to eliminate steady state errors as well as to ensure better transient response. To conduct the specified work, MATLAB software was used. Experimental results on the sensory mode interaction have proven the ability of the system to touch the objects within specified object limits. Simulated results on the system response have also shown the capability of the controller to provide fast and accurate response of the haptic devic

Maximizing output voltage of a piezoelectric energy harvester via beam deflection method for low-frequency inputs

In micro-scale energy harvesting, piezoelectric (PZT) energy harvesters can adequately convert kinetic energy from ambient vibration to electrical energy. However, due to the random motion and frequency of human motion, the piezoelectric beam cannot efficiently harvest energy from ambient sources. This research highlights the ability of piezoelectric energy harvester constructed using a PZT-5H cantilever beam to generate voltage at any input frequency from human motion. An eccentric mass is used to convert the linear motion of human movement to angular motion. Then, using a magnetic plucking technique, the piezoelectric beam is deflected to its maximum possible deflection each time the eccentric mass oscillates past the beam, ensuring the highest stress is induced and hence the highest current is generated. For testing works, the frequency of oscillation of the eccentric mass is controlled using an Arduino Uno microcontroller. In this work, it is found that when given any input frequencies, the energy harvester produced a consistent AC voltage peak around 5.8 Vac. On the other hand, the DC voltage produced varies with respect to the input frequency due to the number of times the peak AC signal is generated. The highest DC voltage produced in this work is 3.7 Vdc, at 5 Hz, which is within the frequency range of human motion. This research demonstrated that energy can still be effectively harvested at any given low-frequency input, in the condition that the piezoelectric beam is being deflected at its maximum

Power Estimation for Wearable Piezoelectric Energy Harvester

The aim of this research work is to estimate the amount of electricity produced to power up wearable devices using a piezoelectric actuator, as an alternative to external power supply. A prototype of the device has been designed to continuously rotate a piezoelectric actuator mounted on a cantilever beam. A MATLAB® simulation was done to predict the amount of power harvested from human kinetic energy. Further simulation was conducted using COMSOL Multiphysics® to model a cantilever beam with piezoelectric layer. With the base excitation and the presence of tip mass at the beam, the natural frequencies and mode shapes have been analyzed to improve the amount of energy harvested. In this work, it was estimated that a maximum amount of power that could be generated is 250 μW with up to 5.5V DC output. The outcome from this research works will aid in optimising the design of the energy harvester. This research work provides optimistic possibility in harvesting sufficient energy required for wearable devices

Rotational piezoelectric energy harvester for wearable devices

Electronic devices are mostly powered externally via batteries. The dependency on the recharging process limits the usage of these devices to work in a specified period of time. This research work highlights the capability of a piezoelectric energy harvester to generate sufficient electricity to power up electronic devices by using low frequency vibrations alone, without relying on external power supplies. In general human motions consists of low frequency vibrations, therefore the capability to power up electronic devices using low frequency vibrations will also eventually become useful to power up wearable devices. Simulations were conducted using COMSOL Multiphysics® to identify the dimensions of a piezoelectric beam which will produce the optimum level of voltage output. A specially fabricated rotational piezoelectric energy harvester prototype that consists of a 40 mm piezoelectric bimorph beam that rotates with the aid of a rotor and aluminum proof-mass was developed together with a corresponding Arduino Uno based data logger. With a given input frequency of 18 Hz, the maximum voltage output that could be generated was recorded at 0.024 V. This research highlights the optimistic possibility that clean energy could be generated and utilized in powering various applications without depending on external power supplies

Electric motorcycle modeling for speed tracking and range travelled estimation

With the massive interest in electric vehicle technology, all different types of vehicles are moving toward green awareness, including the motorcycle. As time progresses, the investigations on the motorcycle developed to an even more complex model as the model need to be able to include the dynamics of the motorcycle at high speed. Relatively, few works of the literature found on an electric motorcycle (MC) modeling. Therefore, this paper aims to develop an E-MC model that represents a realistic model of the motorcycle with both kinematics and dynamics of the motorcycle incorporated in the model. The developed model is then tested for the speed tracking and the range travelled to evaluate the performance. Two different driving cycles that commonly applied in the commercial motorcycle evaluation test are used as the driving profiles in the simulation, namely, the Worldwide Motorcycle Test Cycle and New European Driving Cycle profiles. The results show an evident ability for the developed model of the E-MC to track the speed profile. It is also noted that the distance travelled by the E-MC model can be effectively determined

COLOR RECOGNITION WEARABLE DEVICE USING MACHINE LEARNING FOR VISUALY IMPAIRED PERSON

ABSTRACT: Recognizing colors is a concerning problem for the visually impaired person. The aim of this paper is to convert colors to sound and vibration in order to allow fully/partially blind people to have a ‘feeling’ or better understanding of the different colors around them. The idea is to develop a device that can produce vibration for colors. The user can also hear the name of the color along with ‘feeling’ the vibration. Two algorithms were used to distinguish between colors;  RGB to HSV color conversion in comparison with neural network and decision tree based machine learning algorithms. Raspberry Pi 3 with Open Source Computer Vision (OpenCV) software handles the image processing. The results for RGB to HSV color conversion algorithm were performed with 3 different colors (red, blue, and green). In addition, neural network and decision tree algorithms were trained and tested with eight colors (red, green, blue, orange, yellow, purple, white, and black) for the conversion to sound and vibration. Neural network and decision tree algorithms achieved higher accuracy and efficiency for the majority of tested colors as compared to the RGB to HSV. ABSTRAK: Membezakan antara warna adalah masalah yang merunsingkan terutamanya kepada mereka yang buta, separa buta atau buta warna. Tujuan kertas penyelidikan ini adalah untuk membentangkan kaedah menukar warna kepada bunyi dan getaran bagi membolehkan individu yang buta, separa buta atau buta warna untuk mendapat ‘perasaan’ atau pemahaman yang lebih baik tentang warna-warna yang berbeza disekeliling mereka. Idea yang dicadangkan adalah dengan membuat sebuah alat yang dapat menghasilkan getaran bagi setiap warna yang berbeza. Disamping itu, pengguna juga dapat mendengar nama warna tersebut. Algoritma yang digunakan untuk membezakan antara warna adalah penukaran warna RGB kepada HSV yang dibandingkan dengan rangkaian neural dan algoritma pembelajaran mesin berasaskan pokok keputusan. Raspberry Pi 3 bersaiz kad kredit dengan perisian Open Source Computer Vision (OpenCV) mengendalikan pemprosesan imej. Hasil algoritma penukaran warna RGB kepada HSV telah dilakukan dengan tiga warna yang berbeza (merah, biru, dan hijau). Tambahan pula, hasil rangkaian neural dan algoritma berasaskan pokok keputusan telah dilakukan dengan lapan warna (merah, hijau, biru, oren, kuning, ungu, putih, dan hitam) dengan penukaran warna tersebut kepada bunyi dan getaran. Selain itu, hasil rangkaian neural dan algoritma berasaskan pokok keputusan mencapai hasil dapatan yang baik dengan ketepatan dan kecekapan yang tinggi bagi kebanyakan warna yang diuji berbanding RGB kepada HSV

Performance evaluation for SE113 flow control system plant using self-tuning fuzzy PI controller

The aim of this project is to evaluate the dynamic process performance of SE113 Flow Control System Plant using self-tuning Fuzzy PI controller. The experimental data is used to model the process and the control analysis is done using Self-Tuning Fuzzy PI Controller. The performance evaluation is based on the percent overshoot, rise time and settling time of the process. The overall performance is compared with the conventional Proportional-Integral control method. The results had shown that self-tuning Fuzzy PI controller simplify the tediousness in tuning the controller and enhance the capability of PI controller