Characterizing the Effective Bandwidth of Nonlinear Vibratory Energy Harvesters Possessing Multiple Stable Equilibria

In the last few years, advances in micro-fabrication technologies have lead to the development of low-power electronic devices spanning critical fields related to sensing, data transmission, and medical implants. Unfortunately, effective utilization of these devices is currently hindered by their reliance on batteries. In many of these applications, batteries may not be a viable choice as they have a fixed storage capacity and need to be constantly replaced or recharged. In light of such challenges, several novel concepts for micro-power generation have been recently introduced to harness, otherwise, wasted ambient energy from the environment and maintain these low-power devices. Vibratory energy harvesting is one such concept which has received significant attention in recent years. While linear vibratory energy harvesters have been well studied in the literature and their performance metrics have been established, recent research has focused on deliberate introduction of stiffness nonlinearities into the design of these devices. It has been shown that, nonlinear energy harvesters have a wider steady-state frequency bandwidth as compared to their linear counterparts, leading to the premise that they can used to improve performance, and decrease sensitivity to variations in the design and excitation parameters. This dissertation aims to investigate this premise by developing an analytical framework to study the influence of stiffness nonlinearities on the performance and effective bandwidth of nonlinear vibratory energy harvesters. To achieve this goal, the dissertation is divided into three parts. The first part investigates the performance of bi-stable energy harvesters possessing a symmetric quartic potential energy function under harmonic excitations and carries out a detailed analysis to define their effective frequency bandwidth. The second part investigates the relative performance of mono- and bi-stable energy harvesters under optimal electric loading conditions. The third part investigates the response and performance of tri-stable energy harvesters possessing a symmetric hexic potential function under harmonic excitations and provides a detailed analysis to approximate their effective frequency bandwidth. As a platform to achieve these objectives, a piezoelectric nonlinear energy harvester consisting of a uni-morph cantilever beam is considered. Stiffness nonlinearities are introduced into the harvester’s design by applying a static magnetic field near the tip of the beam. Experimental studies performed on the proposed harvester are presented to validate some of the theoretical findings. Since nonlinear energy harvesters exhibit complex and non-unique responses, it is demonstrated that a careful choice of the design parameters namely, the shape of the potential function and the electromechanical coupling is necessary to widen their effective frequency bandwidth. Specifically, it is shown that, decreasing the electromechanical coupling and/or designing the potential energy function to have shallow wells, widens the effective frequency bandwidth for a given excitation level. However, this comes at the expense of the output power which decreases under these design conditions. It is also shown that the ratio between the mechanical period and time constant of the harvesting circuit has negligible influence on the effective frequency bandwidth but has considerable effect on the associated magnitude of the output power

Least Squares Fitting of Analytic Primitives on a GPU

Metrology systems take coordinate information directly from the surface of a manufactured part and generate millions of (X, Y, Z) data points. The inspection process often involves fitting analytic primitives such as sphere, cone, torus, cylinder and plane to these points which represent an object with the corresponding shape. Typically, a least squares fit of the parameters of the shape to the point set is performed. The least squares fit attempts to minimize the sum of the squares of the distances between the points and the primitive. The objective function however, cannot be solved in the closed form and numerical minimization techniques are required to obtain the solution. These techniques as applied to primitive fitting entail iteratively solving large systems of linear equations generally involving large floating point numbers until the solution has converged. The current problem in-process metrology faces is the large computational times for the analysis of these millions of streaming data points. This research addresses the bottleneck using the Graphical Processing Unit (GPU), primarily developed by the computer gaming industry, to optimize operations. The explosive growth in the programming capabilities and raw processing power of Graphical Processing Units has opened up new avenues for their use in non-graphic applications. The combination of large stream of data and the need for 3D vector operations make the primitive shape fit algorithms excellent candidates for processing via a GPU. The work presented in this research investigates the use of the parallel processing capabilities of the GPU in expediting specific computations involved in the fitting procedure. The least squares fit algorithms for the circle, sphere, cylinder, plane, cone and torus have been implemented on the GPU using NVIDIA\u27s Compute Unified Device Architecture (CUDA). The implementations are benchmarked against those on a CPU which are carried out using C++. The Gauss Newton minimization algorithm is used to obtain the best fit parameters for each of the aforementioned primitives. The computation times for the two implementations are compared. It is demonstrated that the GPU is about 3-4 times faster than the CPU for a relatively simple geometry such as the circle while the factor scales to about 14 for a torus which is more complex

Analysis and Identification of Aeroplane Images Using Transform Based Methods

Object recognition is one of oldest applications of automatic pattern recognition. The object recognition has generated lot of interest among researchers for a variety of applications like face detection, people counting, vehicle detection, manufacturing industry, online images, security etc. The main objective of this work is to recognize aeroplanes, even if they are of different size (different scale) or even if they are Oriented with different skew angles. This work would be useful in tracking an aircraft for navigation applications. Further the identification of aeroplanes can be used in military applications to detect the enemy aircrafts. To achieve these objectives, the main challenge is the different shape, size and orientation of aircrafts which pose difficulty in the object recognition. In this work, an aeroplane is identified by extracting and comparing features between the test and training database images. This task is difficult for computers, however for humans; object recognition is effortless and instantaneous. In the first stage, the images of different aeroplanes and helicopters are selected and these images are downloaded from the web page “www.grabcad.com”. These images are grouped into two sets. The first set comprises database images which are used for training the system, whereas the second set is used for testing and obtaining the recognition accuracy for different algorithms. All these images are normalized and binarized using the thresholding concept. In the second stage, 2D- Transforms (2D-FFT and 2D-Hough Transform) are applied to all the pixels of these binarised images (both testing and training database). After applying the transform, the pixel intensity value will have both, the real and also the imaginary values. Since the imaginary values of the pixel, has only “phase information”, which is not useful in the recognition of aeroplanes, this imaginary value of all the pixels in all the images are neglected. The real part of the pixel intensity values (after applying the transform) is only considered for recognition. In this work, all the images are normalized to 50 X 50 size and hence the total number of pixels becomes 2500 for every image. The size of the matrix of each image (both test and database) is converted to (2500 X 1) column matrix from 50 X 50 matrix size. Hence after applying 2D transforms each image is of matrix size (2500 X 1).This matrix of (2500 X 1) size for each image (both testing and training), becomes the feature vector for that particular image. This process is applied to all the images and the features are extracted for all the test and database images. In the third and the last stage, k-NN (k Nearest Neighbourhood) classifier is used in the identification of an aeroplane. The k-NN classifier with k=1 is the Euclidean distance. Hence, the recognition is achieved by calculating and identifying a database image which has minimum Euclidean distance to the given test image. The test image is shown on the left side of the result image, whereas the identified image of the database is shown on the right side of the result image. The cross validation of the results is also performed in this work. The Recognition accuracy with 2D-FFT is obtained as 88% and the Recognition accuracy with 2D-Hough transform is found to be 82%. The reason for this difference can be because of the reason that, Hough transforms works on the principle of detection of straight lines in any image. Hence it can be concluded that 2D-FFT has higher Recognition accuracy compared to 2D-Hough transform.

Analysis and Recognition of Animals in Zoo Using Transform based techniques

An object recognition system identifies an object which is under test by comparing different features of the test and training database images. This task is difficult for computers, however for humans object recognition is effortless and instantaneous. The various applications for object recognition are in the fields of Medicine, Communications, Military Intelligence, Bioinformatics and many others. It is the ability to perceive an object’s physical properties such as shape, color and texture and apply semantic attributes to the object, which includes the understanding of its use, previous experience with the object and how it relates to others. Algorithmic description of this task for implementation on machines has been very difficult. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different viewpoints, in many different sizes and scales or even when they are translated or rotated. Many approaches to this task have been implemented over decades, but still this is an open area of research. There are standard databases available for research in the area of object recognition. In this proposed work, the images of the different animals are downloaded from https://www.rocq.inria.fr/gamma/gamma/download/ANIMALS/index0.php and 3D Meshes Research Database. These images are to be converted into binary form, by using thresholding method. In the next step, distinguishable features which may be Zone based, Transform based or Statistical in nature are to be extracted from these images. Using these features, animals (in the present work) can be recognized by using various classifiers such as SVM, Neural Networks and NNC (Nearest Neighborhood Classifier) namely ‘Euclidean Distance’. The execution time of the simulation would also be compared for all the transforms implemented in the work. The results obtained would be compared with the existing results of similar wor

Folic Acid Functionalized Nanoparticles for Enhanced Oral Drug Delivery

The oral absorption of drugs that have poor bioavailability can be enhanced by encapsulation in polymeric nanoparticles. Transcellular transport of nanoparticle-encapsulated drug, possibly through transcytosis, is likely the major mechanism through which nanoparticles improve drug absorption. We hypothesized that the cellular uptake and transport of nanoparticles can be further increased by targeting the folate receptors expressed on the intestinal epithelial cells. The objective of this research was to study the effect of folic acid functionalization on transcellular transport of nanoparticle-encapsulated paclitaxel, a chemotherapeutic with poor oral bioavailability. Surface-functionalized poly(D,L-lactide-co-glycolide) (PLGA) nanoparticles loaded with paclitaxel were prepared by the interfacial activity assisted surface functionalization technique. Transport of paclitaxel-loaded nanoparticles was investigated using Caco-2 cell monolayers as an in vitro model. Caco-2 cells were found to express folate receptor and the drug efflux protein, p-glycoprotein, to high levels. Encapsulation of paclitaxel in PLGA nanoparticles resulted in a 5-fold increase in apparent permeability (P(app)) across Caco-2 cells. Functionalization of nanoparticles with folic acid further increased the transport (8-fold higher transport compared to free paclitaxel). Confocal microscopic studies showed that folic acid-functionalized nanoparticles were internalized by the cells and that nanoparticles did not have any gross effects on tight junction integrity. In conclusion, our studies indicate that folic acid functionalized nanoparticles have the potential to enhance the oral absorption of drugs with poor oral bioavailability

Toward More Accurate and Generalizable Evaluation Metrics for Task-Oriented Dialogs

Measurement of interaction quality is a critical task for the improvement of spoken dialog systems. Existing approaches to dialog quality estimation either focus on evaluating the quality of individual turns, or collect dialog-level quality measurements from end users immediately following an interaction. In contrast to these approaches, we introduce a new dialog-level annotation workflow called Dialog Quality Annotation (DQA). DQA expert annotators evaluate the quality of dialogs as a whole, and also label dialogs for attributes such as goal completion and user sentiment. In this contribution, we show that: (i) while dialog quality cannot be completely decomposed into dialog-level attributes, there is a strong relationship between some objective dialog attributes and judgments of dialog quality; (ii) for the task of dialog-level quality estimation, a supervised model trained on dialog-level annotations outperforms methods based purely on aggregating turn-level features; and (iii) the proposed evaluation model shows better domain generalization ability compared to the baselines. On the basis of these results, we argue that having high-quality human-annotated data is an important component of evaluating interaction quality for large industrial-scale voice assistant platforms

A Broadband Internally Resonant Vibratory Energy Harvester

The objective of this paper is twofold: first to illustrate that nonlinear modal interactions, namely, a two-to-one internal resonance energy pump, can be exploited to improve the steady-state bandwidth of vibratory energy harvesters; and, second, to investigate the influence of key system's parameters on the steady-state bandwidth in the presence of the internal resonance. To achieve this objective, an L-shaped piezoelectric cantilevered harvester augmented with frequency tuning magnets is considered. The distance between the magnets is adjusted such that the second modal frequency of the structure is nearly twice its first modal frequency. This facilitates a nonlinear energy exchange between these two commensurate modes resulting in large-amplitude responses over a wider range of frequencies. The harvester is then subjected to a harmonic excitation with a frequency close to the first modal frequency, and the voltage-frequency response curves are generated. Results clearly illustrate an improved bandwidth and output voltage over a case which does not involve an internal resonance. A nonlinear model of the harvester is developed and validated against experimental findings. An approximate analytical solution of the model is obtained using perturbation methods and utilized to draw several conclusions regarding the influence of key design parameters on the harvester's bandwidth

Chromium(VI) bisimido dichloro, bisimido alkylidene, and chromium(V) bisimido iodo N‐heterocyclic carbene complexes

Reaction of CrCl2(N-tBu)2 with 1,3-dimethylimidazol-2-ylidene (IMe), 1,3-dimethyl-4,5-dichloroimidazol-2-ylidene (IMeCl2), 1,3-di(2-propyl)imidazol-2-ylidene (IPr), 1,3-dimesitylimidazol-2-ylidene (IMes) and 1,3-bis(2,6-(2-Pr)2C6H3)imidazol-2-ylidene (IDipp) yields the corresponding N-heterocyclic carbene (NHC) adducts CrCl2(IMe)(N-tBu)2 (1), CrCl2(IMeCl2)(N-tBu)2 (2), CrCl2(IPr)(N-tBu)2 (3), CrCl2(IMes)(N-tBu)2 (4) and CrCl2(IDipp)(N-tBu)2 (5). Likewise, reaction of CrCl2(N-2,6-(2-Pr)2C6H3)2 and CrCl2(N-adamantyl)2 with IMes yields CrCl2(N-2,6-(2-Pr)2C6H3)2(IMes) (6) and CrCl2(N-adamantyl)2(IMes) (7), respectively. Reaction of CrCl2(N-tBu)2 with the bidentate NHCs 1-R-3-(1-(2-LiO-C6H4))imidazol-2-ylidene yields the corresponding pentacoordinated Cr(VI) complexes CrCl2(1-R-3-(1-(2-O-C6H4))imidazol-2-ylidene)2C6H3)2(IMes) (R = 2,4,6-(CH3)3C6H2, 8), (R = tBu, 9), (R = 2-phenyl-C6H4, 10). Reaction of the chromium(VI) complex Cr(N-2,6-(2-Pr)2-C6H3)2(CH2C(CH3)3)2 with 1,3-dimethylimidazol-2-ylidene·AgI yields the bimetallic silver adduct of the chromium alkylidene complex (11) along with the tetrahedral chromium(V) complex CrI(N-2,6-(2-Pr)2-C6H3)2(1,3-dimethylimidazol-2-ylidene) (12). Compounds 1-4, 7, 9-12 were characterized by single-crystal X-ray analysis. Finally, the chromium(VI) bisimido-amido complexes 13-14 bearing the N-6-(2-(diethylboryl)phenyl)pyridyl-2-yl-motif are reported.Deutsche ForschungsgemeinschaftProjekt DEA