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Multispectral analysis of coregistered multiparametric magnetic resonance (MR) images provides a powerful method for tissue phenotyping and segmentation. Acquisition of a sufficiently varied set of multicontrast MR images and parameter maps to objectively define multiple normal and pathologic tissue types can require long scan times. Accelerated MRI on clinical scanners with multichannel receivers exploits techniques such as parallel imaging, while accelerated preclinical MRI scanning must rely on alternate approaches. In this work, tumor‐bearing mice were imaged at 7 T to acquire k‐space data corresponding to a series of images with varying T1‐, T2‐ and T2*‐weighting. A joint reconstruction framework is proposed to reconstruct a series of T1‐weighted images and corresponding T1 maps simultaneously from undersampled Cartesian k‐space data. The ambiguity introduced by undersampling was resolved by using model‐based constraints and structural information from a reference fully sampled image as the joint total variation prior. This process was repeated to reconstruct T2‐weighted and T2*‐weighted images and corresponding maps of T2 and T2* from undersampled Cartesian k‐space data. Validation of the reconstructed images and parameter maps was carried out by computing tissue‐type maps, as well as maps of the proton density fat fraction (PDFF), proton density water fraction (PDwF), fat relaxation rate (R2f*) and water relaxation rate (R2w*) from the reconstructed data, and comparing them with ground truth (GT) equivalents. Tissue‐type maps computed using 18% k‐space data were visually similar to GT tissue‐type maps, with dice coefficients ranging from 0.43 to 0.73 for tumor, fluid adipose and muscle tissue types. The mean T1 and T2 values within each tissue type computed using only 18% k‐space data were within 8%–10% of the GT values from fully sampled data. The PDFF and PDwF maps computed using 27% k‐space data were within 3%–15% of GT values and showed good agreement with the expected values for the four tissue types. [ABSTRACT FROM AUTHOR]

Open Challenges: Continuous monitoring of fundamental cardiovascular hemodynamic parameters is essential to accomplish critical care diagnostics. Today's standard of care measures these critical parameters using multiple monitoring technologies. These state-of-the-art technologies require expensive instrumentation and complex infrastructure. Therefore, it is challenging to use current technologies to accomplish monitoring in a low resource setting. Objective: In order to address the challenges caused by having to use multiple monitoring systems, a point of care monitoring device was developed in this work to provide multiple critical parameters by uniquely measuring the hemodynamic process. Methods: To demonstrate the usability of this novel catheter multiscope, a feasibility study was performed using an animal model. The developed measurement system first acquires the dynamics of blood flow through a minimally invasive catheter. Then, a signal processing framework was developed to characterize the blood flow dynamics and to obtain critical parameters such as heart rate, respiratory rate, and blood pressure. The framework used to extract the physiological data corresponding to the acoustic field of the blood flow consisted of a noise cancellation method and wavelet-based source separation. Results: The preliminary results of the acoustic pressure field of the blood flow revealed the presence of acoustic heart and respiratory pulses. A unique framework was also developed to extract continuous blood pressure from the acoustic pressure field of the blood flow. Finally, the computed heart and respiratory rates, systolic and diastolic pressures were benchmarked with actual values measured using conventional devices to validate the hypothesis. Conclusion: The results confirm that catheter multiscope can provide multiple critical parameters with clinical reliability. Significance: A novel critical care monitoring system has been developed to accurately measure heart rate, respiratory rate, systolic and diastolic pressures from the blood flow dynamics. [ABSTRACT FROM AUTHOR]

The standard for measuring solar irradiance utilizes the units of watts per meter squared (W/m²). Irradiance meters are both costly and limited in the ability to measure low irradiance values. With a lower cost and higher sensitivity in low light conditions, light meters measure luminous flux per unit area (illuminance) utilizing the units of lumens per meter squared or lux (lx). An effective conversion factor between W/m² and lx would enable the use of light meters to evaluate photovoltaic performance under low solar irradiance conditions. A survey of the literature found no definitive and readily available "rule of thumb" conversion standard between solar irradiance and illuminance. Easy-to-find Internet sources contain conflicting and widely varying values ranging from 688449 to 21000 lx for 1000 W/m² (1 Sun) of solar irradiance. Peer-reviewed literature contains Luminous Efficacy equivalent values ranging from 21 to 131 lx per W/m². This manuscript explores the relationship and establishes a theoretical and laboratory measurement guide for the conversion between solar irradiance and illuminance. The conversion factor includes standards data, equipment calibration accuracy, and uncertainty estimates. Solar Irradiance of 1 Sun (1000 W/m²) for an LED-based solar simulator is (116 ± 3) klx and (122 ± 1) klx for outdoor sunlight. [ABSTRACT FROM AUTHOR]

The aim of this paper is to reduce the energy consumption of a humanoid by analyzing electrical power as input to the robot and mechanical power as output. The analysis considers motor dynamics during standing up and sitting down tasks. The motion tasks of the humanoid are described in terms of joint position, joint velocity, joint acceleration, joint torque, center of mass (CoM) and center of pressure (CoP). To reduce the complexity of the robot analysis, the humanoid is modeled as a planar robot with four links and three joints. The humanoid robot learns to reduce the overall motion torque by applying Q-Learning in a simulated model. The resulting motions are evaluated on a physical NAO humanoid robot during standing up and sitting down tasks and then contrasted to a pre-programmed task in the NAO. The stand up and sit down motions are analyzed for individual joint current usage, power demand, torque, angular velocity, acceleration, CoM and CoP locations. The overall result is improved energy efficiency between 25-30% when compared to the pre-programmed NAO stand up and sit down motion task. [ABSTRACT FROM AUTHOR]

New-generation mobile devices will inevitably be employed within the realm of ubiquitous sensing. In particular, smartphones have been increasingly used for human activity recognition (HAR)-based studies. It is believed that recognizing human-centric activity patterns could accurately enough give a better understanding of human behaviors. Further, such an ability could have a chance to assist individuals to enhance the quality of their lives. However, the integration and realization of HAR-based mobile services stand as a significant challenge on resource-constrained mobile-embedded platforms. In this manner, this paper proposes a novel discrete-time inhomogeneous hidden semi-Markov model (DT-IHS-MM)-based generic framework to address a better realization of HAR-based mobile context awareness. In addition, we utilize power-efficient sensor management strategies by providing three intuitive methods and constrained Markov decision process (CMDP), as well as partially observable Markov decision process (POMDP)-based optimal methods. Moreover, a feedback control mechanism is integrated to balance the tradeoff between accuracy in context inference and power consumption. In conclusion, the proposed sensor management methods achieve a 40% overall enhancement in the power consumption caused by the physical sensor with respect to the overall 85–90% accuracy ratio due to the provided adaptive context inference framework. [ABSTRACT FROM PUBLISHER]

Energy consumption is a major concern in context-aware smartphone sensing. This paper first studies mobile device-based battery modeling, which adopts the kinetic battery model (KiBaM), under the scope of battery non-linearities with respect to variant loads. Second, this paper models the energy consumption behavior of accelerometers analytically and then provides extensive simulation results and a smartphone application to examine the proposed sensor model. Third, a Markov reward process is integrated to create energy consumption profiles, linking with sensory operations and their effects on battery non-linearity. Energy consumption profiles consist of different pairs of duty cycles and sampling frequencies during sensory operations. Furthermore, the total energy cost by each profile is represented by an accumulated reward in this process. Finally, three different methods are proposed on the evolution of the reward process, to present the linkage between different usage patterns on the accelerometer sensor through a smartphone application and the battery behavior. By doing this, this paper aims at achieving a fine efficiency in power consumption caused by sensory operations, while maintaining the accuracy of smartphone applications based on sensor usages. More importantly, this study intends that modeling the battery non-linearities together with investigating the effects of different usage patterns in sensory operations in terms of the power consumption and the battery discharge may lead to discovering optimal energy reduction strategies to extend the battery lifetime and help a continual improvement in context-aware mobile services. [ABSTRACT FROM AUTHOR]

This paper presents a novel framework that includes an inhomogeneous (time-variant) Hidden Markov Model (HMM) and learning from data concepts. The framework either recognizes or estimates user contextual inferences called ‘user states’ within the concept of Human Activity Recognition (HAR) for future context-aware applications. Context-aware applications require continuous data acquisition and interpretation from one or more sensor reading(s). Therefore, device battery lifetimes need to be extended due to the fact that constantly running built-in sensors deplete device batteries rapidly. In this sense, a framework is constructed to fulfill requirements needed by applications and to prolong device battery lifetimes. The ultimate goal of this paper is to present an accurate user state representation model, and to maximize power efficiency while the model operates. Most importantly, this research intends to create and clarify a generic framework to guide the development of future context-aware applications. Moreover, topics such as user profile adaptability and variant sensory sampling operations are examined. The proposed framework is validated by simulations and implemented in a HAR-based application by the smartphone accelerometer. According to the results, the proposed framework shows an increase in power efficiency of 60% for an accuracy range from 75% up to 96%, depending on user profiles. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper introduces a new tool to approach the effects of a diffraction grating and its interaction with other optical and electronic components in a light wavelength analysis system. The model's theoretical background is described. This model is intended for educational purposes and it is referred to a miniaturized spectrophotometer. © 2011 Wiley Periodicals, Inc. Comput Appl Eng Educ 22:187-194, 2014; View this article online at ; DOI [ABSTRACT FROM AUTHOR]

The ever increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor designs and deployment, have enabled smart devices to recognize daily human-based actions, activities, and interactions. Therefore, inferring a vast variety of user-device-based activities from diverse contexts obtained by a series of sensory observations has drawn much interest to the research area of ubiquitous sensing. The existence and awareness of context provides the capability of being conscious of physical environments or situations around users, and this allows network services to respond proactively and intelligently based on such awareness. Hence, with the evolution of smartphones, software developers have been empowered to create context-aware applications for recognizing human-centric or community based innovative social and cognitive activities in any situation and at any location. However, the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth compared to the capabilities of PCs and servers. Also, power is a major restriction on implementation of context-aware applications. Mobile device batteries do not last a long time while operating sensor(s) constantly. To this end, this article extensively surveys the emerging concepts of context awareness in mobile platforms by providing up-to-date literature and future research directives. We also point out the challenges faced in this regard and enlighten them by offering possible solutions. [ABSTRACT FROM PUBLISHER]

Doppler spectrum in an aeronautical channel is modeled with dual-Doppler shift instead of classical Jakes model. Orthogonal frequency division multiplexing (OFDM)-based systems are sensitive to Doppler shifts/spread, since the time variation of the channel causes inter-carrier interference (ICI). ICI analysis is provided here for OFDM-based systems in the aeronautical channel. The effect of ICI on the received signal is presented and its power is derived. As compared with terrestrial channels, where ICI is generally overcome by increasing the subcarrier spacing and bounding the normalized Doppler frequency (NDF), we propose to mitigate the effect of Doppler shifts in aeronautical channels, as follows. First, we use parametric spectrum estimation methods to extract the Doppler shifts by exploiting the predictable number of paths. Then, a beamforming-based method is introduced to resolve the incoming rays by separating them individually. Finally, paths are Doppler-compensated and combined using diversity combining techniques. Computer simulations are performed to provide numerical results. It is shown that a mean square error (MSE) performance ofis achieved with the parametric estimation methods, and bit error rate (BER) performance approaching the no-Doppler scenario is obtained with the beamforming-based mitigation method. [ABSTRACT FROM PUBLISHER]

Arrhythmias are the most common cause of death associated with sudden death and are common in US and worldwide. Cardiac resynchronization therapy (CRT), evolving from pacemakers and development of implantable cardioverter defibrillator (ICD), has been adopted for therapeutic use and demonstrated benefits in patients over the years due to its design and intricate functionality. Recent research has been focused on significant design improvement and efforts are dedicated toward device size reduction, weight and functionality in commercially available ICD's since its invention in the 1960's. Commercially available CRT-D has shown advancement on both clinical and technical side. However, improved focus is required on the device miniaturization, technologically supported and integrated wireless based system for real time heart monitoring electrocardiogram (ECG). In the present report a concise overview for the state-of-the art technology in ICDs and avenues for future development are presented. A unique perspective is also included for ICD device miniaturization and integration of flexible sensing array. Sensor array integration along with its capabilities for identifying localized arrhythmia detection and targeted stimulation for enhancing ICD device capabilities is reviewed. [ABSTRACT FROM AUTHOR]

This brief presents a backstepping controller design for helicopters. The controller objective is for the helicopter to autonomously track predefined position and yaw reference trajectories. The incorporation of nested saturation feedback functions in the backstepping design preserves the helicopter's motion and power physical constraints. The intermediate control signals related to the attitude dynamics exploit the structural properties of the rotation matrix and are enhanced with terms that guarantee that the helicopter will not overturn while tracking the desired position trajectory. The attitude dynamics are rendered exponentially stable while the translational dynamics are globally asymptotically stable. Numerical simulations illustrate the applicability of the proposed design. [ABSTRACT FROM PUBLISHER]

Umbilical artery Doppler velocimetry waveform notching has long been associated with umbilical cord abnormalities, such as distortion, torsion, and/or compression (i.e., constriction). The physical mechanism by which the notching occurs has not been elucidated. Flow velocity waveforms (FVWs) from two-dimensional pulsatile flows in a constricted channel approximating a compressed umbilical cord are analyzed, leading to a clear relationship between the notching and the constriction. Two flows with an asymmetric, semi-elliptical constriction are computed using a stabilized finite-element method. In one case, the constriction blocks 75% of the flow passage, and in the other the constriction blocks 85%. Channel width and prescribed flow rates at the channel inflow are consistent with typical cord diameters and flow rates reported in the literature. Computational results indicate that waveform notching is caused by flow separation induced by the constriction, giving rise to a vortex (core) wave and associated eddies. Notching in FVWs based on centerline velocity (centerline FVW) is directly related to the passage of an eddy over the point of measurement on the centerline. Notching in FVWs based on maximum cross-sectional velocity (envelope FVW) is directly related to acceleration and deceleration of the fluid along the vortex wave. Results show that notching in envelope FVW is not present in flows with less than a 75% constriction. Furthermore, notching disappears as the vortex wave is attenuated at distances downstream of the constriction. In the flows with 75 and 85% constriction, notching of the envelope FVW disappears at ∼3.8 and ∼4.3 cm (respectively) downstream of the constriction. These results are of significant medical importance, given that envelope FVW is typically measured by commercial Doppler systems. [ABSTRACT FROM AUTHOR]

Chemical-mechanical planarization is a critical abrasive process in the production of semiconductor devices used for polishing the surface of wafers. The characterization of an IC wafer-polishing pad, in terms of its density, is crucial for planarization efficiency. Most of the currently available procedures for pad characterization are typically destructive in nature. This paper, however, demonstrates through statistical design of experiments the effectiveness of ultrasound technology to analyze the underlying pad structure in a nondestructive regime. Moreover, the impact that physical factors such as temperature and humidity could have on the structure and the ultrasonic characterization of the pad are studied. The results from a single factor and a multi- factor analysis of the different sections of the pad are discussed. [ABSTRACT FROM AUTHOR]