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When shining a light through a finger, some of it will be absorbed by oxygenated and unoxygenated hemoglobin. Measuring the absorbed light over time provides the photo-plethysmographic (PPG) waveform, which can represent the blood flow of a subject. One way of obtaining the PPG waveform is to use the camera and flash of a smartphone, placing them on the finger of a subject, and analyzing the variation of red color. The PPG can also be obtained using oximeter-like devices, which are non-invasive and safe. In contrast, to measure the blood glucose of a subject, a glucometer is used, which is a device that is typically invasive and expensive. Therefore, we propose the use of the following descriptors from Chaos theory to analyze the PPG signal: correlation dimension, maximum Lyapunov exponent and Hurst exponent. Then, these values are converted into a 3-dimensional vector that can be represented in a 3-dimensional space. Each vector has an associated glucose level that is used to train an algorithm which classifies all the vectors in three different ranges of blood glucose levels.

Energy harvesting shock absorbers (EHSA) have made great progress in recent years, although there are still no commercial solutions for this technology. This paper addresses the question of whether, and under what conditions, an EHSA can completely replace a conventional one. In this way, any conventional suspension could be replicated at will, while recovering part of the wasted energy. This paper focuses on mimicking the original passive damper behavior by continuously varying the electrical parameters of the regenerative damper. For this study, a typical ball-screw EHSA is chosen, and its equivalent suspension parameters are tried to be matched to the initial damper. The methodology proposes several electrical control circuits that optimize the dynamic behavior of the regenerative damper from the continuous variation of a load resistance. The results show that, given a target damper curve, the regenerative damper can adequately replicate it when there is a minimum velocity in the damper. However, when the damper velocity is close to zero, the only way to compensate for inertia is through the introduction of external energy to the system.

This paper focuses on describing an implementation for an Ackermann-type vision-based Automated Guided Vehicle (AGV) used for the transportation of materials between and within industrial facilities. This implementation was done using a ZED Stereo Camera, Robot Operating System (ROS), ORB-SLAM2, and Unitronics PLCs and MCUs. The solution proposes a self-localization within a map previously created driving the vehicle around the desired route, the following of such route, and stopping along whenever the operator designated that the vehicle should stop. The vehicle can follow and mimic the route in environments with controlled lighting conditions

Autonomous Vehicles (AVs) have caught people&rsquo, s attention in recent years, not only from an academic or developmental viewpoint but also because of the wide range of applications that these vehicles may entail, such as intelligent mobility and logistics, as well as for industrial purposes, among others. The open literature contains a variety of works related to the subject. They employ a diversity of techniques ranging from probabilistic to ones based on Artificial Intelligence. The increase in computing capacity, well known to many, has opened plentiful opportunities for the algorithmic processing needed by these applications, making way for the development of autonomous navigation, in many cases with astounding results. The following paper presents a low-cost but high-performance minimal sensor open architecture implemented in a modular vehicle. It was developed in a short period of time, surpassing many of the currently available solutions found in the literature. Diverse experiments were carried out in the controlled and circumscribed environment of an autonomous circuit that demonstrates the efficiency of the applicability of the developed solution.

Tecnologico de Monterrey has implemented the Tec21 model, an innovative education model consisting of four components: challenge-based learning, flexibility, inspiring teachers and memorable experience. Challenge-based learning technique faces the student to real problems, binding him with his environment by challenging experiences, in this context this technique was applied to three different groups with students of different engineering programs through a challenging activity in order to evaluate competencies defined for the courses of Control Engineering and Control Systems. In this paper, results of this evaluation are presented, and a comparison is made between traditional and Semestre i courses.

During the learning process of multivariable calculus, the spatial perception of 3D surfaces represents a major challenge for the students. Since it requires the ability to sketch the graph in a 3D system, a handmade sketch can be confusing and lead to misinterpretations of other concepts. In the specific case of triple integration, the task of setting the limits of a solid requires the combination of a good visuospatial competency plus a correct management of symbolic language of functions. Nowadays, one of the most relevant objectives for using mathematical visualization programs and/or technologies is to improve the spatial perception of 3D surfaces. In this work, a comparison between the use of tailor-made Augmented Reality software and Matlab® for visuospatial skills development in multivariable calculus course, is presented.

This paper presents an electronic circuit able to emulate the behavior of a neural network based on memristive synapses. The latter is built with two flux-controlled floating memristor emulator circuits operating at high frequency and two passive resistors. Synapses are connected in a way that a bridge circuit is obtained, and its dynamical behavioral model is derived from characterizing memristive synapses. Analysis of the memristor characteristics for obtaining a suitable synaptic response is also described. A neural network of one neuron and two inputs is connected using the proposed topology, where synaptic positive and negative weights can easily be reconfigured. The behavior of the proposed artificial neural network based on memristors is verified through MATLAB, HSPICE simulations and experimental results. Synaptic multiplication is performed with positive and negative weights, and its behavior is also demonstrated through experimental results getting 6% of error.

We propose a construction algorithm that takes advantage of the geometric features of linear eigenspaces with focus-saddle and center-node equilibrium points to construct piecewise linear systems with Shilnikov chaos. Unlike previously published algorithms to generate chaos, we construct homoclinic (heteroclinic) orbits combining hyperbolic and nonhyperbolic domains by appropriately choosing locations where the trajectories move between linear domains through the switching surfaces at their crossing sections. We present two variants of our construction algorithm: On the first, a homoclinic orbit is constructed locating the exit and return points on the crossing section of the switching surface are connected by a nonhyperbolic center-node domain. In the second variant, we extend the construction method to construct heteroclinic loops with two switching surfaces between them. For the resulting systems a wide range of parameter values results on Shilnikov chaos. These chaotic systems are illustrated with numerical simulations.

This brief describes a simple electronic circuit able to emulate the behavior synaptic of a neuron based on memristors. The proposed circuit is built with two flux-controlled floating memristor emulator circuits operating to high-frequency and two passive resistors. All them are connected such way that a bridge circuit is obtained and its mathematical model is derived for characterizing memristive synapses. An analysis of the memristor characteristics in order to obtain a suitable synaptic response is described, showing symmetrical synaptic weighting when the k parameter is close to its maximum value. The behavior of the proposed topology is verified via simulations at MATLAB and HSPICE, showing a good accuracy among numerical simulations.

This paper deals with the high-level simulation of a frequency-shift-keying (FSK) modulator based on charge-controlled memconductor. The behavioral model of the memconductor is built on SIMULINK under MATLAB environment. It is demonstrated that the incremental memconductance increases and decreases according the width and amplitude of a positive and negative pulse signal, respectively; whereas the decremental memconductance increases and decreases according the width and amplitude of a negative and positive pulse signal. Both incremental and decremental memconductances are used to on-line reconfigure the frequency of oscillation of a single-memconductor controlled oscillator configured as FSK modulator. The obtained results not only allows an easy reconfigurability of the FSK modulator, but also demonstrate the viability of the memconductor to be used in other applications such as cellular neural networks, controllers, sensors, chaotic systems, relaxation oscillators, nonvolatile memory devices and programmable analog circuits.

Nowadays, transportation is a huge problem in highly populated cities since the efficient performance of the public transportation system is crucial for the urban mobility. To measure such an efficiency is still an open problem because it is a dynamical problem whose variables are not easily measured. In this work the public transportation system of Puebla City is analyzed from the Complex Network theory as an static system whose nodes represent the stops of the main routes of the urban region of the Puebla City. We use a georeference software to model the network, nodes and edges with real data, then we use standard software to make a centrality analysis and distribution degree of the nodes in the transportation network. The characterization of the network was done using three measures: the average distance between nodes, the degree of distribution and the centrality of the network, finally highly inflow points were analytically found. The objective of this research is to provide relevant information to the Municipal Planning Institute (IMPLAN) in order to aid in urban planning and economic and social development of the municipality of Puebla. Furthermore its aim is to provide information about conectivity of the public transportation network to the Department of Transportation (ST) of Puebla state.

In this paper, synchronization for n-dimensional discrete systems is analyzed and a general framework is proposed. It is considered synchronization for nonsmooth discrete systems in the form of a differential inclusion. Some preliminary results of the implementation of the proposal are shown.

Applying attractor reconstruction techniques and other chaotic measurements, it is shown that the long-term dynamics of a vertical, underactuated, two-degrees-of-freedom robot called Pendubot may exhibit complex dynamics including chaotic behavior. These techniques use only the measurement of some available variable of the system, and the resulting reconstruction allows us to identify unstable periodic orbits embedded in the chaotic attractor. In this paper, we also propose a parameter-perturbation-like control algorithm to stabilize the behavior of the Pendubot to force its dynamics to be periodic. We control this device using only the measurement of one of its angular position coordinates and consider that the system may be seen as five-dimensional (a non-autonomous, four-dimensional system), taking the amplitude of a sinusoidal external torque as the perturbation parameter. We change this parameter to stabilize one of the equilibrium points in the so-called Lorenz map. The main advantage of the method proposed here is that it can be implemented directly from time series data, irrespective of the overall dimension of the phase space. Also, reconstructions of the attractor based on the measurements are shown, as well as some experimental results of the controlled system.

We report the investigation of an energy harvesting system composed of coupled resonators with the magnetostrictive material Galfenol (FeGa). A coupled system of meso-scale (1-10 cm) cantilever beams for harvesting vibration energy is described for powering and aiding the performance of low-power wireless sensor nodes. Galfenol is chosen in this work for its durability, compared to the brittleness often encountered with piezoelectric materials, and high magnetomechanical coupling. A lumped model, which captures both the mechanical and electrical behavior of the individual transducers, is first developed. The values of the lumped element parameters are then derived empirically from fabricated beams in order to compare the model to experimental measurements. The governing equations of the coupled system lead to a system of differential equations with all-to-all coupling between transducers. An analysis of the system equations reveals different patterns of collective oscillations. Among the many different patterns, a synchronous state appears to yield the maximum energy that can be harvested by the system. Experiments on coupled system shows that the coupled system exhibits synchronization and an increment in the output power. Discussion of the required power converters is also included.

Analysis of biosignals is still an open problem, traditionally it has been tackled using frequency domain techniques. In this paper we propose to analyze the plethysmographic signal through its dynamical properties. Time series measured from healthy subjects were considered for proposing an analysis framework. We use delayed coordinates technique in order to reconstruct the state space dynamics of the attractor, then correlation dimension, Hurst and Lyapunov exponents were computed in order to further classify signals for different health conditions on patients.

Identification and control of unmanned aerial vehicles (UAV) has been a topic of interest in recent years due to several applications they are used for, some examples could be: surveillance or cooperative robotics. This paper shows the parameter identification and control of the AR Drone®, a quadrocopter from Parrot®; the parameter identification was achieved using experimental data from the physical system. For the control part a PID control was used to regulate all the variables of the Drone.

There are two main approaches in order to design biped robots; one is to use precise control and powerful actuators, and the other is based on passive dynamic walking optimizing energy consumption. In this paper a simulation method developed in Matlab® is proposed for a passive robot capable to accomplish a virtual passive walking. The proposed neural network-based method considers three actuators, one at the hip and another two for the ankles. Simulation results are shown for a basic gravity-based control and also for a neural inverse model control.

There are two main approaches in order to design biped robots; one is to use precise control and powerful actuators, and the other is based on passive walking optimizing energy consumption. In this paper a simulation method developed in Matlab® is proposed for a passive robot capable to accomplish a virtual passive walking. The proposed method considers three actuators, one at the hip and another two for the ankles. Simulation results are shown for a basic gravity-based control and can be used to develop another control strategies.

Modeling and bifurcation analysis of an energy harvesting system composed of coupled resonators using the Galfenol-based magnetostrictive material are presented. The analysis in this work should be broad enough to be applicable to a large class of vibratory-based energy harvesting systems since various types of vibratory harvesters share the same normal forms, e.g. magnetostrictive and piezoelectric materials. A combined model of the mechanical and electrical domains of a single energy harvester is discussed first. Building on this model, the governing equations of the coupled system are derived, leading to a system of differential equations with an all-to-all coupling between the resonators. A bifurcation analysis of the system equations reveals different patterns of collective oscillations. Among the many different patterns, a synchronous state exists and it is stable over a broad region of parameter space. This pattern has the potential to yield significant increases in power output and it will be used as a starting point to guide future experimental work. A Hamiltonian approach is employed to study analytically the nature of the bifurcations and to calculate an expression for the onset of synchronization valid for any number of harvesters.

The reconstruction of attractors based on time series obtained from a two-link, planar, underactuated robot called "pendubot" is developed. Time-delay coordinates, average mutual information, and global false nearest neighbors' methods are used to reconstruct the invariant sets. It is shown that, under the action of a periodic torque, a pendubot can display a variety of steady-state dynamics, including strange attractors.