Jose Tuxpan

Instituto Potosino de Investigación Científica y Tecnológica | IPICYT · Department of Applied Geosciences

We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian...

The method's development to detect oil-spills, and concentration monitoring of marine environments, are essential in emergency response. To develop a classification model, this work was based on the spectral response of surfaces using reflectance data, and machine learning (ML) techniques, with the objective of detecting oil in Landsat imagery. Add...

This paper proposes an approach of fusion geo-referenced and non-georeferenced data. These data can be acquired by a) unmanned surface vehicles (USVs), b) unmanned aerial vehicles (UAVs), c) airborne and submersible optical sensors, d) acoustic sensors and e) aquatic sound profiling instruments. The test scenario consisted of monitoring and morphol...

The city of Morelia has been affected by Structurally-Controlled Differential Subsidence (SCDS) since at least 1983, modifying civil structures and infrastructure through the appearance of ground failures and differential ground subsidence. The resulting damage to streets, homes, hydraulic lines, and government facilities has caused economic losses...

Structurally-Controlled Differential Subsidence (SCDS) is the gradual sinking of the ground, characterized by the development of a damage band, terrain discontinuities and collapses, aligned according to the strike of a controlling geological structure. SCDS has been reported since the 1980s in several cities settled on tectonic valleys in central...

Marine oil spills pose significant ecological and economic threats worldwide, requiring effective decision-making tools. In this study, the optimal parameters, and configurations for Deep Learning models in oil spill classification and segmentation using Sentinel-1 SAR imagery were identified. First, a new Sentinel-1 image dataset was created. Nine...

The presence of deep convective clouds is directly related to potential convective hazards, such as lightning strikes, hail, severe storms, flash floods, and tornadoes. On the other hand, Mexico has a limited and heterogeneous network of instruments that allow for efficient and reliable monitoring and forecasting of such events. In this study, a qu...

In the following link you can donwload the paper https://authors.elsevier.com/a/1iZlscBfJ0bCc

This research addressed the implementation of a forecast framework, based on Machine Learning classification techniques, for the identification and monitoring of possible convective events associated with extreme precipitation. This methodology was applied in Los Mochis, Sinaloa, in Northeast Mexico, for this location presents intense convective ac...

This study aims to improve the current method of studying potentially toxic elements (PTEs) in urban dust using direct chemical evidence (from dust, rock, and emission source samples) and robust geochemical methods. The provenance of urban dust was determined using REEs and geochemical diagrams (V-Ni-Th*10, Zr vs. TiO2, and Zr/Ti vs. Nb/Y). The geo...

The Villa de Reyes Graben (VRG) is located in the Mesa Central (MC) province and is composed of felsic Oligocene volcanic rocks. In this paper, we present new petrography and whole-rock geochemistry for felsic volcanic rocks to understand their origin, evolution, petrogenesis and their tectonic relation with the MC. Based on geochemical composition...

Groundwater occurrence in semi-arid regions is variable in space and time due to climate patterns, terrain features, and aquifer properties. Thus, accurate delineation of Groundwater Potential Zones (GWPZs) is essential for sustainable water resources management in these environments. The present research aims to delineate and assess GWPZs in a sem...

In this work, we introduce families of multimodal maps based on logistic map, i.e., families of m-modal maps are defined on an interval I ⊂ ℝ , which is partitioned into non-uniform subdomains, with m ∈ ℕ . Because the subdomains of the partition are not uniform, each subdomain contains a unimodal map, given by the logistic map, that can have diffe...

In the Colorado River Delta, the interaction of tidal currents and sea-bottom sediment formed, in geological times, large-scale seabed patterns known as sandbanks. These patterns are oriented along the delta, almost parallel to the dominant tidal flow, with the bathymetry having an undulating character across the delta. Calculations and analysis sh...

Groundwater has become an alternative water supply for various sectors of the population and the economy, and its extraction is increasing worldwide. The water poverty index (WPI) is a holistic tool that enables the establishment of links between poverty, social marginalization, environmental integrity, water availability and health. The index incl...

The present investigation focused on solving the significance of tidal currents in the sediment resuspension process revealed by satellite imagery in the tidal basin of the Colorado River Delta, located in the northernmost part of the Gulf of California. A depth-integrated finite difference hydrodynamic model, coupled with a suspended sediment tran...

The rapid population growth in urban areas stresses the underlying aquifers in both quantity and quality. The identification of aquifer vulnerability and quality characteristics help decision makers in managing groundwater resources and mitigating potential contamination pathways. For this purpose, the units composing complex aquifer systems should...

In arid and semi-arid regions, unregulated land use changes as a result of poor planning, and the expansion of agricultural and livestock activities increase the risk of desertification and other potentially severe environmental impacts. Several consequences of improper land management practices are soil deterioration and erosion, which may be inte...

Different idealized geometries, resembling shallow tidal-dominated seas with different geographical features like bays, islands, and headlands, were investigated by applying a two-dimensional hydrodynamic-numerical and morphological model. The geometries were exposed to fast oscillatory tidal velocities and unbounded sediment availability. The inte...

Numerical simulations revealed a profound interaction between the severe dust storm of 2007 caused by Santa Ana winds and the Gulf of California. The weather research and forecasting model coupled with a chemistry module (WRF-CHEM) and the hybrid single-particle Lagrangian integrated trajectory model (HYSPLIT) allowed for the estimation of the mete...

Groundwater quality and availability are essential for human consumption and social and economic activities in arid and semiarid regions. Many developing countries use wastewater for irrigation, which has in most cases led to groundwater pollution. The Mezquital Valley, a semiarid region in central Mexico, is the largest agricultural irrigation reg...

Recent monitoring techniques employ multiple sources of information for the characterization of the phenomenon to be studied, being the coupling and adjustment of multi-source data one of the first challenges to consider and solve. The authors propose a new framework of the multi-source and multi-temporal data-oriented fusion for the characterizati...

Groundwater quality and availability are essential for human consumption and social and economic activities in arid and semiarid regions. Many developing countries use wastewater for irrigation, which has in most cases led to groundwater pollution. The Mezquital Valley, a semiarid region in central Mexico, is the largest agricultural irrigation reg...

Tornadoes are violent and destructive natural phenomena that occur on a local scale in most regions around the world. Severe storms occasionally lead to the formation of mesocyclones, whose direction or sense of rotation is often determined by the Coriolis force, among other factors. In the Northern Hemisphere, more than 99% of all tornadoes rotate...

This paper presents a containerized service for clus- tering and categorization of weather records in the cloud. This service considers a scheme of microservices and containers for organizations and end-users to manage/process weather records from the acquisition, passing through the prepossessing and processing stages, to the exhibition of results...

This paper presents the design and development of an interoperable geoportal service for discovery and management of earth observation products (EOPs). In this service, the geoportal components are encapsulated into virtual containers that are launched into the cloud by using a microservice scheme to solve issues such as interoperability (with othe...

In areas with long periods of drought, it is essential to implement strategies to manage the available water resource. Tierra Nueva Basin is affected by this situation, consequently the farm production and livestock holdings are affected and the people don’t have access to enough water. In this paper, we propose an integrative methodology based on...

In recent years, the development of the Global Navigation Satellite System (GNSS) has brought benefits to new methods of surveying with Global Positioning System (GPS), they have developed rapidly and have new measurement techniques (static, rapid static, real- Time Kinematic), increased productivity, reduced time measurement and quality for accura...

This work is based on the robust unified approach for fractional synthetic aperture radar (SAR) imagery feature enhancement via the descriptive experiment design regularization-total variation (DEDR-TV) technique. We present a new DEDR-TV methodology behavioral study, modifying the weighting factors balanced scheme, with the objetive of maximizing...

Feature-enhanced reconstruction of the reflectivity maps (remotely sensed scene images) from the low-resolution fractional SAR imagery is treated for harsh sensing scenarios with uncertainties attributed to possible imperfect sensor calibration, atmospheric turbulence and uncontrolled carrier trajectory deviations. These effects lead to the randoml...

We address a new multimode system/method fusion oriented neural network (NN) computing approach to enhancement of conventional low resolution remote sensing (RS) radar and/or fractional synthetic aperture radar imagery. First, the squared error norm objective function minimization-based descriptive experiment design regularization (DEDR) framework...

The descriptive experiment design regularization (DEDR) paradigm is aggregated with the variational analysis approach that combines the ℓ2 image metric with the ℓ1 sparse image gradient map metric structures in the solution space. The proposed ℓ2 - ℓ1 structured total variation DEDR (TV-DEDR) framework is particularly adapted for enhanced imaging w...

The convex optimization-based descriptive experiment design regularization (DEDR) method is aggregated with the variational analysis (VA) approach for adaptive high-resolution sensing into a unified DEDR-VA framework that puts in a single optimization frame high-resolution radar/SAR image formation in uncertain operational scenarios, adaptive despe...

The descriptive experiment design regularization (DEDR) paradigm is aggregated with the variational analysis approach that combines the l2 image metric with the l1 sparse image gradient map metric structures in the solution space. The proposed l2−l1 structured total variation DEDR (STV-DEDR) framework is particularly adapted for enhanced imaging wi...

In this paper, we propose a new multilevel regularization technique for solving nonlinear inverse problems of reconstruction of remote sensing (RS) imagery acquired with low resolution radar sensor systems of different modalities operated in the scenarios with perturbed (uncertain) system operators. We infer the proposed generalized multilevel regu...

We address a neural network (NN) computing-based approach to the problem of near real-time enhancement of compressed fractional SAR imagery. The proposed approach employs the recently developed descriptive experiment design regularization (DEDR) framework for multimode image reconstruction/fusion aggregated with the variational analysis (VA) image...

The convex optimization-based descriptive experiment design regularization (DEDR) method is aggregated with the neural network (NN)-adapted variational analysis (VA) approach for adaptive high-resolution sensing into a unified DEDR -VA-NN framework that puts in a single optimization frame high-resolution radar/SAR image formation in uncertain opera...

The problem of high-resolution array radar/SAR imaging is stated and treated as a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the wavefield scattered from the remotely sensed scene observed through a linear signal formation operator. We develop the radar/SAR adapted imaging framewor...

We address a new approach to the problem of improvement of the quality of remote sensing (RS) imagery obtained with multimode imaging radar/SAR systems that employ different image formation methods via performing the collaborative RS image/method fusion. The collaborative considerations involve adaptive adjustment of the user-controllable regulariz...

In this study, we apply the robust error estimation theory as a basis to develop an appropriate procedure that performs the processing and enhancement of the remote sensing (RS) image contaminated by composite noise (additive and multiplicative) and degraded by the data acquisition system. The first reconstruction stage is performed using the Bayes...

We address new robust computationally efficient numerical technique for high-resolution reconstructive imaging as required for enhanced remote sensing (RS) with fractional synthetic aperture radar (SAR) sensor systems. The modification of the previously proposed descriptive experiment design regularization (DEDR) method via incorporating the conver...

In this paper, the statistical Bayesian and descriptive regularization approaches for high resolution radar image formation is detailed in many works, where such approach is adapted to the sm and mm waveband remote sensing (RS) applications considered.

We address unified intelligent descriptive experiment design regularization (DEDR) methodology for computer-aided investigation of new intelligent signal processing (SP) perspectives for collaborative remote sensing (RS) and distributed sensor network (SN) data acquisition, intelligent processing and information fusion. The sophisticated “Virtual R...

We address new approach for enhanced multi-sensor imaging in uncertain remote sensing (RS) operational scenarios. Our approach is based on incorporating the projections onto convex solution sets (POCS) into the descriptive experiment design regularization (DEDR) and fused Bayesian regularization (FBR) methods to enhance the robustness and convergen...

This paper presents a technique for the high resolution enhancement of remote sensing imagery degraded in a random channel and contaminated with composed noise (additive and multiplicative). The proposed method aggregates the Constraint Least Square (CLS), the Bayes Minimum Risk (BMR), the maximum entropy Median Filter (MF) and the Variational Anal...

This paper proposes a new approach for the reconstruction of the RS images degraded by composite noise (additive and multiplicative), taking into account the limitations of the sensor system. Our proposal takes advantage of the statistical and probabilistic qualities of the employed Weighted Constrained Least Squares (WCLS) and Robust Bayes Minimum...