Analysis of the properties of a linear system using the method of artificial basis matrices

Formulas relating elements of the method in adjacent basis matrices are used to solve a system of linear algebraic equations and to represent analytically the general solutions to the corresponding system of linear algebraic inequalities for a nondegenerate constraint matrix. Sponsored by the ICS NATO program of April 18 2006, in line with the Project “Optimal replacement of information technologies and stable development (in Kazakhstan, Ukraine, and the USA),” NATO Grant CLG 982209. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 119–127, July–August 2007.     

Measurement technique for the thermal properties of thin-film diaphragms embedded in calorimetric flow sensors

In diaphragm-based micromachined calorimetric flow sensors, convective heat transfer through the test fluid competes with the spurious heat shunt induced by the thin-film diaphragm where heating and temperature sensing elements are embedded. Consequently, accurate knowledge of thermal conductivity, thermal diffusivity, and emissivity of the diaphragm is mandatory for design, simulation, optimization, and characterization of such devices. However, these parameters can differ considerably from those stated for bulk material and they typically depend on the production process. We developed a novel technique to extract the thermal thin-film properties directly from measurements carried out on calorimetric flow sensors. Here, the heat transfer frequency response from the heater to the spatially separated temperature sensors is measured and compared to a theoretically obtained relationship arising from an extensive two-dimensional analytical model. The model covers the heat generation by the resistive heater, the heat conduction within the diaphragm, the radiation loss at the diaphragm’s surface, and the heat sink caused by the supporting silicon frame. This contribution summarizes the analytical heat transfer analysis in the microstructure and its verification by a computer numerical model, the measurement setup, and the associated thermal parameter extraction procedure. Furthermore, we report on measurement results for the thermal conductivity, thermal diffusivity, and effective emissivity obtained from calorimetric flow sensor specimens featuring dielectric thin-film diaphragms made of plasma enhanced chemical vapor deposition silicon nitride.     

Adaptive virtual sensors using SNPER for the localized construction and elastic net regularization in nonlinear processes

Traditional data-driven virtual sensors were constructed upon the macro-perspective of the manifold structure using generalized models. They did not focus on the local relationships among the data samples through micro-perspective of manifold proximity indicating the local relationships among the data samples. In the case with the quantity of data points fewer than the dimensions of the data variables, the virtual sensor model is likely to be unstable, ill-conditioned, and computationally expensive. And the real-world data often vary with time. It is difficult, in the long term, to sustain good performance by a single fixed model. This paper addresses the aforementioned issues by proposing three algorithms (NPER, SNPER, and LW-SNPER) to successively improve the virtual sensor modeling performance for nonlinear, high-dimensional and time-varying processes. It is shown through the numerical cases and a real semiconductor process that the proposed algorithms perform better than the other regular regression algorithms.     

Detection and differentiation of breast cancer using neural classifiers with first warning thermal sensors

Breast cancer occurs when cells in the breast begin to grow out of control and invade nearby tissues or spread throughout the body. It is one of the leading causes of death in women. Cancer development appears to generate an increase in the temperature on the breast surface. The limitations of mammography as a screening modality, especially in young women with dense breasts, necessitated the development of novel and more effective screening strategies with high sensitivity and specificity. The aim of this study was to evaluate the feasibility of discrete thermal data (DTD) as a potential tool for the early detection of the breast cancer.Our protocol uses 1170, 16-sensor data collected from 54 individuals consisting of three different kinds of breast conditions: namely, normal, benign and cancerous breast. We compared two different kinds of neural network classifiers: the feedforward neural network and the radial basis function classifier. Temperature data from the 16 temperature sensors on the surface of the two breasts (eight sensors on each side) are fed as input to the classifiers. We demonstrated a sensitivity of 84% and 91% for these classifiers (feedforward and radial basis function, respectively) with a specificity of 100%. Our classifying systems are ready to run on large data sets.     

Measuring the learnability of spreadsheets in inexperienced users and those with previous spreadsheet experience

The issues of 'usability' and 'learnability' are assuming an increasingly important role for both the designers of software and their prospective customers. Objective measures of the interaction between system and user are important for the development of software that is both easy to learn and pleasurable to use. In this study we apply a set of five measures to evaluate users' interactions with spreadsheet software, and to compare two spreadsheet packages. We tested 16 people with no previous experience of spreadsheets and 16 with experience of spreadsheets generally though not of the spreadsheet we gave them. Half were allocated to learn Excel and half to learn Wingz, running on Apple Macintosh computers. A standard task was constructed to assess understanding of the basic concepts involved in the use of • spreadsheets. Users' previous experience of spreadsheet use was the most salient factor in the scores achieved on the task. The brand of spreadsheet had no significant effect on task performance. Implications for designers of software and users of spreadsheet packages are discussed.     

Refined sandwich model for the vibration of beams with embedded shear piezoelectric actuators and sensors

This work extends a previously presented refined sandwich beam finite element (FE) model to vibration analysis, including dynamic piezoelectric actuation and sensing. The mechanical model is a refinement of the classical sandwich theory (CST), for which the core is modelled with a third-order shear deformation theory (TSDT). The FE model is developed considering, through the beam length, electrically: constant voltage for piezoelectric layers and quadratic third-order variable of the electric potential in the core, while mechanically: linear axial displacement, quadratic bending rotation of the core and cubic transverse displacement of the sandwich beam. Despite the refinement of mechanical and electric behaviours of the piezoelectric core, the model leads to the same number of degrees of freedom as the previous CST one due to a two-step static condensation of the internal dof (bending rotation and core electric potential third-order variable). The results obtained with the proposed FE model are compared to available numerical, analytical and experimental ones. Results confirm that the TSDT and the induced cubic electric potential yield an extra stiffness to the sandwich beam.     

The effect of previous experience with information and communication technologies on performance in a Web-based learning program

The purpose of the present study was to analyze the influence of previous experience with information and communication technology (ICT) tools on performance in a Web-based course. Online activity was logged. Specifically, total activities carried out and five online activity measures were computed. One hundred and twenty university undergraduates participated in a Web-based course designed to train personnel trainers to apply ICT in training processes. Before starting the study, participants filled out a questionnaire designed to assess their frequency using ICT tools. At the end of the study, participants were asked to complete a questionnaire in order to collect information about their learning experience and their assessment of the virtual environment. Results suggest that previous experience significantly influences how people organize their online activities, but not the quantity of work carried out. Participants with more experience in the use of Internet tools, organize their work in the virtual classroom using less time and visiting fewer pages in each session. Previous experience did not influence the assessment of the virtual classroom as a training tool, since participants with both high and low previous experience gave extremely positive feedback on their Web-based learning experience.     

Detection principles and development of microfluidic sensors in the last decade

Microfluidic sensor converts a physical quantity to useful signal with the help of microfluidic platform. Microfluidic sensors have got a wide attention in the last decade because of the increased demands from the automation and control in microsystems. This review on microfluidic sensors focuses on various types of sensors which have been developed for the microfluidic systems or applications based on the research contributions in the last decade. We start with a detailed comparison on the research developments in the last decade on microfluidic sensors with the help of year and country wise statistical charts on published works in the area. The review continues with the basics of microfluidic sensors and the working principles of microfluidic sensors by classifying various microfluidic sensors based on the parameter to be sensed. This review concludes with the attempt to provide an idea on research gap in the area of microfluidic sensors.     

Detection and replacement of a failing node in the wireless sensors networks

The lifetime in a wireless network, in particular a wireless sensor network, depends strongly on the connectivity factor between nodes. Several factors can be at the origin of a connectivity rupture such as: lack of energy on a significant node level, infection of a vital node by a malevolent code and a logical or physical failure of a primary node. This rupture can lead in some cases to a reconfiguration of the network by generating a prejudicial overhead or in other cases to a failure of the mission assigned to the network. In this paper, we propose a DRFN approach (Detection and Replacement of a Failing Node) for the connectivity maintenance by carrying out a replacement chain according to a distributed algorithm. Through simulation, we have shown our approach efficiency. Compared with similar work, our proposed approach consumes less energy, and improves the percentage of reduction in field coverage.     

Evaluation of elastic properties and temperature effects in Si thin films using an electrostatic microresonator

Laterally driven microresonators were used to estimate the temperature-dependent elastic modulus of single-crystalline Si for microelectromechanical systems (MEMS). The resonators were fabricated through surface micromachining from silicon-on-glass wafers. They were moved laterally by alternating electrostatic force at a series of frequencies, and then a resonance frequency was determined, under temperature cycling in the range of 25/spl deg/C to 600/spl deg/C, by detecting the maximum displacement. The elastic modulus was obtained in the temperature range by Rayleigh's energy method from the detected resonance frequency. At this time, the temperature dependency of elastic modulus was affected by surface oxidation as well as its intrinsic variation: a temperature cycle permanently reduces the resonance frequency. The effect of Si oxidation was analyzed for thermal cycling by applying a simple composite model to the measured frequency data; here the oxide thickness was estimated from the difference in the resonance frequency before and after the temperature cycle, and was confirmed by field-emission scanning electron microscopy. Finally, the temperature coefficient of the elastic modulus of Si in the <110> direction was determined as -64/spl times/10/sup -6/[/spl deg/C/sup -1/]. This value was quite comparable to those reported in previous literatures, and much more so if the specimen temperature is calibrated more exactly.     

Identifying the molecular functions of electron transport proteins using radial basis function networks and biochemical properties

The electron transport proteins have an important role in storing and transferring electrons in cellular respiration, which is the most proficient process through which cells gather energy from consumed food. According to the molecular functions, the electron transport chain components could be formed with five complexes with several different electron carriers and functions. Therefore, identifying the molecular functions in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. This work includes two phases for discriminating electron transport proteins from transport proteins and classifying categories of five complexes in electron transport proteins. In the first phase, the performances from PSSM with AAIndex feature set were successful in identifying electron transport proteins in transport proteins with achieved sensitivity of 73.2%, specificity of 94.1%, and accuracy of 91.3%, with MCC of 0.64 for independent data set. With the second phase, our method can approach a precise model for identifying of five complexes with different molecular functions in electron transport proteins. The PSSM with AAIndex properties in five complexes achieved MCC of 0.51, 0.47, 0.42, 0.74, and 1.00 for independent data set, respectively. We suggest that our study could be a power model for determining new proteins that belongs into which molecular function of electron transport proteins.     

Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey

An artificial neural network (ANN) model was used to estimate the solar radiation parameters for seven cities from Mediterranean region of Anatolia in Turkey. As well known that Turkey is a bridge between Asia and Europe and it lies in a sunny belt, between 36° and 42°N latitudes. Indeed, the country has sufficient solar radiation intensities for solar applications. In order to make estimation of solar radiation, the data from the Turkish State and Meteorological Service were used. Data of 2006 were used for testing and data of 2005, 2007, and 2008 were estimated. Effects of number of input parameters were tested on solar radiation that was output layer. With this aim, number of input layer parameters changed from 2 to 6. The obtained results indicated that the method could be used by researchers or scientists to design high efficiency solar devices. It was also found that number of input parameters was the most effective parameter on estimation of future data on solar radiation.     

Experimental evaluation of sensitivity and non-linearity in polysilicon piezoresistive pressure sensors with different diaphragm sizes

MEMS-based piezoresistive pressure sensors are widely popular due to advantages such as small size, low cost, simple fabrication, and DC output. In this work, the design simulation, fabrication process, and characterization of four pressure sensors with square diaphragms of edge-length 1,060, 1,280, 1,480, and 1,690 µm are reported. Several design principles such as appropriate boundary condition, piezoresistor placement, and fracture stress are considered in the design phase. The sensors have novel shaped polysilicon piezoresistors and equal diaphragm thickness of 50 µm. The sensors are fabricated simultaneously by putting the different designs on the same mask set so that the best design can be determined after characterization. The uncompensated and unamplified output response of the different sensors are reported at three temperatures (?5, 25 and 55 °C). Out of the four sensors with different diaphragm sizes, the sensor with a diaphragm edge length of 1,280 μm is found to have optimum characteristics. For the diaphragm with edge-length of 1,280 µm, in the pressure range of 0–30 Bar, sensitivity of 3.35–3.73 mV/Bar, non-linearity of <0.3 %, and hysteresis of <0.1 % are obtained. The different sensors can be used in the specified pressure range for suitable applications.     

Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning

The objectives of this study were to quantify and analyze differences in laser height and laser intensity distributions of individual trees obtained from airborne laser scanner (ALS) data for different canopy conditions (leaf-on vs. leaf-off) and sensors. It was also assessed how estimated tree height, stem diameter, and tree species were influenced by these differences. The study was based on 412 trees from a boreal forest reserve in Norway. Three different ALS acquisitions were carried out. Leaf-on and leaf-off data were acquired with the Optech ALTM 3100 sensor, and an additional leaf-on dataset was acquired using the Optech ALTM 1233 sensor. Laser echoes located within the vertical projection of the tree crowns were attributed to different echo categories (“first echoes of many”, “single echoes”, “last echoes of many”) and analyzed. The most pronounced changes in laser height distribution from leaf-on to leaf-off were found for the echo categories denoted as “single” and “last echoes of many” where the distributions were shifted towards the ground under leaf-off conditions. The most pronounced change in the intensity distribution was found for “first echoes of many” where the distribution was extremely skewed towards the lower values under leaf-off conditions compared to leaf-on. Furthermore, the echo height and intensity distributions obtained for the two different sensors also differed significantly. Individual tree properties were estimated fairly accurately in all acquisitions with RMSE ranging from 0.76 to 0.84 m for tree height and from 3.10 to 3.17 cm for stem diameter. It was revealed that tree species was an important model term in both and tree height and stem diameter models. A significantly higher overall accuracy of tree species classification was obtained using the leaf-off acquisition (90 vs. 98%) whereas classification accuracy did not differ much between sensors (90 vs. 93%).     

Improved performance of the micro planar double-axis fluxgate sensors with different magnetic core materials and structures

As a classic weak magnetic field sensor, the fluxgate sensors have great potential application in many fields. This paper presents four kinds of the micro planar double-axis fluxgate sensors based on the MEMS technologies, which have different core materials and core structures. The core materials include electroplated permalloy, Co-based amorphous ribbon and Fe-based amorphous ribbon, and the core structures include single-layer open magnetic loop structure and double-layer closed magnetic loop structure. The sensor with closed double-layer Fe-based ribbon core exhibits a best sensitivity of 238 V/T due to reducing the magnetic flux leakage. The results show that the magnetic core with closed magnetic loop, high permeability and high saturation induction density will help increase the sensitivity of the micro double-axis fluxgate sensor.     

Linguistic properties based on American Sign Language isolated word recognition with artificial neural networks using a sensory glove and motion tracker

Sign language (SL), which is a highly visual–spatial, linguistically complete, and natural language, is the main mode of communication among deaf people. Described in this paper are two different American Sign Language (ASL) word recognition systems developed using artificial neural networks (ANN) to translate the ASL words into English. Feature vectors of signing words taken at five time instants were used in the first system, while histograms of feature vectors of signing words were used in the second system. The systems use a sensory glove, Cyberglove™, and a Flock of Birds^® 3-D motion tracker to extract the gesture features. The finger joint angle data obtained from strain gauges in the sensory glove define the hand shape, and the data from the tracker describe the trajectory of hand movement. In both systems, the data from these devices were processed by two neural networks: a velocity network and a word recognition network. The velocity network uses hand speed to determine the duration of words. Signs are defined by feature vectors such as hand shape, hand location, orientation, movement, bounding box, and distance. The second network was used as a classifier to convert ASL signs into words based on features or histograms of these features. We trained and tested our ANN models with 60 ASL words for a different number of samples. These methods were compared with each other. Our test results show that the accuracy of recognition of these two systems is 92% and 95%, respectively.     

Evaluating the effectiveness of biometric sensors and their signal features for classifying human experience in virtual environments

Built environments play an essential role in our day-to-day lives since people spend more than 85% of their times indoors. Previous studies at the conjunction of neuroscience and architecture confirmed the impact of architectural design features on varying human experience, which propelled researchers to study the improvement of human experience in built environments using quantitative methods such as biometric sensing. However, a notable gap in the knowledge persists as researchers are faced with sensors that are commonly used in the neuroscience domain, resulting in a disconnect regarding the selection of effective sensors that can be used to measure human experience in designed spaces. This issue is magnified when considering the variety of sensor signal features that have been proposed and used in previous studies. This study builds on data captured during a series of user studies conducted to measure subjects’ physiological responses in designed spaces using the combination of virtual environments and biometric sensing. This study focuses on the data analysis of the collected sensor data to identify effective sensors and their signal features in classifying human experience. To that end, we used a feature attribution model (i.e., SHAP), which calculates the importance of each signal feature in terms of Shapley values. Results show that electroencephalography (EEG) sensors are more effective as compared to galvanic skin response (GSR) and photoplethysmogram (PPG) (i.e., achieving the highest SHAP values among the three at 3.55 as compared to 0.34 for GSR and 0.21 for PPG) when capturing human experience in alternate designed spaces. For EEG, signal features calculated from the back channels (occipital and parietal areas) were found to possess comparable effectiveness as the frontal channel (i.e., have similar mean SHAP values per channel). In addition, frontal and occipital asymmetry were found to be effective in identifying human experience in designed spaces.     

Design of an exoskeleton with minimized energy consumption based on using elastic and dissipative elements

This study proposes the design of an exoskeleton featuring minimized energy consumption during stand-to-sit and sit-to-stand (STS) motion and walking while carrying a load through the utilization of elastic and dissipative elements. In order to determine which phase and joint can utilize elastic and dissipative elements, we analyzed a human’s walk and STS motions. With this human motion data, we propose an elastic element for hip adduction and abduction (Ad/Ab), series dissipative actuation (SDA) using a semi-active hydraulic system for hip flexion and extension (Fl/Ex) and parallel elastic and series dissipative actuation (PESDA) for the knee Fl/Ex, which is combined with the SDA and the parallel elastic element. The effect of the developed exoskeleton (EXO) with a hip Ad/Ab spring, hip SDA and knee PESDA was evaluated by measuring the user’s ground reaction force (GRF). When wearing the EXO with a hip Ad/Ab spring, hip SDA and knee PESDA, the subject’s GRF was smaller as compared to when the subject was not wearing the EXO while walking and performing the STS motion under a 20-kg load condition, except during the heel strike of the walk motion.

     

Detection of potential fishing zones for neon flying squid based on remote-sensing data in the Northwest Pacific Ocean using an artificial neural network

Ommastrephes bartramii is a short-lived species of squid and reacts rapidly to changes in the regional environmental conditions of the fishing ground. Understanding the preferred range of key environmental variables and predicting potential resource distributions are critical to conserve and manage its resources. Commercial fishery data for the western winter–spring cohort of O. bartramii from Chinese squid-jigging vessels during 2003–2013 were used to evaluate a suitable range of three key environmental variables, sea surface temperature (SST), sea surface height (SSH), and chlorophyll-a (chl-a) concentration, and to explore potential fishing zones (PFZs) using an artificial neural network. The neural interpretation diagram and independent variable relevance analysis indicate that month, latitude, and SST had significant influences on the PFZ distribution of O. bartramii, yielding 21.78%, 23.91%, and 26.04% of contribution rates, respectively. Based on the sensitivity analyses, a high abundance of O. bartramii mainly occurred in the waters between 150°–165° E and 37°–42° N during July to August. Suitable ranges of environmental variables for O. bartramii were 11–18°C for SST, ?10 to 60 cm for SSH, and 0.1–1.7 mg/m³ for chl-a concentration, respectively. The back-propagation network model was well developed and could be used to predict the PFZ with 80% accuracy. The actual fishing grounds coincided with the predicted PFZ, suggesting that the established model of PFZ is effective in forecasting the potential habitat of O. bartramii in the Northwest Pacific Ocean.