This paper deals with the design of an indoor optical tracking system based on commercially available off-the-shelf products. In the proposed system, four V2 NoIR Raspberry cameras are connected to various Raspberry Pi boards (Model 3B, 3B+, and 4) as capture stations. In this work, algorithms for clock synchronization, rapid contour extraction, and intrinsic camera calibration are discussed. The size, layout, illumination, and safety of an arena are also addressed, as well as construction issues like non-uniform lighting or noisy reflections. The system's accuracy is sub-centimeter at a frame rate of 100Hz, which is comparable to the performance of the proprietary and commercially available optical tracking systems. These results demonstrate that the proposed solution is feasible and show the correctness of the suggested methodology.

Bio-medical signal acquisition systems (or simply bio-medical systems) are commonly used during the diagnosis, monitoring, and treatment of patients. The Electrocardiograph (ECG) is an example of this type of system. It is necessary to submit bio-medical systems to regulatory agencies for certification. Regulatory agencies conduct evaluations seeking the commercialization of high-quality systems. In a previous work, we specified an ECG model by integrating the Coloured Petri Nets (CPN) and MATLAB® tools. However, this introduced an external component dependency that limited the application and re-usability of the model during the certification process. We present in this paper a mixed-radix Fast Fourier Transform (FFT) algorithm based on CPN aiming to assist the design of bio-medical systems using CPN. We improved the design of an ECG model to show how the mixed-radix FFT algorithm solves the previous limitations, and to evaluate the effectiveness of a CPN/ML implementation. The comparison of a system under development with similar certified systems is commonly accepted by regulatory agencies. The replacement of a similar system by the open source CPN/Tools software and the available reference model can decrease costs, and development and certification time.

Stereotypic behaviours are present in both human and nonhuman primates. Usually, these behaviours are a welfare indicator. However, the stereotypic behaviours may be also a symptom of some mental disorder in the humans. A specific case is Autism Spectrum Disorder (ASD). The individuals with ASD may exhibit stereotypic behaviours through some gestures. The classic stereotyped gestures of autism are: (i) Body Rocking; (ii) Hand Flapping; and (iii) Top Spinning. This paper study the performance between two machine learning algorithms to recognition the stereotyped gestures typical of autism: (i) Hidden Markov Model [HMM]; and (ii) Support Vector Machine [SVM]. Sequence of orientations data from some joints obtained through a RGB-D (Red Green Blue - Depth) camera [Kinect®] are used for analysis. The results of these two machine learning algorithms are compared with state-of-the-art. The HMM approach proposed in this paper have shown 98.89% average recognition rate and 98.9% recall. This value is higher compared to the SVM approach and the others of art method presented.

Battery mathematical models can be employed to predict its behavior under various charging and discharging conditions, and in some applications, they are key elements for the successes of the design. In this paper, it is analyzed a lead-acid battery model for voltage and lifetime estimation. The chosen model synthesis is based on an electrical equivalent circuit, and has the features that allow it to be used in the calculation of the remaining capacity. The lifetime model employs an adaptive update mechanism during the simulation such that the battery performance is reduced depending on the operating conditions. This paper objective is to generalize the analyzed mathematical system to be used in any lead-acid battery. Both the model and the estimation algorithm are evaluated with two batteries with same voltage and nominal capacity. The generalization of equation is made by the addition of three new parameters to minimize an objective function based on the least squares error. The study is based on a specific scenario that makes the battery reached the end of its lifetime. With the addition of new parameters, the RMS error between real capacity and calculated capacity are evaluated.

This paper presents some improvements over the previously presented current-shaping control for parallel active filters. The proposed strategy allows the active filter to operate with a reduced number of sensors, and dependently of phase synchronization schemes for references generation. A new realization proposal employs a quasi-resonant system frequency adaptable to implement complex impedances, and have low interaction with harmonic components in the grid. An additional quasi-resonant scheme in a stationary reference frame is proposed to extend the current shaping control in order to achieve great mitigation capability. The energy flow control in this paper deals with further uncertainties, which results in an enhanced robust design methodology. Experimental results are presented using a 1kVA prototype.

This paper presents a control for universal active filters derived from the current-shaping control strategy previously employed for shunt active filters. It operates with a reduced number of sensors, using only two AC and one DC real time measurements. The proposed solution has natural mitigation capability for nonlinear loads, which can be further improved with a quasi-resonant scheme. The resulting nonlinear energy flow tracking is designed using a robust control framework, with guaranteed stability and optimal performance for a predefined range of operation. Experimental results are presented using a 1kVA prototype.

Exploit the flux concentration design is good alternative for designing a relatively low cost synchronous motor based on ferrite magnets. The paper discusses the design of the feedback control laws for the stator current and mechanical speed of a synchronous motor in which the magnets are placed transversely along the rotor radius. The dynamic modeling as well as the parameter identification of the motor have been performed. Simulation and experimental results are presented to demonstrate the correctness of the methodology and the feasibility of proposed motor design and its related feedback control laws.

The lifetime of a battery bank can be improved by controlling the charge and discharge of each battery aiming at equalizing the state of charge for all of them. Selecting the circuit topology and its related control system is quite important to achieve a feasible solution for charge equalization at battery banks. In this paper we evaluate several circuit topologies proposed for providing charge equalization in battery banks. Besides circuity topology, the comparative study consider the complexity of the control system.

In this article a visual feedback control system implementation is presented. The proposed system integrates a trajectory planning method into the controller of a five-degree-of-freedom manipulator aiming to simplify object handling and obstacle avoidance. The system aims the intelligent handling of several parts, generating obstacle free paths whenever possible. The workspace information is provided by a redundant vision system: a RGB camera placed over the handler actuator and a fixed RGB-D camera.

Energy efficiency has become one of the key challenges for a large class of electronic systems. Longer time between battery recharges is highly desirable for battery-powered devices such as mobile phones, digital cameras, Internet tablets, and electronic organizers. Energy efficiency is also important for electronic systems powered from the electric grid since it may reduce power consumption and the cooling requirements. Power savings are possible because electronic systems generally have an idle state, when, for example, the processor can run in a low-power state. Thus, the correct estimation of the workload model plays an essential role in the decision of which and when a power state transition should be performed by the electronic system. This paper introduces a multisize sliding window workload estimation technique for dynamic power management (DPM) in nonstationary environments. This technique reduces both the effects of identification delay and sampling error present in the previous fixed-size sliding window approach. The system is modeled by discrete-time Markov chains and the model offers a rigorous mathematical formulation of the problem and allows one to obtain an excellent trade-off between performance and power consumption.

Safety Instrumented Systems (SIS) are usually designed to prevent accidents, avoid undesirable situations and guarantee continuous operation of oil and gas production systems. An interruption in the operation can be caused by faults in sensors and/or actuators. Hence, SIS are usu ally integrated to the supervisory control system in order to use the information from sensors to prevent such unde sirable situations. In this scenario, it is important to be able to validate the SIS implementation against its spec ification in order to increase the reliability of the system. In this work a technique to improve the dependability of SIS is introduced. A method to obtain a timed automata from a ISA 5.2 specification is presented and applied to a case study provided by Petrobras (Brazilian oil company). Finally, an approach to perform automatic testing of the implementation using the generated model is discussed. The method introduced here is based on the use of the Up paal model checker and the Uppaal-TRON testing tool.

A wireless sensor network (WSN) comprises many sensor nodes each one containing a processing unit, one or more sensors, a power unit, and a radio for data communication. Nodes are power constrained, because they run on batteries, that in many cases cannot be easily replaced. This paper presents a novel dynamic power management technique, named Dynamic Power Management with Scheduled Switching Modes (DPM-SSM), derived from a more realistic analysis of the battery capacity recovery effect and the switching energy. This was only possible thanks to the application of a more realistic battery model (i.e., Rakhmatov-Vrudhula battery model). Preliminary results showed the potential for improving the battery lifetime by taking advantage of the battery recovery effect when a node transitions to a sleeping state, and mostly when transitions are scheduled after packet transmissions. DPM-SSM provides several DPM modes which are triggered depending on the battery remaining capacity. Simulations results show that DPM-SSM can provide real battery power recovery without compromising the timeliness of the applications running on the wireless sensor network.