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The design and optimization of a Rayleigh- Benard convection based polymerase chain reaction (PCR) microchip is presented. This PCR microchip simplifies the fabrication, implementation and control of microfluidic PCR while enhancing the detection of the bacterial pathogen Salmonella Enteritidis . The device’s dimensions were optimized using multiphysics simulation then it was fabricated using high precision milling of Poly(methyl methacrylate) (PMMA) substrate. The device incorporated a single heater at the bottom end of its cylindrical chamber and two sensors at both ends. The heater was controlled using a fuzzy proportional, integral, derivative (PID) controller to maintain denaturation and annealing temperatures necessary for efficient PCR to take place. Salmonella Enteritidis DNA was successfully detected using the device while reducing the required assay time by 66%.

This study aims to enhance the condition monitoring of external ball bearings using the raw data provided by Paderborn University which provided sufficient data for motor current signal MCS. Three classes of bearings have been used: healthy bearings, bearings with an inner race defect, and bearings with outer race defect. Online data at different operating conditions, bearings, and faults extent of artificial and real damages have been chosen to provide the generalization and robustness of the model. After proper preprocessing to the raw data of vibration and MCS, time, frequency, and time–frequency domain features have been extracted. Then, optimal features have been selected using genetic algorithm. Artificial neural network with optimized structure using genetic algorithm has been implemented. A comparison between the performance of vibration and motor current signal has been presented. Moreover, our results are compared to previous work by using the same raw data. Results showed the potential of motor current signal in bearing fault diagnosis with high classification accuracy. Moreover, the results showed the possibility to provide a promised diagnostic model that can diagnose bearings of real faults with different fault severities using MCS.

In this paper, a Proportional-Integral-Differential plus second order derivative Fuzzy Logic Controller (PIDD2-FLC) is implemented aiming to maintain blood glucose level (BGL) normal in type I diabetic subjects. In type I diabetes, insulin-secreting cells are destroyed and, hence, patient depends on external insulin to maintain BGL. The suggested controller is responsible for driving a micro-pump that injects the diabetic patient with proper insulin dose, and a continuous BGL sensor is used for feedback. The nonlinear patient model used is the two-delay differential model, and the reference model for BGLs considered as in the treatment model planned using the two-delay differential model with oscillatory behavior. Particle Swarm Optimization with Linearly Decreasing Weight (LDW-PSO) algorithm is used to optimize the proposed controllers to match the reference model performance. Finally, a comparison is held between the optimized controller with other FLC controllers structures taking into consideration the normal and abnormal conditions. PIDD2-FLC with seven linguistic fuzzy membership functions (MFs) was found to have the best performance overall under different examining conditions.

Wheelchairs equipped with fixed positioned robotic arms is proposed to help paralyzed people. However, such a configuration makes it hard for the users to orient the wheel chair such that the task in need lies within the arm workspace. In this work a robotic arm that slides over a track mounted over a wheelchair is proposed to attain much wider workspace, at no extra complexity of arm design. Proposed design also reliefs the need to orient the wheelchair for extra user comfort. This work represents the Kinematic equations, Dh-parameters and dynamic modeling & simulation of an RRR robotic arm moving along a path built onto a wheelchair including the 3D workspace of the system. Preliminary results show robotic arm can achieve a very dexterous workspace at no extra cost in terms of arm torques yet offering a great comfort for wheelchair users.

Due to their various promising potential applications in the field of structural control, semi-active control of Magneto-Rheological (MR) dampers has been enjoying significant research focus for more than two decades. In this paper, a new direct voltage control algorithm for the MR damper is proposed to enhance the seismic response of civil structures. The MR dampers are represented using a modified Bouc-Wen model. A mathematical model representing a 3-story frame building with MR dampers at the ground floor in a chevron brace configuration, is used for the simulations. The proposed control law shows a high ability of attenuating structural response (floor accelerations and displacements). Simulations of the structural response under to the El Centro earthquake ground motion, are used to demonstrate the efficiency of the proposed control algorithm. The proposed control algorithm performance compares very well against other control algorithms available in the literature given its inherent relative simplicity.

Conventional shooting practice systems are expensive, life threatening, and lack proper statistical evaluation of trainee's performance. This paper presents the implementation and testing of a proposed evolutionary laser shooting practice system for early shooting practice stages. Presented system consists of a real gun replica equipped with lasers rather than live ammunition, a simple CCD camera, a shooting target, and a Compact Vision System (CVS) running National Instruments LabVIEW software. In order to detect con-centric rings of the shooting target an image processing algorithm is developed and employed by the CVS. In addition, the developed algorithm distinguishes locations of trainee's laser shots relative to the center point of the shooting target. For each training session, the system delivers a detailed overall performance report including scores of shooter accuracy and precision. The system was tested at a 100 m long shooting range under different camera-to-target angles and various lighting conditions. Results proved robust performance with accurate and detailed shooting reports under all tested conditions. Presented system overcomes conventional shooting practice systems in terms of cost, safety, evaluation accuracy and portability.

Classification problems of machine learning use supervised learning under specific targets to classify new observations. This work presents a new clustering and classification approach that combines an evolutionary algorithm with the K-means algorithm. In order to assess the performance of the proposed approach, the authors conducted a simulation study using a well-known benchmark problem called “two half-moon rings classification”. The selected problem introduces further complexity and higher classification challenge when a new observation is located in region of intersection of the two half-moons. The Cartesian coordinates of several points are used as a data set for two half-moon rings. The set is injected with complex overlap situations to constitute data points that belong to more than one class (ring) at a time. The modified set is investigated using the proposed clustering and classification approach. The proposed algorithm obtains the optimal cluster centers using genetic algorithm. Furthermore, it adopts whitening method to overcome the effect of overlapped points on clustering accuracy. Obtained classification results showed enhancement over those produced by the conventional K-means clustering algorithm. The results are consistent under different ring dimensions, and several overlap situations.

In sound source localization, there is a fundamental size limit; the smaller the size, the smaller the directional cues that are relied on to pinpoint the sound source. As such, it is challenging to develop miniature sound source localization robotic system where space is too confined to employ conventional microphone arrays without compromising localization performance. Our previous studies show that through mechanical coupling with well-tuned structural parameters, directional microphones inspired by the parasitic fly Ormia ochracea can amplify the minute interaural time delay (ITD) by more than ten times, which enables the reduction of device size significantly while maintaining localization performance. In this paper, Cramer Rao lower bound (CRLB) is derived for the fly-ear inspired sensor and the conventional directional microphones to study the effects of mechanical coupling on the decrease of the theoretical lower bound of azimuth estimation. This improvement gives mobile robots the capability to reactively localize sound in an indoor environment. Using this miniature sensor, new sound source localization method is proposed to localize a stationary sound source in 2-D (azimuth and elevation). In the proposed sound localization method, Model-Free Gradient Descent (MFGD) optimization method, one of the main challenges is to choose the appropriate cost function to achieve minimum number of iterations and the smallest absolute error. To this end, different cost functions are proposed and investigated with different control schemes. Simulation results showed the ability of this technique to solve the ambiguity problem and localize the sound source.Copyright © 2012 by ASME