Search

In dietary assessment using a single-view food image, an object of known size, such as a checkerboard, is often placed manually in the camera's view as a scale reference to estimate food volume. This traditional scale reference is inconvenient to use because of the manual placement requirement. Consequently, utensils, such as plates and bowls, have been suggested as alternative references. Although these references do not need a manual placement procedure, there is a unique challenge when a dining bowl is used as a reference. Unlike a dining plate, whose shallow shape does not usually block the view of the food, a dining bowl does obscure the food view, and its shape may not be fully observable from the single-view food image. As a result, significant errors may occur in food volume estimation due to the unknown shape of the bowl. To address this challenge, we present a novel method to premeasure both the size and shape of the empty bowl before it is used in a dietary assessment study. In our method, an image is taken with a labeled paper ruler adhered to the interior surface of the bowl, a mathematical model is developed to describe its shape and size, and then an optimization method is used to determine the bowl parameters based on the locations of observed ruler makers from the bowl image. Experimental studies were performed using both simulated and actual bowls to assess the reliability and accuracy of our bowl measurement method. [ABSTRACT FROM AUTHOR]

A relationship between triboelectric charge and contact force for two triboelectric layers is presented, by combining the theories of insulator contact charging and contact mechanics. Experimental verification has been successfully performed using contact-mode triboelectric nanogenerators (TENGs) in two cases: (a) under varying contact forces while keeping the surface roughness profile constant, and (b) under varying surface roughness profiles while keeping the contact force constant. The theory presented here can serve as an important guide in the design of triboelectric systems, particularly of a contact-mode TENG structure for specific applications and self-powered systems. [ABSTRACT FROM AUTHOR]

The need to recharge and eventually replace batteries is increasingly significant for operating a variety of wearable electronic devices. Rapid advances in low power design have stimulated the requirements for portable and sustainable power sources, thus opening the possibility of using human biomechanical energy as a promising alternative power source. Respiration is a unique form of spontaneous and stable source of human biomechanical energy that is currently untapped, and has the potential to be converted to a sustainable power source for low power wearable electronic devices and integrated body sensor networks. However, effectively harvesting respiration energy characterized by low frequency and low force is currently a technological challenge, and cannot be well‐achieved by classical energy harvesting methods. In this work, a triboelectric nanogenerator based on contact–separation mode is demonstrated as a small and light‐weight wearable respiratory energy harvester (wREH). The wREH can also be utilized as a self‐powered respiratory motion sensor capable of tracking rate and depth of respiration. A simple and cost‐effective poly lactic‐co‐glycolic acid patterning technique is utilized to create microdome‐patterned polydimethylsiloxane for increasing the effective contact area of the triboelectric surfaces, thus enhancing power output while achieving higher durability than existing nanosurface structures. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal Sensors titled "Sensing-Based Biomedical Communication and Intelligent Identification for Healthcare." The editorial highlights the importance of integrating sensor technology in healthcare for disease diagnosis and treatment. It discusses the development of various types of sensors and data processing algorithms, as well as advancements in artificial intelligence (AI) and sensing technology. The special issue includes original research articles and reviews that explore the latest developments and challenges in sensor-based information extraction for disease diagnosis and health monitoring. The articles cover topics such as innovative sensors for healthcare applications, processing algorithms for wearable sensors, analysis of public datasets, and the application of machine learning techniques for disease monitoring and management. The editorial concludes by stating that the special issue provides readers with a comprehensive view of the latest prospects of sensors for healthcare applications. [Extracted from the article]

Ambient energy harvesting has gained considerable interest as a potential power source for portable electronic devices and wireless sensor networks since the last decade. Triboelectric generators based on contact electrification and electrostatic induction have recently emerged as a promising mechanical energy harvesting technique. However, due to the complex nature of contact electrification, the underlying mechanism of charge transfer between two contacting surfaces when at least one of them is insulating, remains an unresolved problem in physics. Determining and controlling surface charge density is therefore a significant problem, making prediction of the output voltage of a triboelectric generator a challenge. In addition, structural parameters such as area, gap, and dielectric thickness, which affect the output voltage of the generator, have previously been investigated individually but their interaction effects have not been taken into account. Presented here, for the first time, is a method to design finite element simulations of triboelectric generators using experimentally derived surface charge density as a boundary condition in order to investigate the overall effect of the structural parameters on generator's output voltage. Two metamodels have been established as a result of a 2 3 full factorial design and a central composite design. The models have been experimentally verified, and have shown to predict the output voltage within one order of magnitude. [ABSTRACT FROM AUTHOR]

Hand motion tracking is useful in human-computer interface and many other applications requiring human-machine interactions. In this work, permanent magnets and contactless magnetic sensors are used to track finger motion. A magnet patch is affixed to each fingernail to mark the location and orientation of the fingertip. When fingers move, the combined magnetic fields produced by the magnets at fingertips are recorded by a set of magnetic sensors around a wristband. The recorded data from the sensors are utilized to calculate the magnets' locations and orientations, which can provide estimate of the hand posture based on a geometric hand model. [ABSTRACT FROM PUBLISHER]

Abstract: We investigate the performance of regular systematic low-density generator matrix (LDGM) codes under the majority rule based (MB) iterative decoding algorithm. We derive a recursive form which can be used to extract the error performance of the code. Based on the recursive expression, we derive a tight non-recursive lower bound. These results can serve as efficient tools to evaluate the performance of the code for different degrees. [Copyright &y& Elsevier]

Provides information on a study that examined the function approximation properties of the random neural-network model (GNN). Introduction of the GNN; Related models of GNN; Conclusions.

Introduction Neural spike sorting is curial for neuroscience applications. For example, spike sorting allows designing of efficient human-computer interfaces. Therefore, many investigations, aiming for accurate computer-aided algorithms for spike detection and classification, have emerged. However, many of the available algorithms require human intervention during the classification step. This study proposes decomposition and classification of spike waveforms using dictionary learning. In sparse decomposition, each spike is represented as a unique sparse combination of the dictionary basis vectors. This can lead to interesting sorting performance. Methods Spiking activity was detected using amplitude thresholding and a 2.5-ms window around the peak value. Under the assumption that each neuron has a consistent waveform, the dictionary atoms were learned using training samples. This learned dictionary was used to get a sparse code representation for the test samples. These codes can be used to reconstruct the original spikes and therefore it provides data compression capability. Classification of the neural spikes was done using the sparse coefficients alongside with the reconstruction error. To ensure high discrimination, the dictionary was learned such that the resultant atoms have correspondence to class labels. In other words, the dictionary is structured of sub-dictionaries each corresponds to a different spiking pattern. Subsequently, an optimization problem was solved to make sure that (i) the sub-dictionaries accurately represent the training data, and (ii) the coding coefficients are sparse and minimize the reconstruction error. A testing sample was classified by sparsely coding it over each sub-dictionary. The reconstruction error for each sub-dictionary is computed. An error function is defined for each class as the summation of the reconstruction error and the difference between the sparse code and that class average. The testing sample was assigned to the class that gave the minimum error. The algorithm was tested on four simulated data sets, each contains spiking activities from three different neurons. Approximately 10% of each class data was used to construct the dictionary. Results The reconstruction mean-squared error for all the training samples was 3.49. This confirms the reconstruction power and accuracy of the learned dictionary. We measured the accuracy of our algorithm at different noise levels. The classification accuracy of the proposed method is 97% ± 5%. This accuracy outperforms the existing state-of-the-art algorithms. Conclusion A dictionary learning based spike sorting algorithm has been developed. The results suggest that dictionary-based representation enables us to use the sparse representation of signals without a severe loss of data; besides that, this sparse representation can be also used for classification. This could also enable estimation of missing data points or clipped signals. The reconstruction error can be used to efficiently sort spikes. [ABSTRACT FROM AUTHOR]

In this work, we present a novel coil structure for the design of Wireless Power Transfer (WPT) systems via magnetic resonant coupling. The new coil consists of two layers of flat polygonal windings in square, pentagonal and hexagonal shapes. The double-layer coil can be conveniently fabricated using the print circuit broad (PCB) technology. In our design, we include an angle between the two layers which can be adjusted to change the area of inter-layer overlap. This unique structure is thoroughly investigated with respect to the quality factor Q and the power transfer efficiency (PTE) using the finite element method (FEM). An equivalent circuit is derived and used to explain the properties of the angularly shifted double-layer coil theoretically. Comparative experiments are conducted from which the performance of the new coil is evaluated quantitatively. Our results have shown that an increased shift angle improves the Q-factor, and the optimal PTE is achieved when the angle reaches the maximum. When compared to the pentagonal and hexagonal coils, the square coil achieves the highest PTE due to its lowest parasitic capacitive effects. In summary, our new coil design improves the performance of WPT systems and allows a formal design procedure for optimization in a given application. [ABSTRACT FROM AUTHOR]

There are many tubular or rod-shaped organs and tissues within the human body. A miniature medical implant that wraps around such a biological structure can monitor or modulate its function. In order to provide the wrap-around implant with power, a solenoidal coil coupled wirelessly with a planar coil outside the human body can be used. Unfortunately, there is a serious practical problem that this configuration cannot be realized easily because the implantable solenoidal coil cannot be positioned around the tubular biological structure unless either the structure or the coil is cut and reconnected, which is impermissible in most cases. In addition, when a planner exterior coil is used for wireless power transfer and communication, its maximum magnetic coupling with the implanted solenoidal coil is achieved when the tubular structure is perpendicular to the surface of the body. However, in human anatomy, most tubular/rod structures are oriented horizontally. In order to solve these problems, we present a new flexible coil for the class of wrapped-around implantable devices. Our multilayer coil has specially designed windings in cross patterns. The new coil can be made conveniently in high precision at low cost on a flat substrate using the same technology for making the flexible multilayer printed circuit boards along with miniature sensors and electronic circuits. This allows the implant to be made in a flat form and then wrapped around the biostructure during surgery. We present the design of this new coil, perform theoretical analysis with respect to its wireless power transfer efficiency, discuss the effects of coil parameters, and conduct experiments using constructed miniature prototypes. Our results confirm the validity of the new coil. [ABSTRACT FROM AUTHOR]

Many medical implants need to be designed in the shape of a cylinder (rod), a cuboid or a capsule in order to adapt to a specific site within the human body or facilitate the implantation procedure. In order to wirelessly power these types of implants, a pair of coils, one is located inside the human body and one is outside, is often used. Since most organs such as major muscles, blood vessels, and nerve bundles are anatomically parallel to the body surface, the most desired wireless power transfer (WPT) direction is from the external power transmission pad (a planar coil) to the lateral surface of the implant. However, to obtain optimal coupling, the currently used solenoid coil requires being positioned perpendicular to the body surface, which is often medically or anatomically unacceptable. In this research, a concentric double-helix (DH) coil with an air core is presented for use in implantable devices. Two helical coils are tilted at opposite angles (±45 degrees) to form a cross pattern. The WPT system is designed using the magnetic resonance concept for wireless power transfer (MR-WPT). The power transfer efficiency (PTE) relies on the near-field magnetic coupling which is closely related to the location and orientation of the DH coil. We explain how the novel structure of the DH solenoid magnifies the mutual inductance with the widely adopted circular planner coil and how the PTE is improved in comparison to the case of the conventional solenoid coil. We also study an important case where the double-helix power reception coil is laterally and angularly misaligned with the transmitter. Finally, our computational study using the finite element method and experimental study with actually constructed prototypes are presented which have proven our new double-helix coil design. [ABSTRACT FROM AUTHOR]

According to the special working condition in the commutation region of the traction grid, a novel technique of online core-loss measurement for the electric locomotive onboard traction transformer is proposed to noninvasively monitor the status of the magnetic core in real time. Meanwhile, a new modeling approach is also put forward for the equivalent nonlinear magnetizing inductance LM of the magnetic core. First, a no-load test under 31 kV is implemented on a healthy traction transformer, and the measured transient terminal voltages and currents are then filtered and downsampled by the comprehensive test and fault diagnosis system of electric locomotive traction transformer. Next, the magnetizing inductance LM is modeled as the ratio of “the derivative of main flux linkage λp with respect to time” over “the derivative of magnetizing current im with respect to time.” After that the proposed LM model is verified by comparing with the experimental results and two other models. Finally, the proposed technique for online estimation of core loss is applied on a normal onboard traction transformer and a downscaled testing transformer with different severity of interlaminar fault. The experimental results approximately match the theoretical calculation, and the online estimated values of core loss show sharply rising trend under incipient fault. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a progressive neurodegenerative disease characterized by rigidity, bradykinesia, resting tremor, and postural instability. Rigidity, defined as an increased resistance to passive movement of a joint, progresses faster than other motor signs in PD. Rigidity is attributable to both exaggerated neural reflex and altered muscle mechanical properties. However, little is known about the contributions of individual components to rigidity. Further, there is no evidence regarding the effects of dopaminergic medication on individual components. Objectives of this study were to quantify the contributions of neural reflexes and intrinsic muscle properties to rigidity and investigate the effects of medication on each contributing component. Joint torque and muscle activities of the wrist in 14 patients and 14 controls were measured during externally induced movements. Each subject with PD was tested in Off- and On-medication states. A system identification technique was applied to differentiate and quantify the neural reflex and intrinsic mechanical components. A mixed model of ANOVA was performed to compare the differences between the two components of rigidity for both groups, and to compare between the Off- and On-medication states for patients. The results showed that reflex and intrinsic components are comparable ( p > 0.05), and both are enhanced in subjects with PD than in the controls ( p < 0.05). Medication decreased the reflex component of rigidity ( p < 0.01). It is concluded that both reflex and intrinsic factors are responsible for rigidity. Present findings are clinically significant as they may provide guidance in development of effective therapeutic interventions. [ABSTRACT FROM AUTHOR]

An unhealthy diet is strongly linked to obesity and numerous chronic diseases. Currently, over two-thirds of American adults are overweight or obese. Although dietary assessment helps people improve nutrition and lifestyle, traditional methods for dietary assessment depend on self-report, which is inaccurate and often biased. In recent years, as electronics, information, and artificial intelligence (AI) technologies advanced rapidly, image-based objective dietary assessment using wearable electronic devices has become a powerful approach. However, research in this field has been focused on the developments of advanced algorithms to process image data. Few reports exist on the study of device hardware for the particular purpose of dietary assessment. In this work, we demonstrate that, with the current hardware design, there is a considerable risk of missing important dietary data owing to the common use of rectangular image screen and fixed camera orientation. We then present two designs of a new camera system to reduce data loss by generating circular images using rectangular image sensor chips. We also present a mechanical design that allows the camera orientation to be adjusted, adapting to differences among device wearers, such as gender, body height, and so on. Finally, we discuss the pros and cons of rectangular versus circular images with respect to information preservation and data processing using AI algorithms. [ABSTRACT FROM AUTHOR]

Image-assisted dietary assessment has become popular in dietary monitoring studies in recent years. However, food volume estimation is still a challenging problem due to the lack of 3D information in a 2D image and the occlusion of the food by itself or container (e.g., bowl, cup). This study aims to investigate the relationship between the observable surface of food in a bowl and a normalized index (i.e., bowl fullness) to represent its volume. A mathematical model is established for describing different shapes of bowls, and a convenient experimental method is proposed to determine the bowl shape. An image feature called Food Area Ratio (FAR) is used to estimate the volume of food in a bowl based on the relationship between bowl fullness and the FAR calculated from the image. Both simulations and experiments with real food/liquid demonstrate the feasibility and accuracy of the proposed approach. • Developing a new mathematical model for describing shapes of dinning bowls. • Using a unitless index (i.e., fullness) to represent the volume of food in a bowl. • Estimating the volume of food in a bowl based on analysis of visible food surface. [ABSTRACT FROM AUTHOR]

• This article develops a symbolic, reduced order, dq-based dynamic model of inverter based microgrids. • The model utilizes less states and accurately reflects the dynamics of a full order, detailed model compared to the literature. • The model developed can be used to conveniently investigate controller design and tuning, parameter variations and robust stability analysis under parametric uncertainties. • The model accounts for cable impedances between inverters. • The model can be useful in expanding secondary controller design literature and applications. Improving the design of primary and secondary level controllers of microgrids is of critical importance to microgrid deployment. Controller design and tuning rely heavily upon adequate, dynamic models of microgrids. In modeling of microgrids, there is always a compromise between accuracy and complexity as indicated by model order. Many of the currently available models found in the literature are reflective of system behavior but with high-order or less representative of system behavior with a low-order mathematical representation. In this paper, a reduced order, linear, inverter based, microgrid model based on the dq reference frame is developed. The order of the model is 2 n (for n distributed generators (DG)) lower than the least of the orders of models suited for secondary controller design currently available. The system representation includes the commonly neglected cable impedance dynamics and the interconnection coupling between DG units. Static and dynamic model equations are utilized to capture strong correlations between the linear, reduced order (6 n - 1 states for n DGs) model and non-linear system model in the transient (low frequency) and steady-state regions. The model developed is validated via a time domain simulation in the MATLAB/SIMULINK environment. The effect of the interconnection coupling between DGs on the eigenvalues is also presented. [ABSTRACT FROM AUTHOR]

The service industry has become a primary point of growth in most countries. However, the current service business is largely constrained by human factors and lacks automated and quantitative techniques for operation and decision making. In this paper, we propose an optimal-control-based decision-making model that facilitates performance analysis and strategy planning for service enterprises. Using approximate dynamic programming, we are able to handle the model’s complexity and obtain a near-optimal solution for the decision making of employee management, advertisement, and asset investment. We validate our model and approach in a business simulation practice. Furthermore, we propose a consulting methodology based on the optimal-control model and describe its application in service consulting practices. [ABSTRACT FROM PUBLISHER]

Objective: Accurate measurements of food volume and density are often required as 'gold standards' for calibration of image-based dietary assessment and food database development. Currently, there is no specialised laboratory instrument for these measurements. We present the design of a new volume of density (VD) meter to bridge this technological gap.Design: Our design consists of a turntable, a load sensor, a set of cameras and lights installed on an arc-shaped stationary support, and a microcomputer. It acquires an array of food images, reconstructs a 3D volumetric model, weighs the food and calculates both food volume and density, all in an automatic process controlled by the microcomputer. To adapt to the complex shapes of foods, a new food surface model, derived from the electric field of charged particles, is developed for 3D point cloud reconstruction of either convex or concave food surfaces.Results: We conducted two experiments to evaluate the VD meter. The first experiment utilised computer-synthesised 3D objects with prescribed convex and concave surfaces of known volumes to investigate different food surface types. The second experiment was based on actual foods with different shapes, colours and textures. Our results indicated that, for synthesised objects, the measurement error of the electric field-based method was <1 %, significantly lower compared with traditional methods. For real-world foods, the measurement error depended on the types of food volumes (detailed discussion included). The largest error was approximately 5 %.Conclusion: The VD meter provides a new electronic instrument to support advanced research in nutrition science. [ABSTRACT FROM AUTHOR]

Polyphase permanent magnet synchronous motors exhibit outstanding advantages over the traditional three-phase traction motors in electrified transportation applications. This article introduces a novel direct torque model predictive control technique aiming at developing a highly reliable, loss-minimizing, and energy-efficient five-phase permanent magnet synchronous motor drive. Leveraging the underlying constrained optimization process, the direct torque model predictive control approach optimizes the torque delivery and speed regulation quality, improves the flux and torque ripples, reduces higher order current harmonics, minimizes motor drive losses, and boosts power efficiency of the electric powertrain. dSPACE DS1104 hardware-in-the-loop and computer simulation studies are jointly utilized to verify the effectiveness and robustness of the proposed direct torque model predictive control approach in polyphase permanent magnet synchronous motor drive applications. [ABSTRACT FROM AUTHOR]

A large circular transmitter (power pad) is often utilized for inductively wireless charging so that the power receiver can position itself freely within the power pad. However, the uneven magnetic field distribution leads to the impedance mismatch and the variable transferred power due to the misalignment between a receiving coil and a relatively large transmitter coil. In this paper, the effects of the turn numbers, trace spacing, and coil size on the uniformity of magnetic field distribution over a planar spiral coil (PSC) are investigated based on an analytical model. Then, the genetic algorithm is used to optimize the trace width and spacing of a ten-turn PSC with the fixed outer and inner diameters, whereas the magnetic field is numerically calculated by HFSS software. The geometrically optimal PSC is obtained with the trace width approximately equal to trace spacing for each turn by minimizing the coefficient of variation (COV) of magnetic field within an effective charging area over the coil. The results showed that the simulated and measured COVs of magnetic field were 0.130 and 0.121, respectively, which are obviously less than that of the uniformly spaced regular coil and show a little less than that of the optimally spaced coil with fixed trace width. Therefore, the trace spacing is a major consideration to achieve the uniform magnetic field, whereas the trace width variation is applied to refine the evenness of field. It is also expected that the proposed GA-based optimization is well applied to design other Txs with conformable shape in inductively WPT systems for stable power delivery regardless of the receiver positions. [ABSTRACT FROM AUTHOR]

Semantic segmentation is a key step in scene understanding for autonomous driving. Although deep learning has significantly improved the segmentation accuracy, current high-quality models such as PSPNet and DeepLabV3 are inefficient given their complex architectures and reliance on multi-scale inputs. Thus, it is difficult to apply them to real-time or practical applications. On the other hand, existing real-time methods cannot yet produce satisfactory results on small objects such as traffic lights, which are imperative to safe autonomous driving. In this paper, we improve the performance of real-time semantic segmentation from two perspectives, methodology and data. Specifically, we propose a real-time segmentation model coined Narrow Deep Network (NDNet) and build a synthetic dataset by inserting additional small objects into the training images. The proposed method achieves 65.7% mean intersection over union (mIoU) on the Cityscapes test set with only 8.4G floating-point operations (FLOPs) on $1024\times 2048$ inputs. Furthermore, by re-training the existing PSPNet and DeepLabV3 models on our synthetic dataset, we obtained an average 2% mIoU improvement on small objects. [ABSTRACT FROM AUTHOR]

Objective: To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment.Design: To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network.Results: A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively.Conclusions: The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns. [ABSTRACT FROM AUTHOR]

Cervical auscultation is a method for assessing swallowing performance. However, its ability to serve as a classification tool for a practical clinical assessment method is not fully understood. In this study, we utilized neural network classification methods in the form of Deep Belief networks in order to classify swallows. We specifically utilized swallows that did not result in clinically significant aspiration and classified them on whether they originated from healthy subjects or unhealthy patients. Dual-axis swallowing vibrations from 1946 discrete swallows were recorded from 55 healthy and 53 unhealthy subjects. The Fourier transforms of both signals were used as inputs to the networks of various sizes. We found that single and multi-layer Deep Belief networks perform nearly identically when analyzing only a single vibration signal. However, multi-layered Deep Belief networks demonstrated approximately a 5–10% greater accuracy and sensitivity when both signals were analyzed concurrently, indicating that higher-order relationships between these vibrations are important for classification and assessment. [ABSTRACT FROM AUTHOR]

Wireless Power Transfer (WPT) and wireless data communication are both important problems of research with various applications, especially in medicine. However, these two problems are usually studied separately. In this work, we present a joint study of both problems. Most medical electronic devices, such as smart implants, must have both a power supply to allow continuous operation and a communication link to pass information. Traditionally, separate wireless channels for power transfer and communication are utilized, which complicate the system structure, increase power consumption and make device miniaturization difficult. A more effective approach is to use a single wireless link with both functions of delivering power and passing information. We present a design of such a wireless link in which power and data travel in opposite directions. In order to aggressively miniaturize the implant and reduce power consumption, we eliminate the traditional multi-bit Analog-to-Digital Converter (ADC), digital memory and data transmission circuits all together. Instead, we use a pulse stream, which is obtained from the original biological signal, by a sigma-delta converter and an edge detector, to alter the load properties of the WPT channel. The resulting WPT signal is synchronized with the load changes therefore requiring no memory elements to record inter-pulse intervals. We take advantage of the high sensitivity of the resonant WPT to the load change, and the system dynamic response is used to transfer each pulse. The transient time of the WPT system is analyzed using the coupling mode theory (CMT). Our experimental results show that the memoryless approach works well for both power delivery and data transmission, providing a new wireless platform for the design of future miniaturized medical implants. [ABSTRACT FROM AUTHOR]

In analyzing the human–machine interaction (HMI), a human-centered approach is needed to address the potential and limitation of human control, especially in the control of high-order or unstable systems. However, there is no quantitative measure of the human performance or cognitive workload in these difficult HMI tasks. We propose to characterize the HMI as information flows quantified by the information-transmission rate in bits per second (b/s). Using information- and control-theoretic approaches, we derive the minimum rates of information transmission in manual control required by any deterministic controller to stabilize the feedback system. Furthermore, we suggest a method adopted from time-series analysis to estimate the information-transmission rate from human experiments. We show that the relationship between the empirically estimated information rates and the minimum bounds allows for the quantitative indication of the potential and limitation of human manual control. We illustrate our method in the control of an inverted pendulum with time delays. [ABSTRACT FROM PUBLISHER]

This paper proposes an adaptive control architecture for maximum power point tracking (MPPT) in photovoltaic systems. MPPT technologies have been used in photovoltaic systems to deliver the maximum available power to the load under changes of the solar insolation and ambient temperature. To improve the performance of MPPT, this paper develops a two-level adaptive control architecture that can reduce complexity in system control and effectively handle the uncertainties and perturbations in the photovoltaic systems and the environment. The first level of control is ripple correlation control (RCC), and the second level is model reference adaptive control (MRAC). By decoupling these two control algorithms, the system achieves MPPT with overall system stability. This paper focuses mostly on the design of the MRAC algorithm, which compensates the underdamped characteristics of the power conversion system. The original transfer function of the power conversion system has time-varying parameters, and its step response contains oscillatory transients that vanish slowly. Using the Lyapunov approach, an adaption law of the controller is derived for the MRAC system to eliminate the underdamped modes in power conversion. It is shown that the proposed control algorithm enables the system to converge to the maximum power point in milliseconds. [ABSTRACT FROM AUTHOR]

Wireless power transfer via magnetically resonant coupling is a new technology to deliver power over a relatively long distance. Here, we present a mat-based design to wirelessly power moving targets based on this technology. Our design is specifically applied to transcutaneously power medical implants within free-moving laboratory animals. Our system comprises a driver coil array, a hexagonally packed transmitter mat, a receiver coil, and a load coil, and generates a nearly flat magnetic distribution over a defined area to produce an approximately constant power output independent of the location of the receiver coil. This paper also describes a novel power receiver coil design of the same shape as the exterior of the implant, allowing for maximum magnetic coupling, eliminating the space restrictions due to the coil within the implant, and matching the resonant frequencies of the implant and the transmitter coil. Our new transmitter and receiver designs significantly reduce the size of a biomedical implant and may provide a lifetime power supply to implanted circuits without the need for an internal battery. Our designs are also useful in various other applications involving moving targets, such as part of a robot or a vehicle. [ABSTRACT FROM PUBLISHER]

Abstract: A new technique to extract and evaluate physical activity patterns from image sequences captured by a wearable camera is presented in this paper. Unlike standard activity recognition schemes, the video data captured by our device do not include the wearer him/herself. The physical activity of the wearer, such as walking or exercising, is analyzed indirectly through the camera motion extracted from the acquired video frames. Two key tasks, pixel correspondence identification and motion feature extraction, are studied to recognize activity patterns. We utilize a multiscale approach to identify pixel correspondences. When compared with the existing methods such as the good features detector and the speed-up robust feature (SURF) detector, our technique is more accurate and computationally efficient. Once the pixel correspondences are determined which define representative motion vectors, we build a set of activity pattern features based on motion statistics in each frame. Finally, the physical activity of the person wearing a camera is determined according to the global motion distribution in the video. Our algorithms are tested using different machine learning techniques such as the K-nearest neighbor (KNN), naive Bayesian and support vector machine (SVM). The results show that many types of physical activities can be recognized from field acquired real-world video. Our results also indicate that, with a design of specific motion features in the input vectors, different classifiers can be used successfully with similar performances. [Copyright &y& Elsevier]

Abstract: Conventional IEEE 802.11 medium access control (MAC) protocol discourages simultaneous transmission to avoid collisions. With fast advances in physical layer technologies, multi-user detection (MUD) capable receivers which can detect multiple frames from different users simultaneously become available. If we are to utilize them in today''s wireless LAN, however, it is not entirely clear how we should change the MAC and how much benefit is available and can be obtained by doing so. The primary objective of this paper is to investigate such questions. We approach this objective by developing a new throughput expression for 802.11 distributed coordination function (DCF). The derived expression has been verified in simulation. We show that significant throughput gain can be garnered with slight modification in 802.11 DCF. [Copyright &y& Elsevier]

Knowing the amounts of energy and nutrients in an individual's diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl. [ABSTRACT FROM AUTHOR]

This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies. [ABSTRACT FROM AUTHOR]

Traditional protection methods such as over-current or under-voltage methods are unreliable in inverter-based microgrid applications. This is primarily due to low fault current levels because of power electronic interfaces to the distributed energy resources (DER), and IEEE1547 low-voltage-ride-through (LVRT) requirements for renewables in microgrids. However, when faults occur in a microgrid feeder, system changes occur which manipulate the internal circuit structure altering the system dynamic relationships. This observation establishes the basis for a proposed, novel, model-based, communication-free fault detection technique for inverter-based microgrids. The method can detect faults regardless of the fault current level and the microgrid mode of operation. The approach utilizes fewer measurements to avoid the use of a communication system. Protecting the microgrid without communication channels could lead to blinding (circuit breakers not tripping for faults) or nuisance tripping (tripping incorrectly). However, these events can be avoided with proper system design, specifically with appropriately sized system impedance. Thus, a major contribution of this article is the development of a mathematical framework to analyze and avoid blinding and nuisance tripping scenarios by quantifying the bounds of the proposed fault detection technique. As part of this analysis, the impedance based constraints for microgrid system feeders are included. The performance of the proposed technique is demonstrated in the MATLAB/SIMULINK (MathWorks, Natick, MA, USA) simulation environment on a representative microgrid architecture showing that the proposed technique can detect faults for a wide range of load impedances and fault impedances. [ABSTRACT FROM AUTHOR]