Earmarking government revenues in Colombia

This paper has two broad objectives. The first is the examination of the trends in the size and structure of earmarking since 1970, illuminating the major changes and their causes. The second is an evaluation of the major examples of earmarking with a view toward making recommendations for change. In making recommendations for reducing the scope of earmarking in Colombia, several principles should be used for guidance: (a) is there a substantial overlap between the beneficiaries and the tax/price payers for any given government service; (b) do the tax/price arrangements appear to be leading to appropriate levels of the service over time; and (c) are resources being utilized effectively for the purpose intended. The remainder of the paper is divided into four parts: (a) time series data on the size and structure of earmarking during the last two decades; (b) factors behind the popularity of earmarking in Colombia and a review of the findings and recommendations of two major government commissions which have examined the subject; (c) a critical review of the major examples which make up over 90% of total earmarking; and (d) a summary of major findings and recommendations for changes.Economic Theory&Research,Public Sector Economics&Finance,Environmental Economics&Policies,National Governance,Banks&Banking Reform

Experimental Validation of a 3D Microwave Imaging Device for Brain Stroke Monitoring

In this work we present the experimental validation of a 3D microwave imaging device to brain observation. The device is conceived as a way to monitor stroke development, supporting physicians in the follow-up of patients in the aftermath of cerebrovascular accidents, and giving to them extra information for decision-making and application of therapies. The device acquires data through antennas placed around the patient head, in a low-complexity system that guarantees that available information is enough for reliable outcome. Experimental testing is performed on a 3-D human-like head phantom with promising results

Discretization Error Analysis in the Contrast Source Inversion Algorithm

This paper describes the use of the contrast source inversion method combined with the finite element method for the numerical solution of 3-D microwave inversion problems. In particular, this work is focused on the discretization of the involved physical vector quantities, analyzing the impact of the chosen discretization on the solution process with the goal of optimizing the implemented algorithm in terms of accuracy, memory requirements and computational cost

Multi-shot Calibration Technique for Microwave Imaging Systems

This paper proposes a novel “multi-shot” calibration technique that reduces imaging microwave reconstructions artifacts, compensating for uncontrolled variations during the measuring process and later propagated in the inversion. The calibration combines different consecutive sets of measured data with simulated ones in a post-processing stage, providing benefits without the need for additional experimental reference calibrations. The proposed scheme is tested experimentally in a non-trivial scenario. A microwave scanner images an early-stage hemorrhagic stroke in the left parietal lobe, applying a differential imaging algorithm based on the truncated singular value decomposition. Though, the proposed mechanisms can be used for other microwave imaging devices. The results reveal that the calibration procedure improves the quality of the retrieved images compared to the non-calibrated approach, cleaning the images and making the interpretation of imaged contrast variation easier

Hybrid Simulation-Measurement Calibration Technique for Microwave Imaging Systems

This paper proposes an innovative technique to calibrate microwave imaging (MWI) systems combining available measured data with simulated synthetic ones. The introduced technique aims to compensate the variations of the antenna array due to unavoidable manufacturing tolerances and placement, in comparison to the nominal electromagnetic (EM) scenario. The scheme is tested virtually and experimentally for the MWI of the adult human head tissues. The virtual EM analysis uses a realistic 3-D CAD model working together with a full-wave software, based on the finite element method. Meanwhile, the real implementation employs a single-cavity anthropomorphic head phantom and a custom brick-shaped antenna array working at around 1 GHz

Moving Forward to Real-time Imaging-based Monitoring of Cerebrovascular Diseases Using a Microwave Device: Numerical and Experimental Validation

This paper introduces a numerical and experimental assessment of the microwave device capabilities to perform continuous real-time imaging-based monitoring of a brain stroke, exploiting a differential measuring scheme of the scattering matrices and the distorted Born approximation. The device works around 1 GHz and consists of a low-complexity 22-antenna-array composed of custom-made wearable elements. The imaging kernel is built using an average-head reference scenario computed off-line via accurate numerical models and an in-house finite element method electromagnetic solver. The validation follows the progression of emulated evolving hemorrhagic stroke condition, including tests with both an average single-tissue head model and a multi-tissue one in the numerical part and the average scenario in the experimental one. The results show the system's capacity to localize and track the shape changes of the stroke-affected area in all studied cases

Efficient Data Generation for Stroke Classification via Multilayer Perceptron

The aim of this paper is to overcome one of the main problems of machine learning when it faces the medical world: the need of a large amount of data. Through the distorted Born approximation, the scattering parameters and the dielectric contrast in the domain of interest are linked by a linearized integral operator. This method allows to generate a large dataset in a short time. In this work, machine learning is exploited to classify brain stroke presence, typology and position. The classifier model is based on the multilayer perceptron algorithm and it is used firstly for validation and then with a testing set composed by full-wave simulations. In both cases, the model reaches very high level of accuracy

Model-based data generation for support vector machine stroke classification

This paper presents a new and efficient method to generate a dataset for brain stroke classification. Exploiting the Born approximation, it derives scattering parameters at antennas locations in a 3-D scenario through a linear integral operator. This technique allows to create a large amount of data in a short time, if compared with the full-wave simulations or measurements. Then, the support vector machine is used to create the classifier model, based on training set data with a supervised method and to classify the test set. The dataset is composed by 9 classes, differentiated for presence, typology and position of the stroke. The algorithm is able to classify the test set with a high accuracy

Brain Stroke Classification via Machine Learning Algorithms Trained with a Linearized Scattering Operator

This paper proposes an efficient and fast method to create large datasets for machine learning algorithms applied to brain stroke classification via microwave imaging systems. The proposed method is based on the distorted Born approximation and linearization of the scattering operator, in order to minimize the time to generate the large datasets needed to train the machine learning algorithms. The method is then applied to a microwave imaging system, which consists of twenty-four antennas conformal to the upper part of the head, realized with a 3D anthropomorphic multi-tissue model. Each antenna acts as a transmitter and receiver, and the working frequency is 1 GHz. The data are elaborated with three machine learning algorithms: support vector machine, multilayer perceptron, and k-nearest neighbours, comparing their performance. All classifiers can identify the presence or absence of the stroke, the kind of stroke (haemorrhagic or ischemic), and its position within the brain. The trained algorithms were tested with datasets generated via full-wave simulations of the overall system, considering also slightly modified antennas and limiting the data acquisition to amplitude only. The obtained results are promising for a possible real-time brain stroke classification