Smart steering auto alert system

Drivers can easily be distracted by their handheld devices while they are driving and this ultimately contributed to the increase of road accidents. This work proposed a steering wheel cover that is designed using an array of touch sensors TTP223 and Raspberry Pi 3 microprocessor. A tilt sensor is also incorporated in order to mimic the movement of the system. Using Python as the main programming language and the Raspbian OS, for a sample size of 40 touch inputs, the system yielded an accuracy of 97.5 % and 75.0 % in its input detection during stationary and driving mode. The results have shown that as a proof of concept, the proposed system is capable of detecting touch inputs from the user’s hand and determining the position of the hands on the steering wheel

Electrical characterisation of highly doped triangular silicon nanowires

A top-down silicon nanowire fabrication using a combination of optical lithography and orientation dependent etching (ODE) has been developed using Silicon-on Insulator (SOI) as the starting substrate. Initially, the samples were doped with phosphorus using the diffusion process resulting in carrier concentration of 2 X 10 18 cm-3. After the silicon nanowires were fabricated, they were measured using a dual configuration method which is similar to the four-point probe measurement technique to deduce its resistivity. The data obtained had suggested that the doping distribution in the silicon nanowires were lower and this may have been affected by the surface depletion effect. In addition, with respect to carrier mobility, the effective mobility of electrons extracted using the four-point probe data had demonstrated that the mobility of carriers in the silicon nanowire is comparable with the bulk mobility. This is most probably due to the fact that in this research, the quantum confinement effect on these nanowires is not significant

Artificial neural network based fast edge detection algorithm for MRI medical images

Currently, magnetic resonance imaging (MRI) has been utilized extensively to obtain high contrast medical image due to its safety which can be applied repetitively. Edges are represented as important contour features in the medical image since they are the boundaries where distinct intensity changes or discontinuities occur. Many traditional algorithms have been proposed to detect the edge, such as Canny, Sobel, Prewitt, Roberts, Zerocross, and Laplacian of Gaussian (LoG). Moreover, many researches have shown the potential of using Artificial Neural Network (ANN) for edge detection. Although many algorithms have been conducted on edge detection for medical images, however higher computational cost and subjective image quality could be further improved. Therefore, the objective of this paper is to develop a fast ANN based edge detection algorithm for MRI medical images. First, we developed features based on horizontal, vertical, and diagonal difference. Then, Canny edge detector will be used as the training output. Finally, optimized parameters will be obtained, including number of hidden layers and output threshold. Results showed that the proposed algorithm provided better image quality while it has faster processing time around three times time compared to other traditional algorithms, such as Sobel and Canny edge detector

Material characterization of a doped triangular silicon nanowire using raman spectroscopy

A top-down silicon nanowire fabrication using a combination of optical lithography and orientation dependent etching (ODE) has been developed using a doped Silicon-on Insulator (SOI) as the starting substrate. The use of ODE etchant such as potassium hydroxide (KOH) and Tetra-Methyl Ammonium Hydroxide (TMAH) is known to create geometrical structures due to its anisotropic mechanism of etching. The SOI is doped with an n-type dopant (phosphorus) and the doped silicon nanowire is then characterized using Raman Spectroscopy. Due to the changes in the silicon structure, the result shows that the highly doped silicon nanowire has a wider Full Width Half Maximum (FWHM) as compared to the undoped silicon substrate

Artificial neural network based fast edge detection algorithm for MRI medical images

Currently, magnetic resonance imaging (MRI) has been utilized extensively to obtain high contrast medical image due to its safety which can be applied repetitively. Edges are represented as important contour features in the medical image since they are the boundaries where distinct intensity changes or discontinuities occur. Many traditional algorithms have been proposed to detect the edge, such as Canny, Sobel, Prewitt, Roberts, Zerocross, and Laplacian of Gaussian (LoG). Moreover, many researches have shown the potential of using Artificial Neural Network (ANN) for edge detection. Although many algorithms have been conducted on edge detection for medical images, however higher computational cost and subjective image quality could be further improved. Therefore, the objective of this paper is to develop a fast ANN based edge detection algorithm for MRI medical images. First, we developed features based on horizontal, vertical, and diagonal difference. Then, Canny edge detector will be used as the training output. Finally, optimized parameters will be obtained, including number of hidden layers and output threshold. Results showed that the proposed algorithm provided better image quality while it has faster processing time around three times time compared to other traditional algorithms, such as Sobel and Canny edge detector

Multi-user mmWave MIMO channel estimation with hybrid Beamforming over frequency selective fading channels

In multi-user millimeter wave (mmWave) multiple input multiple output (MIMO) systems, obtaining accurate information/knowledge regarding the channel state is crucial to achieving multi-user interference cancellation and reliable beamforming (BF)-to compensate for severe path loss. This knowledge is nonetheless very challenging to acquire in practice since large antenna arrays experience a low signal-to-noise ratio (SNR) before BF. In this paper, a multi-user channel estimation (CE) scheme namely generalized-block compressed sampling matching pursuit (G-BCoSaMP), is proposed for multi-user mmWave MIMO systems over frequency selective fading channels. This scheme exploits the cluster-structured sparsity in the angular and delay domain of mmWave channels determined by the actual spatial frequencies of each path. As the corresponding spatial frequencies of multi-user mmWave MIMO systems with Hybrid BF often fall between the discrete Fourier transform (DFT) bins due to the continuous Angle of Arrival (AoA)/Angle of Departure (AoD), the proposed G-BCoSaMP algorithm can address the resulting power leakage problem. Simulation results show that the proposed algorithm is effective and offer a better CE performance in terms of MSE when compared to the generalized block orthogonal matching pursuit (G-BOMP) algorithm that does not possess a pruning step

Statistics of rainfall rate at 60 minutes integration time in Malaysia

Background: This paper presents the statistics of rainfall at 60- minute integration time in Malaysia for the period of 12 months from January to December 2009. Objective: To analyze the statistics and characteristics of rainfall intensity measurement at KLIA, Malaysia. To study the behavior of measured rainfall intensity and represent the annual distribution of the measured rainfall through cumulative distribution functions together with different types of rainfall that occurred in 2009. Results: The results obtained show the cumulative distribution functions and amount of the rainfall rate for that particular year. The results also show the different cumulative distribution functions for four different rainfall types that occur in 2009. Conclusion: The study of the 12-month tipping bucket data has given the characteristics of the collected rainfall. From the results obtained, it shows that Malaysia is within equatorial region with the characteristics of two distinguishable rainfall rates that occurs during the whole year

Finite element simulation of MEMS piezoelectric energy scavenger based on pzt thin film

Vibration energy harvesting has been progressively developed in the advancement of technology and widely used by a lot of researchers around the world. There is a very high demand for energy scavenging around the world due to it being cheaper in price, possibly miniaturized within a system, long lasting, and environmentally friendly. The conventional battery is hazardous to the environment and has a shorter operating lifespan. Therefore, ambient vibration energy serves as an alternative that can replace the battery because it can be integrated and compatible to micro-electromechanical systems. This paper presents the design and analysis of a MEMS piezoelectric energy harvester, which is a vibration energy harvesting type. The energy harvester was formed using Lead Zicronate Titanate (PZT-5A) as the piezoelectric thin film, silicon as the substrate layer and structural steel as the electrode layer. The resonance frequency will provide the maximum output power, maximum output voltage and maximum displacement of vibration. The operating mode also plays an important role to generate larger output voltage with less displacement of cantilever. Some designs also have been studied by varying height and length of piezoelectric materials. Hence, this project will demonstrate the simulation of a MEMS piezoelectric device for a low power electronic performance. Simulation results show PZT-5A piezoelectric energy with a length of 31 mm and height of 0.16 mm generates maximum output voltage of 7.435 V and maximum output power of 2.30 mW at the resonance frequency of 40 Hz

Chitosan Coating on Quartz Crystal Microbalance Gas Sensor for Isopropyl Alcohol and Acetone Detection

The development of acoustic wave sensors was driven by the presence of modern technology. Quartz crystal microbalance (QCM) has excellent sensing capabilities and has wide range of applications. Selection of sensing layer is crucial to ensure the performance of the QCM sensor for volatile organic compound (VOC) detection.Hence, the objective of this paper is to compare the performance of chitosan coated QCM sensor for different analyte gas: isopropyl alcohol (IPA). Finite element simulation was implemented using COMSOL Multiphysics to study the resonance frequency shift before and after sensing. Simulation results shows IPA detection shows a higher resonance frequency shift of 62.5 Hz compared to acetone due to higher molar mass. Experimental work is conducted to validate the simulation results where IPA analyte gas yields in 84.8 Hz which is higher than acetone analyte gas at 41.8 Hz. The functional groups for both sensing layer and analyte gas also affects the gas detection performance. IPA analyte gas possessed hydroxyl groups that favors to hydrogen bond formation with chitosan sensing layer. Thus, the QCM sensor with chitosan as the sensing layer has the potential for VOC sensing of different molar mass and functional groups

Effect of chitosan dissolved in different acetic acid concentration towards VOC sensing performance of quartz crystal microbalance overlay with chitosan

Improvement in sensing layer properties of quartz crystal microbalance (QCM) sensors are crucial in developing gas sensors with high sensitivity and selectivity. In this work, we study the use of chitosan thin film as the sensing layer on a QCM sensor to identify the presence of volatile organic compounds specifically isopropyl alcohol (IPA). The effect of chitosan dissolved in different acetic acid concentrations towards QCM overlay with chitosan sensing performance were studied. Characterization work on chitosan thin film at different acetic acid concentrations (1.0, 1.5, 2.0, 2.5% (v/v)) were performed by using FTIR and FESEM. Higher acid concentration led to a higher degree of protonation which results in a more progressive solubilization of chitosan and promotes smoother film. For chitosan layer dissolved in 2% acetic acid, the highest resonance frequency shift (99.3 Hz) was observed during the adsorption of the analyte gas molecules on QCM sensors. This can be explained by the increase in chitosan solubility and protonation. This indicates that difference acid concentration in chitosan dissolution affects the sensing performance during the presence of the analyte gas