Electrokinetic pumping using packed microcapillary

Electrokinetic micropumps are fabricated using 6–8 μm ODS beads into 320–700 μm fused-silica capillaries. The fabricated bead column is characterized by measuring the porosity and tortuosity. Experiments are carried out to study the flow characteristics under the effects of the packing conditions, solution properties and applied electric field strength. Specifically, the EO flow rates are measured using two independent methods: the test column method and the electric current monitoring method. Thus, the zeta potential of the packing beads can be obtained. In addition, a theoretical model is presented, and the theoretical predictions are found in reasonable agreement with the experimental data.     

iGAIT: An interactive accelerometer based gait analysis system

This paper presents a software program (iGAIT) developed in MATLAB, for the analysis of gait patterns extracted from accelerometer recordings. iGAIT provides a user-friendly graphical interface to display and analyse gait acceleration data recorded by an accelerometer attached to the lower back of subjects. The core function of iGAIT is gait feature extraction, which can be used to derive 31 features from acceleration data, including 6 spatio-temporal features, 7 regularity and symmetry features, and 18 spectral features. Features extracted are summarised and displayed on screen, as well as an option to be stored in text files for further review or analysis if required. Another unique feature of iGAIT is that it provides interactive functionality allowing users to manually adjust the analysis process according to their requirements. The system has been tested under Window XP, Vista and Window 7 using three different types of accelerometer data. It is designed for analysis of accelerometer data recorded with sample frequencies ranging from 5 Hz to 200 Hz.     

On-chip polymerase chain reaction microdevice employing a magnetic droplet-manipulation system

An on-chip polymerase chain reaction (PCR) device employing a magnetic beads-droplet-handling system was developed. Actuation with a magnet offers a simple system for droplet manipulation that allows separation and fusion of droplets containing magnetic beads by handling with a magnet. The device consists of a reaction chamber channel and two magnet-handling channels for the manipulation of micro-droplets containing magnetic beads. Micro-droplets were placed inside a reaction chamber filled with oil and manipulated with a magnet. When a droplet containing NaOH and magnetic beads was manipulated towards a droplet containing phenol red, a color change was observed after fusion. Sample preparation was performed by fusion of droplets containing a forward primer, reverse primer, template DNA and PCR mixture, using a droplet containing magnetic beads. PCR amplification or RT-PCR was also successfully performed, with efficiency comparable to manual methods that use this device by placing it on a thermal cycler for amplification. With a magnetic beads-manipulation step, purification of amplified DNA was also accomplished by using magnetic beads as the carrier. The amplified DNA was captured on streptavidin conjugated magnetic beads using a biotinylated primer, purified by washing and digested for separation of the target DNA.     

Piezoelectric-actuated drop-on-demand droplet generator control using adaptive wavelet neural network controller

This paper presents the design, fabrication and control of a piezoelectric-type droplet generator which is applicable for on-line dispensing. Adaptive wavelet neural network (AWNN) control is applied to overcome nonlinear hysteresis inherited in the LPM. The adaptive learning rates are derived based on the Lyapunov stability theorem so that the stability of the closed-loop system can be assured. Unlike open-loop dispensing system, the system proposed can potentially generate droplets with high accuracy. Experimental verifications focusing on regulating control are performed firstly to assure the reliability of the proposed control schemes. Real dispensing is then conducted to validate the feasibility of the piezoelectric-actuated drop-on-demand droplet generator. In order to illustrate the effectiveness of the proposed method, experimental results obtained using the AWNN scheme are compared with their counterparts using traditional PID control. The results indicate that the proposed AWNN scheme not only outperforms PID control but also works well in developing the piezoelectric-actuated drop-on-demand dispensing system. The proposed dispensing system provides droplet chains with an averaged mass as small as 31.5 mg while the associated standard deviation is as low as 0.72%.     

Neural-swarm visual saliency for path following

This paper extends an existing saliency-based model for path detection and tracking so that the appearance of the path being followed can be learned and used to bias the saliency computation process. The goal is to reduce ambiguities in the presence of strong distractors. In both original and extended path detectors, neural and swarm models are layered in order to attain a hybrid solution. With generalisation to other tasks in mind, these detectors are presented as instances of a generic neural-swarm layered architecture for visual saliency computation. The architecture considers a swarm-based substrate for the extraction of high-level perceptual representations, given the low-level perceptual representations extracted by a neural-based substrate. The goal of this division of labour is to ensure parallelism across the vision system while maintaining scalability and tractability. The proposed model is shown to exhibit, at 20 Hz, a 98.67% success rate on a diverse data-set composed of 39 videos encompassing a total of 29,789 640 × 480 frames. An open source implementation of the model, fully encapsulated as a node of the Robotics Operating System (ROS), is available for download.     

A new metric based on extended spatial frequency and its application to DWT based fusion algorithms

A new quantitative metric is proposed to objectively evaluate the quality of fused imagery. The measured value of the proposed metric is used as feedback to a fusion algorithm such that the image quality of the fused image can potentially be improved. This new metric, called the ratio of spatial frequency error (rSFe), is derived from the definition of a previous measure termed “spatial frequency” (SF) that reflects local intensity variation. In this work, (1) the concept of SF is first extended by adding two diagonal SFs, then, (2) a reference SF (SF_R) is computed from the input images, and finally, (3) the error SF (SF_E) (subtracting the fusion SF from the reference SF), or the ratio of SF error (rSFe = SF_E/SF_R), is used as a fusion quality metric. The rSFe (which can be positive or negative) indicates the direction of fusion error—over-fused (if rSFe > 0) or under-fused (if rSFe < 0). Thus, the rSFe value can be back propagated to the fusion algorithm (BP fusion), thereby directing further parameter adjustments in order to achieve a better-fused image. The accuracy of the rSFe is verified with other quantitative measurements such as the root mean square error (RMSE) and the image quality index (IQI), as well as with a qualitative perceptual evaluation based on a standard psychophysical paradigm. An advanced wavelet transform (aDWT) method that incorporates principal component analysis (PCA) and morphological processing into a regular DWT fusion algorithm is implemented with two adjustable parameters—the number of levels of DWT decompositions and the length of the selected wavelet. Results with aDWT were compared to those with a regular DWT and with a Laplacian pyramid. After analyzing several inhomogeneous image groups, experimental results showed that the proposed metric, rSFe, is consistent with RMSE and IQI, and is especially powerful and efficient for realizing the iterative BP fusion in order to achieve a better image quality. Human perceptual assessment was measured and found to strongly support the assertion that the aDWT offers a significant improvement over the DWT and pyramid methods.     

Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients

For the quality of the fused outcome is determined by the amount of the information captured from the source images, thus, a multi-modal medical image fusion method is developed in the shift-invariant shearlet transform (SIST) domain. The two-state Hidden Markov Tree (HMT) model is extended into the SIST domain to describe the dependent relationships of the SIST coefficients of the cross-scale and inter-subbands. Base on the model, we explain why the conventional Average–Maximum fusion scheme is not the best rule for medical image fusion, and therefore a new scheme is developed, where the probability density function and standard deviation of the SIST coefficients are employed to calculate the fused coefficients. Finally, the fused image is obtained by directly applying the inverse SIST. Integrating the SIST and the HMT model, more spatial feature information of the singularities and more functional information contents can be preserved and transferred into the fused results. Visual and statistical analyses demonstrate that the fusion quality can be significantly improved over that of five typical methods in terms of entropy and mutual information, edge information, standard deviation, peak signal to noise and structural similarity. Besides, color distortion can be suppressed to a great extent, providing a better visual sense.     

Genetic programming for epileptic pattern recognition in electroencephalographic signals

This paper reports how the genetic programming paradigm, in conjunction with pattern recognition principles, can be used to evolve classifiers capable of recognizing epileptic patterns in human electroencephalographic signals. The procedure for feature extraction from the raw signal is detailed, as well as the genetic programming system that properly selects the features and evolves the classifiers. Based on the data sets used, two different epileptic patterns were detected: 3 Hz spike-and-slow-wave-complex (SASWC) and spike-or-sharp-wave (SOSW). After training, classifiers for both patterns were tested with unseen instances, and achieved sensibility = 1.00 and specificity = 0.93 for SASWC patterns, and sensibility = 0.94 and specificity = 0.89 for SOSW patterns. Results are very promising and suggest that the methodology presented can be applied to other pattern recognition tasks in complex signals.     

Optimal fuzzy sliding-mode control for bio-microfluidic manipulation

In biometric and biomedical applications, a special transporting mechanism must be designed for the micro total analysis system (μTAS) to move samples and reagents through the microchannels that connect the unit procedure components in the system. An important issue for this miniaturization and integration is the microfluid management technique, i.e., microfluid transportation, metering, and mixing. In view of this, an optimal fuzzy sliding-mode control (OFSMC) based on the 8051 microprocessor is designed and a complete microfluidic manipulated biochip system is implemented in this study, with a pneumatic pumping actuator, two feedback-signal photodiodes and flowmeters for better microfluidic management. This new technique successfully improved the efficiency of biochemical reaction by increasing the effective collision into the probe molecules as the target molecules flow back and forth. The new technique was used in DNA extraction. When the number of Escherichia coli cells was 2×10²–10⁴ in 25 μl of whole blood, the extraction efficiency of immobilized beads with solution flowing back and forth was 600-fold larger than that of free beads.     

Scaling-based forest structural change detection using an inverted geometric-optical model in the Three Gorges region of China

We use the Li-Strahler geometric-optical model combined with a scaling-based approach to detect forest structural changes in the Three Gorges region of China. The physical-based Li-Strahler model can be inverted to retrieve forest structural properties. One of the main input variables for the inverted model is the fractional component of sunlit background, which is calculated by using pure reflectance spectra (endmembers) of surface components. In this study, we extract these endmembers from moderate spatial resolution MODIS data using two scaling-based methods (namely, a regional based linear unmixing and a purest-pixel approach) relying on corresponding high spatial resolution Landsat TM images. Then, the forest structural property crown closure (CC) is estimated by inverting the Li-Strahler model based on the extracted endmembers. Changes in CC are mapped using MODIS mosaics dated 2002 and 2004 for the whole Three Gorges region. Validation of the estimated CC using 25 sample sites indicates that the regional scaling-based endmembers extracted using linear unmixing are more suitable to be used in combination with the inverted Li-Strahler model for monitoring the forest CC than the purest-pixel approach, and results in significantly better estimates in both years (R²₂₀₀₂ = 0.614, RMSE₂₀₀₂ = 6%, R²₂₀₀₄ = 0.631 and RMSE₂₀₀₄ = 5.2%). A change detection map of the model derived CC in 2002 and 2004 shows a decrease in CC in the eastern counties of the Three Gorges region located close to the Three Gorges Dam. An increase in CC has been observed in other counties of the Three Gorges region, implying a preliminary positive feedback on certain policy measures taken safeguarding forest structure.     

Modeling perspective projections in 3-dimensions by rotations in 4-dimensions

We show how to represent perspective projections in 3-dimensions using rotations in 4-dimensions. This representation permits us to replace classical singular 4 × 4 matrices for perspective projection with nonsingular 4 × 4 orthogonal matrices. This approach also allows us to compute perspective projections by sandwiching vectors between two copies of a unit quaternion. In addition to deriving explicit formulas for these 4 × 4 rotation matrices for perspective projection, we also explain the geometric intuition underlying the observation that perspective projections in 3-dimensions can be represented by rotations in 4-dimensions. We show too that every rotation in 4-dimensions models either a rotation, a reflection, a perspective projection, or one of their composites in 3-dimensions.     

Multi-class fault diagnosis of induction motor using Hilbert and Wavelet Transform

The information extraction capability of two widely used signal processing tools, Hilbert Transform (HT) and Wavelet Transform (WT), is investigated to develop a multi-class fault diagnosis scheme for induction motor using radial vibration signals. The vibration signals are associated with unique predominant frequency components and instantaneous amplitudes depending on the motor condition. Using good systematic and analytical approach this fault frequencies can be identified. However, some faults either electrical or mechanical in nature are associated with same or similar vibration frequencies leading to erroneous conclusions. Genetic Algorithm (GA) is proposed and used successfully to find the most relevant fault frequencies in radial (vertical) frame vibration signal which can be used to diagnose the induction motor faults very effectively even in the presence of noise. The information obtained by Continuous Wavelet Transform (CWT) was found to be highly redundant compared to HT and thus by selecting the most relevant features using GA, the fault classification accuracy has considerably improved especially for CWT. Almost similar fault frequencies were found using CWT + GA and HT + GA for radial vibration signal.     

Fusion of voice signal information for detection of mild laryngeal pathology

Detection of mild laryngeal disorders using acoustic parameters of human voice is the main objective in this study. Observations of sustained phonation (audio recordings of vocalized /a/) are labeled by clinical diagnosis and rated by severity (from 0 to 3). Research is exclusively constrained to healthy (severity 0) and mildly pathological (severity 1) cases – two the most difficult classes to distinguish between.Comprehensive voice signal characterization and information fusion constitute the approach adopted here. Characterization is obtained through diverse feature set, containing 26 feature subsets of varying size, extracted from the voice signal. Usefulness of feature-level and decision-level fusion is explored using support vector machine (SVM) and random forest (RF) as basic classifiers. For both types of fusion we also investigate the influence of feature selection on model accuracy. To improve the decision-level fusion we introduce a simple unsupervised technique for ensemble design, which is based on partitioning the feature set by k-means clustering, where the parameter k controls the size and diversity of the prospective ensemble.All types of the fusion resulted in an evident improvement over the best individual feature subset. However, none of the types, including fusion setups comprising feature selection, proved to be significantly superior over the rest. The proposed ensemble design by feature set decomposition discernibly enhanced decision-level and significantly outperformed feature-level fusion. Ensemble of RF classifiers, induced from a cluster-based partitioning of the feature set, achieved equal error rate of 13.1 ± 1.8% in the detection of mildly pathological larynx. This is a very encouraging result, considering that detection of mild laryngeal disorder is a more challenging task than a common discrimination between healthy and a wide spectrum of pathological cases.     

Remote sensing image fusion using the curvelet transform

This paper presents an image fusion method suitable for pan-sharpening of multispectral (MS) bands, based on nonseparable multiresolution analysis (MRA). The low-resolution MS bands are resampled to the fine scale of the panchromatic (Pan) image and sharpened by injecting highpass directional details extracted from the high-resolution Pan image by means of the curvelet transform (CT). CT is a nonseparable MRA, whose basis functions are directional edges with progressively increasing resolution. The advantage of CT with respect to conventional separable MRA, either decimated or not, is twofold. Firstly, directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve a noise reduction that is better than that obtained in the separable wavelet domain. Secondly, modeling of the relationships between high-resolution detail coefficients of the MS bands and of the Pan image is more fitting, being accomplished in the directional multiresolution domain. Experiments are carried out on very-high-resolution MS + Pan images acquired by the QuickBird and Ikonos satellite systems. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art. Fusion tests at the full scale reveal that an accurate and reliable Pan-sharpening, little affected by local inaccuracies even in the presence of complex and detailed urban landscapes, is achieved by the proposed method.     

A magnetoelectric energy harvester and management circuit for wireless sensor network

This paper presents an electromagnetic energy harvesting scheme by using a composite magnetoelectric (ME) transducer and a power management circuit. In the transducer, the vibrating wave induced from the magnetostrictive Terfenol-D plate in dynamic magnetic field is converged by using an ultrasonic horn. Consequently more vibrating energy can be converted into electricity by the piezoelectric element. A switching capacitor network for storing electricity is developed. The output of the transducer charges the storage capacitors in parallel until the voltage across the capacitors arrives at the threshold, and then the capacitors are automatically switched to being in series. More capacitors can be employed in the capacitor network to further raise the output voltage in discharging. For the weak magnetic field environment, an active magnetic generator and a magnetic coil antenna under ground are used for producing an ac magnetic field of 0.2–1 Oe at a distance of 25–50 m. In combination with the supply management circuit, the electromagnetic energy harvester with a rather weak power output (about 20 μW) under an ac magnetic field of 1 Oe can supply power for wireless sensor nodes with power consumption of 75 mW at a duration of 620 ms.     

Improvement in smoothness of anisotropically etched silicon surfaces: Effects of surfactant and TMAH concentrations

We investigated the anisotropic etching properties of single-crystal silicon using tetramethyl-ammonium-hydroxide (TMAH) water solutions containing poly-oxethylene-alkyl-phenyl-ether (NC-200) as a surfactant. When the surfactant was added at 0.1% of the total volume of the etchant, the etched surface morphologies drastically changed, along with the anisotropy of the etching rate. We found that by using the surfactant at the low TMAH concentration region, a smooth mirror-like surface can be etched in both (1 0 0) and (1 1 0) orientations simultaneously. Although the addition of the surfactant reduces the etching rate, we show how this procedure can be used to improve the roughness of an etched surface without significantly increasing the overall processing time.     

Fabrication and chromatic characteristics of the greenish LCD colour-filter layer with nano-particle ink using inkjet printing technique

Inkjet-printed greenish color filter layer for the application of active-matrix TFT-LCD had been significantly fabricated by the modified inkjet printing technology. The greenish ink with nano-particle pigments was injected in the stripe-type pattern with 90 μm in width and 250 μm in length by pre-designed inkjet head and driving system, and only green color was tested. The height of transparent rib wall, prepared by lithographic processes, on black matrix is 5.0 μm. The chromatic coordinates of color filter with greenish subpixel patterns in CIE 1931 standard diagram can achieve in the region of x=0.3095±0.04, y=0.5912±0.04, brightness of Y=58.887 for 50 droplets and x=0.3103±0.04, y=0.5784±0.04, brightness of Y=60.328 for 41 droplets. The structural surface morphologies of the greenish subpixel patterns remain homogenous, smooth and flatten.     

Multi-layer atom chips for atom tunneling experiments near the chip surface

This paper describes the design and fabrication of an atom chip to be used in ultra-high-vacuum cells for cold-atom tunneling experiments. A fabrication process was developed to pattern micrometer- and nanometer-scale copper wires onto a single chip. The wires, with fabricated widths down to 200 nm, can sustain current densities of more than 7.5 × 10⁷ A/cm². Partially suspended wires, developed in order to reduce the Casimir–Polder force between atoms and surface, were also fabricated and tested. Extensive measurements for variable wire width show that the sustainable currents are sufficiently large to allow chip-based atom tunneling experiments. Such chips may allow the realization of an atom transistor.     

An automated signalized junction controller that learns strategies by temporal difference reinforcement learning

This paper shows how temporal difference learning can be used to build a signalized junction controller that will learn its own strategies through experience. Simulation tests detailed here show that the learned strategies can have high performance. This work builds upon previous work where a neural network based junction controller that can learn strategies from a human expert was developed (Box and Waterson, 2012). In the simulations presented, vehicles are assumed to be broadcasting their position over WiFi giving the junction controller rich information. The vehicle's position data are pre-processed to describe a simplified state. The state-space is classified into regions associated with junction control decisions using a neural network. This classification is the strategy and is parametrized by the weights of the neural network. The weights can be learned either through supervised learning with a human trainer or reinforcement learning by temporal difference (TD). Tests on a model of an isolated T junction show an average delay of 14.12 s and 14.36 s respectively for the human trained and TD trained networks. Tests on a model of a pair of closely spaced junctions show 17.44 s and 20.82 s respectively. Both methods of training produced strategies that were approximately equivalent in their equitable treatment of vehicles, defined here as the variance over the journey time distributions.