Chipless RFID tags fabricated by fully printing of metallic inks

This paper reviews recent advances in fully printed chipless radio frequency identification (RFID) technology with special concern on the discussion of coding theories, ID generating circuits, and tag antennas. Two types of chipless tags, one based on time-domain reflections and the other based on frequency domain signatures, are introduced. To enable a fully printed encoding circuit, linearly tapering technique is adopted in the first type of tags to cope with parasitic resistances of printed conductors. Both simulation and measurement efforts are made to verify the feasibility of the eight-bit fully printed paper-based tag. In the second type of tags, a group of LC tanks are exploited for encoding data in frequency domain with their resonances. The field measurements of the proof-of-concept of the tag produced by toner-transferring process and flexible printed circuit boards are provided to validate the practicability of the reconfigurable ten-bit chipless RFID tag. Furthermore, a novel RFID tag antenna design adopting linearly tapering technique is introduced. It shows 40 % save of conductive ink materials while keeping the same performance for conventional half-wave dipole antennas and meander line antennas. Finally, the paper discusses the future trends of chipless RFID tags in terms of fabrication cost, coding capacity, size, and reconfigurability. We see that, coupled with revolutionary design of low-cost tag antennas, fabrication/reconfiguration by printing techniques, moving to higher frequencies to shrink tag sizes and reduce manufacturing cost, as well as innovation in ID generating circuits to increase coding capacities, will be important research topics towards item-level tracking applications of chipless RFID tags.     

Monodispersed p(2-VP) and p(2-VP-co-4-VP) particle preparation and their use as template for metal nanoparticle and as catalyst for H2 production from NaBH4 and NH3BH3 hydrolysis

The monodispersed poly(2-vinyl pyridine) (p(2-VP)) and poly(2-vinyl pyridine-co-4-vinyl pyridine) (p(2-VP-co-4-VP)) particles of different compositions were synthesized by a surfactant-free emulsion polymerization system using divinyl benzene (DVB) as cross-linker. The diameter of p(2-VP) and p(2-VP-co-4-VP) particles were measured between 370 and 530 nm. Co, Ni and Cu metal nanoparticles were prepared inside these microgels after quaternization with HCl and loading of metal salts, such as CoCl₂, NiCl₂, and CuCl₂, in ethyl alcohol followed by reduction with NaBH₄. The prepared metal nanoparticles within these particles were used as catalyst for H₂ production via hydrolysis of NaBH₄ and NH₃BH₃. Various parameters of the polymeric microgels such as template, metal types, reuse, the amount of NaOH, and temperature were investigated. From hydrolysis reactions the activation energy (E_a), enthalpy (ΔH), and entropy (ΔS) were calculated for Co metal nanoparticles as catalyst for the NaBH₄ hydrolysis reaction in the temperature range of 0–50 °C. The activation parameters of NaBH₄ hydrolysis catalyzed by Co nanoparticle composite systems were calculated as 46.44 ± 1.1 kJ mol⁻¹ for E_a, 36.39 ± 6.5 kJ mol⁻¹ for ΔH and −170.56 ± 20.1 kJ mol⁻¹ K⁻¹ for ΔS.     

The effect of plasma electrolytic oxidation process parameters on the tribocorrosion properties of TiO2 coatings

Despite the fact that Titanium and its alloys are materials which have excellent corrosion-resistant properties, they have poor wear and friction performance under tribological conditions. The aim of this study is to find suitable parameters for the surface treatment of Cp-Ti substrates which are used under saline environment. In this study, TiO₂ coatings were grown on Cp-Ti substrates at different frequencies which are parameters of the coating process. Due to its low cost and ability to achieve high thicknesses, The Plasma Electrolytic Oxidation (PEO) method was applied to grow TiO₂ coatings. The microstructures, morphology, and crystallographic structure were analyzed using SEM and XRD. Tribocorrosion properties of the coatings were investigated using a combination of the pin-on-disk wear test and potentiodynamic polarization test units. The frequency is known to have a strong impact on the PEO process. The impacts of frequency changes on the PEO coating performance were examined under a constant voltage. As result, the increase of the frequency caused smaller pores and cracks in the surface morphology of the coating and at the same time this yielded an increment on the tribocorrosion behavior of the coating.     

Comparison of cost and treatment efficiency of solar assisted advance oxidation processes for textile dye bath effluent

The study investigated the efficiency and cost effectiveness of solar-assisted photochemical processes in comparison with advance oxidation processes (AOPs) for the textile effluents treatment. Efficiency of UV irradiation alone for one hour in removing color was almost double in comparison to solar radiation alone for effluents of different dye concentrations (E1>E2>E3). For coupled UV/H₂O₂ process, there was higher color removal efficiency obtained for effluent E₃ (85%) as compared to E₂ (70%) and E₁ (57%), while E1 showed higher COD removal efficiency (70%) as compared to E₂ (50%) and E₃ (62%). However, the efficiency of solar/H₂O₂ for COD removal was comparable to UV/H₂O₂, i.e., E₂ (57%) and E₃ (53%). In the case of UV and solar-assisted photo-Fenton processes, removal efficiency of the UV process was further increased as approached to almost 90% removal for E₁; on the other hand, the solar-assisted process efficiency remained the same. The relative efficiencies of AOPs were found to be in the order of UV assisted photo-Fenton process>UV/H₂O₂>UV alone. Although, solar-assisted Fenton treatments were relatively low and slow but without any energy consumption in comparison to high energy consumption of UV. Among the UV processes, UV assisted photo-Fenton treatment appeared to have better color removal efficiency with energy requirements of 5 kWh/m³, 8 kWh/m³ and 3 kWh/m³ for E₁, E₂ and E₃, respectively.     

On three intelligent systems: dynamic neural,fuzzy, and wavelet networks for training trajectory

Intelligent systems cover a wide range of technologies related to hard sciences, such as modeling and control theory, and soft sciences, such as the artificial intelligence (AI). Intelligent systems, including neural networks (NNs), fuzzy logic (FL), and wavelet techniques, utilize the concepts of biological systems and human cognitive capabilities. These three systems have been recognized as a robust and attractive alternative to the some of the classical modeling and control methods. The application of classical NNs, FL, and wavelet technology to dynamic system modeling and control has been constrained by the non-dynamic nature of their popular architectures. The major drawbacks of these architectures are the curse of dimensionality, such as the requirement of too many parameters in NNs, the use of large rule bases in FL, the large number of wavelets, and the long training times, etc. These problems can be overcome with dynamic network structures, referred to as dynamic neural networks (DNNs), dynamic fuzzy networks (DFNs), and dynamic wavelet networks (DWNs), which have unconstrained connectivity and dynamic neural, fuzzy, and wavelet processing units, called neurons, feurons, and wavelons, respectively. The structure of dynamic networks are based on Hopfield networks. Here, we present a comparative study of DNNs, DFNs, and DWNs for non-linear dynamical system modeling. All three dynamic networks have a lag dynamic, an activation function, and interconnection weights. The network weights are adjusted using fast training (optimization) algorithms (quasi-Newton methods). Also, it has been shown that all dynamic networks can be effectively used in non-linear system modeling, and that DWNs result in the best capacity. But all networks have non-linearity properties in non-linear systems. In this study, all dynamic networks are considered as a non-linear optimization with dynamic equality constraints for non-linear system modeling. They encapsulate and generalize the target trajectories. The adjoint theory, whose computational complexity is significantly less than the direct method, has been used in the training of the networks. The updating of weights (identification of network parameters) is based on Broyden–Fletcher–Goldfarb–Shanno method. First, phase portrait examples are given. From this, it has been shown that they have oscillatory and chaotic properties. A dynamical system with discrete events is modeled using the above network structure. There is a localization property at discrete event instants for time and frequency in this example.     

Potential functions based sampling heuristic for optimal path planning

Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to the large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the potential function based-RRT* that incorporates the artificial potential field algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.     

Relative nocturnal hypertension in children with insulin-dependent diabetes mellitus

Twenty-four-hour blood pressure and heart rate measurements were carried out in 14 newly diagnosed diabetics and in 28 diabetics with 5-13 years' duration of the disease; 8 healthy children were used as controls. Mean arterial blood pressure increased at night in 5, decreased slightly (less than 10%) in 5 and decreased markedly (more than 10%) in 18 diabetics with longer duration of the disease. The diurnal-nocturnal differences in heart rates were significantly lower in diabetics with relative "nocturnal hypertension" compared to the control group (p < 0.05). A significant negative correlation was found between maximal arterial blood pressure during physical exercise and the diurnal-nocturnal differences in mean arterial blood pressure in diabetics (r = -0.58; p < 0.02). In conclusion, we found elevated nocturnal blood pressure in a subgroup of children with longer duration of diabetes and without increased albumin excretion. However, longitudinal studies of blood pressure profiles are needed to identify the candidates for diabetic vasculopathy among diabetic children.     

Intelligent adaptive nonlinear flight control for a high performance aircraft with neural networks

This paper describes the development of a neural network (NN) based adaptive flight control system for a high performance aircraft. The main contribution of this work is that the proposed control system is able to compensate the system uncertainties, adapt to the changes in flight conditions, and accommodate the system failures. The underlying study can be considered in two phases. The objective of the first phase is to model the dynamic behavior of a nonlinear F-16 model using NNs. Therefore a NN-based adaptive identification model is developed for three angular rates of the aircraft. An on-line training procedure is developed to adapt the changes in the system dynamics and improve the identification accuracy. In this procedure, a first-in first-out stack is used to store a certain history of the input-output data. The training is performed over the whole data in the stack at every stage. To speed up the convergence rate and enhance the accuracy for achieving the on-line learning, the Levenberg-Marquardt optimization method with a trust region approach is adapted to train the NNs. The objective of the second phase is to develop intelligent flight controllers. A NN-based adaptive PID control scheme that is composed of an emulator NN, an estimator NN, and a discrete time PID controller is developed. The emulator NN is used to calculate the system Jacobian required to train the estimator NN. The estimator NN, which is trained on-line by propagating the output error through the emulator, is used to adjust the PID gains. The NN-based adaptive PID control system is applied to control three angular rates of the nonlinear F-16 model. The body-axis pitch, roll, and yaw rates are fed back via the PID controllers to the elevator, aileron, and rudder actuators, respectively. The resulting control system has learning, adaptation, and fault-tolerant abilities. It avoids the storage and interpolation requirements for the too many controller parameters of a typical flight control system. Performance of the control system is successfully tested by performing several six-degrees-of-freedom nonlinear simulations.