Application of Memristors in Microwave Passive Circuits

The recent implementation of the fourth fundamental electric circuit element, the memristor, opened new vistas in many fields of engineering applications. In this paper, we explore several RF/microwave passive circuits that might benefit from the memristor salient characteristics. We consider a power divider, coupled resonator bandpass filters, and a low-reflection quasi-Gaussian lowpass filter with lossy elements. We utilize memristors as configurable linear resistors and we propose memristor-based bandpass filters that feature suppression of parasitic frequency pass bands and widening of the desired rejection band. The simulations are performed in the time domain, using LTspice, and the RF/microwave circuits under consideration are modeled by ideal elements available in LTspice

Hierarchical inference of disparity

Disparity selective cells in V1 respond to the correlated receptive fields of the left and right retinae, which do not necessarily correspond to the same object in the 3D scene, i.e., these cells respond equally to both false and correct stereo matches. On the other hand, neurons in the extrastriate visual area V2 show much stronger responses to correct visual matches [Bakin et al, 2000]. This indicates that a part of the stereo correspondence problem is solved during disparity processing in these two areas. However, the mechanisms employed by the brain to accomplish this task are not yet understood. Existing computational models are mostly based on cooperative computations in V1 [Marr and Poggio 1976, Read and Cumming 2007], without exploiting the potential benefits of the hierarchical structure between V1 and V2. Here we propose a two-layer graphical model for disparity estimation from stereo. The lower layer matches the linear responses of neurons with Gabor receptive fields across images. Nodes in the upper layer infer a sparse code of the disparity map and act as priors that help disambiguate false from correct matches. When learned on natural disparity maps, the receptive fields of the sparse code converge to oriented depth edges, which is consistent with the electrophysiological studies in macaque [von der Heydt et al, 2000]. Moreover, when such a code is used for depth inference in our two layer model, the resulting disparity map for the Tsukuba stereo pair [middlebury database] has 40% less false matches than the solution given by the first layer. Our model offers a demonstration of the hierarchical disparity computation, leading to testable predictions about V1-V2 interactions

Learning sparse representations of depth

This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer graphical model for inferring depth from stereo, by including a sparsity prior on the learned features. Since they capture higher-order dependencies in the depth structure, these priors can complement smoothness priors commonly used in depth inference based on Markov Random Field (MRF) models. Inference on the proposed graph is achieved using an alternating iterative optimization technique, where the first layer is solved using an existing MRF-based stereo matching algorithm, then held fixed as the second layer is solved using the proposed non-stationary sparse coding algorithm. This leads to a general method for improving solutions of state of the art MRF-based depth estimation algorithms. Our experimental results first show that depth inference using learned representations leads to state of the art denoising of depth maps obtained from laser range scanners and a time of flight camera. Furthermore, we show that adding sparse priors improves the results of two depth estimation methods: the classical graph cut algorithm by Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page

Software Verification and Graph Similarity for Automated Evaluation of Students' Assignments

In this paper we promote introducing software verification and control flow graph similarity measurement in automated evaluation of students' programs. We present a new grading framework that merges results obtained by combination of these two approaches with results obtained by automated testing, leading to improved quality and precision of automated grading. These two approaches are also useful in providing a comprehensible feedback that can help students to improve the quality of their programs We also present our corresponding tools that are publicly available and open source. The tools are based on LLVM low-level intermediate code representation, so they could be applied to a number of programming languages. Experimental evaluation of the proposed grading framework is performed on a corpus of university students' programs written in programming language C. Results of the experiments show that automatically generated grades are highly correlated with manually determined grades suggesting that the presented tools can find real-world applications in studying and grading

Learning from Experience: My Time with SWIM and READ

My experiences at SWIM and READ have led me through many challenges and earned me many successes and have helped me to gain a variety of hard skills and soft skills while letting me improve upon some that I already had. Here, I got the opportunities to work on a book project called “20 Stories of Hope” and to spend a year teaching a young child literacy skills, both of which have given me valuable chances to both find and pursue new passions and to develop my career pathway towards teaching

Foolproof completions for high rate production wells

Operators, especially those managing production from deepwater reservoirs, are striving to produce hydrocarbons at higher and higher rates without exposing the wells to completion failure risk. To avoid screen failures, recent studies have favored gravel pack (GP) and high rate water pack (HRWP) completions over high-permeability fracturing (HPF), known in the vernacular as a frac&pack (FP) for very high rate wells. While a properly designed GP completion may prevent sand production, it does not stop formation fines migration, and, over time, fines accumulation in the GP will lead to increasing completion skin. Although, and not always, the skin can be removed by acidizing, it is not practical to perform repeated acid treatments on deepwater wells, particularly those with subsea wellheads, and the alternative has been to subject the completion to increasingly high drawdown, accepting a high skin effect. A far better solution is to use a HPF completion. Of course the execution of a successful HPF is not a trivial exercise, and frequently, there is a steep learning curve for such a practice. This work explains the importance to HPF completions of the well trajectory through the interval to be hydraulically fractured, for production, not execution, reasons. A new model quantifies the effect of the well inclination on the connectivity between the fracture and the well via perforations. Guidelines based on the maximum target production rate, including forecasts of multiphase flow, are provided to size the HPF completion to avoid common completion failures that may result from high fluid rate and/or fines movement. Skin model will be developed for both vertical and deviated wells. Once the HPF is properly designed and executed, the operators should end up with a long term low skin good completion quality well. The well will be safely produced at the maximum flow rates, with no need for well surveillance and monitoring