Effect of chemical environments on palladium phthalocyanine thin film sensors for humidity analysis

Chemiresistors based on palladium phthalocyanine (PdPc) thin films were investigated as humidity sensors. The samples were thermally evaporated onto gold electrodes with a thickness about 100 nm. Optical and electrical characteristics of PdPc thin films were investigated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and electrical measurements. The SEM image demonstrates PdPc (30–60 nm) nanosized particles, and XRD pattern shows that thin films are in α-phase at room temperature. Electrical measurements also confirm that PdPc exhibit semiconducting and photoconducting behaviors, and thermal activation energies of thin films were calculated. After that, the sensitivity and reversibility of devices were investigated on exposure to 20–90% RH in various chemical environments at 293 and 323 K. The response time (35–45 s) and recovery time (75–105 s) of sensors were measured at 293 K with respect to different chemical environments. At last, the stability of devices versus different RH% and chemical environments were tested. The sensors show very good stability on exposure to RH for a period of 2 months but their stability has been reduced in ethanol, acetone, and ammonia environments.     

Sensing and identification of carbon monoxide using carbon films fabricated by methane arc discharge decomposition technique

Carbonaceous materials have recently received attention in electronic applications and measurement systems. In this work, we demonstrate the electrical behavior of carbon films fabricated by methane arc discharge decomposition technique. The current-voltage (I-V) characteristics of carbon films are investigated in the presence and absence of gas. The experiment reveals that the current passing through the carbon films increases when the concentration of CO₂ gas is increased from 200 to 800 ppm. This phenomenon which is a result of conductance changes can be employed in sensing applications such as gas sensors.     

A novel low voltage very low power CMOS class AB current output stage with ultra high output current drive capability

In this paper a novel low voltage (LV) very low power (VLP) class AB current output stage (COS) with extremely high linearity and high output impedance is presented. A novel current splitting method is used to minimize the transistors gate–source voltages providing LV operation and ultra high current drive capability. High linearity and very high output impedance are achieved employing a novel resistor based current mirror avoiding conventional cascode structures to be used. The operation of the proposed COS has been verified through HSPICE simulations based on TSMC 0.18 μm CMOS technology parameters. Under supply voltage of ±0.7 V and bias current of 5 μA, it can deliver output currents as high as 14 mA with THD better than ?53 dB and extremely high output impedance of 320 MΩ while consuming only 29 μW. This makes the proposed COS to have ultra large current drive ratio (I_outmax/I_bias or the ratio of peak output current to the bias current of output branch transistors) of 2800. By increasing supply voltage to ±0.9 V, it can deliver extremely large output current of ±24 mA corresponding to 3200 current drive ratio while consuming only 42.9 μW and exhibiting high output impedance of 350 MΩ. Interestingly, the proposed COS is the first yet reported one with such extremely high output current and a THD even less than ?45 dB. Such ultra high current drive capability, high linearity and high output impedance make the proposed COS an outstanding choice for LV, VLP and high drive current mode circuits. The superiority of the proposed COS gets more significance by showing in this work that conventional COS can deliver only ±3.29 mA in equal condition. The proposed COS also exhibits high positive and negative power supply rejection ratio (PSRR+/PSRR?) of 125 dB and 130 dB, respectively. That makes it very suitable for LV, VLP mixed mode applications. The Monte Carlo simulation results are provided, which prove the outstanding robust performance of the proposed block versus process tolerances. Favorably the proposed COS resolves the major limitation of current output stages that so far has prevented designing high drive current mode circuits under low supply voltages. In brief, the deliberate combination of so many effective novel methods presents a wonderful phenomenal COS block to the world of science and engineering.     

Prediction of carbon dioxide separation from gas mixtures in petroleum industries using the Levenberg–Marquardt algorithm

In this study, two mathematical models for hydrate formation process to separate carbon dioxide by a combination of two different kinds of organic and surfactant promoters are presented. Promoters such as sodium dodecyl sulfate, sodium dodecyl benzene sulfonate, and dodecyl trimethyl ammonium chloride as surfactant promoters; also, tetrahydrofuran, cyclopentane, 1,3-dioxolane, and 2-methyl tetrahydrofuran as organic promoters have been used in recent years. The results showed that a combination of 3000 ppm of surfactant promoters and 4 wt% organic promoters had the highest separation rate of carbon dioxide and; consequently, the investigated models were based on this optimum condition. As a matter of fact, by using these simulations the hydrate formation behavior was predicted with high accuracy; moreover, conducting consuming experiments is not essential anymore. To sum up, in the present research both Vandermonde matrix model and Levenberg-Marquardt algorithm were applied to predict the hydrate formation behavior; in addition, their results were precisely considered and validated with experimental data.     

Adaptive neural dynamic surface control of MIMO stochastic nonlinear systems with unknown control directions

In this paper, an adaptive neural output‐feedback control approach is considered for a class of uncertain multi‐input and multi‐output (MIMO) stochastic nonlinear systems with unknown control directions. Neural networks (NNs) are applied to approximate unknown nonlinearities, and K‐filter observer is designed to estimate unavailable system's states. Due to utilization of Nussbaum gain function technique in the proposed approach, the singularity problem and requirement to prior knowledge about signs of high‐frequency gains are removed, simultaneously. Razumikhin functional method is employed to deal with unknown state time‐varying delays, so that the offered control approach is free of common assumptions on derivative of time‐varying delays. Also, an adaptive neural dynamic surface control is developed; hence, explosion of complexity in conventional backstepping method is eliminated, effectively. The boundedness of all the resulting closed‐loop signals is guaranteed in probability; meanwhile, convergence of the tracking errors to adjustable compact set in the sense of mean quartic value is also proved. Finally, simulation results are shown to verify and clarify efficiency of the offered approach. Copyright © 2016 John Wiley & Sons, Ltd.     

RACER: accurate and efficient classification based on rule aggregation approach

Neural Computing and Applications - Rule-based classification is one of the most important topics in the field of data mining due to its wide applications. This article presents a novel rule-based...     

Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window

Neural Computing and Applications - This study addresses a many-to-many hub location-routing problem where the best-found locations of hubs and the best-found tours for each hub are determined with...     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.