Hyperspectral Imaging Sensors (HSI) obtain spectral information from an object, and they are used to solve problems in Remote Sensing, Food Analysis, Precision Agriculture, and others. This paper took advantage of modern high-resolution cameras, electronics, and optics to develop a robust, low-cost, and easy to assemble HSI device. This device could be used to evaluate new algorithms for hyperspectral image analysis and explore its feasibility to develop new applications on a low-budget. It weighs up to 300 g, detects wavelengths from 400 nm–1052 nm, and generates up to 315 different wavebands with a spectral resolution up to 2.0698 nm. Its spatial resolution of 116 × 110 pixels works for many applications. Furthermore, with only 2% of the cost of commercial HSI devices with similar characteristics, it has shown high spectral accuracy in controlled light conditions as well as ambient light conditions. Unlike related works, the proposed HSI system includes a framework to build the proposed HSI from scratch. This framework decreases the complexity of building an HSI device as well as the processing time. It contains every needed 3D model, a calibration method, the image acquisition software, and the methodology to build and calibrate the proposed HSI device. Therefore, the proposed HSI system is portable, reusable, and lightweight.

In this paper, we present a novel algorithm for learning fuzzy measures for Choquet integration. There are two novel aspects of the algorithm: it seeks to explicitly reduce the number of nonzero parameters in the measure to eliminate noninformative or useless information sources and it uses a Bayesian model for parameter estimation which has not been previously applied to the fuzzy measure learning problem. The method uses a hierarchical model that implements a sparsity promotion algorithm through a Gibbs sampler. This approach builds on the methods proposed by Figueiredo et al which uses expectation maximization (EM) to maximize the least absolute shrinkage and selection operator (LASSO) criterion under a distribution that promotes sparsity. Additional constraints are needed to satisfy the requirements of fuzzy measures. Figueiredo's algorithm does not have a mechanism for imposing these constraints. The constraints are imposed by sequentially exploring the lattice tree of the power set and requiring that each fuzzy measure value assigned to a set lies in the domain of a truncated Gaussian determined by the fuzzy measures of supersets of the set under consideration

The increasing use of smaller technologies in the manufacture of analog front-ends (AFEs) for communication systems has increased the impact their non-ideal components produce. This results in a significant degradation of the system performance that must be identified and addressed. In particular, the I/Q imbalance is commonly estimated and compensated via digital signal processing techniques using training sequences. In order to preclude the rise of other non-idealities, such as the non-linearity of the power amplifier (PA), these training sequences should be chosen to have a low peak-to-average power ratio (PAPR). In addition, it is desirable that these techniques have reduced computational complexity for minimizing estimation times and area resources. This paper presents a novel I/Q imbalance estimation algorithm that is computationally simple, it only requires adders and shifters, while exhibiting a PAPR ≤ 2. It can include the transmitter and receiver I/Q imbalances as well as the multipath phenomena. It is based on a newly used property of Golay complementary sequences (GCS). The statistical efficiency and low complexity of the proposed algorithm are proved, while its flexibility is illustrated under several extreme test cases.

Datasets with thousands of features represent a challenge for many of the existing learning methods because of the well known curse of dimensionality. Not only that, but the presence of irrelevant and redundant features on any dataset can degrade the performance of any model where training and inference is attempted. In addition, in large datasets, the manual management of features tends to be impractical. Therefore, the increasing interest of developing frameworks for the automatic discovery and removal of useless features through the literature of Machine Learning. This is the reason why, in this paper, we propose a novel framework for selecting relevant features in supervised datasets based on a cascade of methods where speed and precision are in mind. This framework consists of a novel combination of Approximated and Simulate Annealing versions of the Maximal Information Coefficient (MIC) to generalize the simple linear relation between features. This process is performed in a series of steps by applying the MIC algorithms and cutoff strategies to remove irrelevant and redundant features. The framework is also designed to achieve a balance between accuracy and speed. To test the performance of the proposed framework, a series of experiments are conducted on a large battery of datasets from SPECTF Heart to Sonar data. The results show the balance of accuracy and speed that the proposed framework can achieve.