PVT‐compensated low‐voltage and low‐power CMOS LNA for IoT applications

In this paper, a low‐noise amplifier (LNA) with process, voltage, and temperature (PVT) compensation for low power dissipation applications is designed. When supply voltage and LNA bias are close to the subthreshold, voltage has significant impact on power reduction. At this voltage level, the gain is reduced and various circuit parameters become highly sensitive to PVT variations. In the proposed LNA circuit, in order to enhance efficiency at low supply voltage, the cascade technique with g_m boosting is used. To improve circuit performance when in the subthreshold area, the forward body bias technique is used. Also, a new PVT compensator is suggested to reduce sensitivity of different circuit's parameters to PVT changes. The suggested PVT compensator employs a current reference circuit with constant output regarding temperature and voltage variations. This circuit produces a constant current by subtracting two proportional to absolute temperature currents. At a supply voltage of 0.35 V, the total power consumption is 585 μW. In different process corners, in the proposed LNA with PVT compensator, gain and noise figure (NF) variations are reduced 10.3 and 4.6 times, respectively, compared to a conventional LNA with constant bias. With a 20% deviation in the supply voltage, the gain and noise NF variations decrease 6.5 and 34 times, respectively.     

Introducing an Efficient Statistical Model for Automatic Modulation Classification

Journal of Signal Processing Systems - Nowadays, Automatic Modulation Classification (AMC) plays an important role in many applications of cooperative and non-cooperative communication such as...     

A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network

One of the undesirable phenomena in the surface mines, which results in various hazards for human and facilities, is flyrock. It seems that the careful study of the subject and its effects on the environment can affect the control of flyrock hazards in the studied area. Therefore, the use of intelligent models and methods which are capable of predicting and simulating the risk of flyrock can be considered as an appropriate solution in this regard. The current research was conducted using nonlinear models and Monte Carlo (MC) simulation. The data used in this study consist of 260 samples of rock thrown from a mine in Malaysia. The parameters used in these models include hole’s diameter (D), hole’s depth (HD), burden to spacing (BS), stemming (ST), maximum charge per delay (MC), and powder factor (PF). At first, multiple regression analysis (MRA) and artificial neural network (ANN) models were used in order to develop a non-linear relationship between dependent and independent parameters. The ANN model was an appropriate predictor of flyrock in the mine. Then using the best implemented model of ANN, the flyrock environmental phenomenon was simulated using MC technique. MC simulation showed a proper level of accuracy of flyrock ranges in the mine. Using this simulation, it can be concluded with 90% accuracy that the Flyrock phenomenon does not exceed 331 m. Under these conditions, this simulation can be used for various areas requiring risk assessment. Finally, a sensitive analysis was carried out on data. This analysis showed MC has the greatest effect on flyrock.     

Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance

Engineering with Computers - Prediction of tunnel boring machine (TBM) performance parameters can be caused to reduce the risks associated with tunneling projects. This study is aimed to introduce...     

Finding Diachronic Like‐Minded Users

User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, just to name a few, they do not necessarily succeed at pulling like‐minded users that share the same interests together. Therefore, researchers have explored the topical similarity of social content to build like‐minded communities of users. In this article, following the topic‐based approaches, we are interested in identifying communities of users that share similar topical interests with similar temporal behavior. More specifically, we tackle the problem of identifying temporal (diachronic) topic‐based communities, i.e., communities of users who have a similar temporal inclination toward emerging topics. To do so, we utilize multivariate time series analysis to model the contributions of each user toward emerging topics. Further, our modeling is completely agnostic to the underlying topic detection method. We extract topics of interest by employing seminal topic detection methods; one graph‐based and two latent Dirichlet allocation‐based methods. Through our experiments on Twitter data, we demonstrate the effectiveness of our proposed temporal topic‐based community detection method in the context of news recommendation, user prediction, and document timestamp prediction applications, compared with the nontemporal as well as the state‐of‐the‐art temporal approaches.     

Understanding the Time and Frequency Varying Characteristics of a Multipath Wireless Channel

Designing a spectrally efficient wireless channel requires a comprehensive understanding of its time and frequency varying characteristics, making it a stochastic medium of communication. These characteristics become more challenging to cater at the receiving terminal in a multipath transmission. This is because of the fading effect and Doppler shift of the transmitted frequency, specifically in cellular mobile radio systems and vehicle to vehicle communications. This paper presents the modeling, simulation, and then characterization of a cellular mobile radio multipath channel over its time and frequency varying fading gain. For this purpose, a discrete-time Finite Impulse Response (FIR) type filter of such a channel is designed, modeled, and simulated using time and frequency varying characteristics of the received signal. The simulated channel response is further analyzed in terms of coherence bandwidth, coherence time, delay spread, Doppler spread, and symbol time.

     

Development of a method for chlorophyll removal from canola oil using mineral acids

Because of the high level of chlorophyll-type compounds found in canola oil, bleaching is an important and critical step in the canola oil refining process. In this study, a new method for reducing the chlorophyll-type impurities prior to the bleaching step was developed. This method is based on precipitating the chlorophyll compounds with mineral acids. Concentrations of chlorophyll-type compounds of up to 30 ppm could be reduced to amounts of less than 0.01 ppm by mixing the crude canola oil with a 0.4 wt% mixture of phosphoric and sulfuric acids (2:0.75, vol/vol) for 5 min at 50°C. Centrifugation and filtration also were examined as two main methods for separating the chlorophyll precipitates. The results showed that filtration by a precoated textural filter with filter-aid clay could separate the precipitates as well as the centrifugation method.     

A high‐voltage gain nonisolated noncoupled inductor based multi‐input DC‐DC topology with reduced number of components for renewable energy systems

This paper proposes a modular nonisolated noncoupled inductor‐based high‐voltage gain multi‐input DC‐DC converter. Despite the high‐voltage gain of the proposed topology, the average of normalized voltage stress (NVS) on its switches/diodes is low. This property leads to less loss and cost of switches/diodes. Using the same number of components, the proposed topology produces higher voltage gains, in comparison with recently presented high step‐up topologies. Also, the proposed topology utilizes less number of components (capacitors, inductors, diodes, and switches) for producing a desired voltage gain, which can reduce the size, mass, cost, complexity, and losses and improve the efficiency of converter. Continuous current of input sources is another main advantage of the proposed topology. All the abovementioned characteristics have made the proposed topology very suitable for renewable energy systems (or even hybrid/electric vehicles). Design considerations of the proposed topology have also been presented. For better evaluation, the proposed topology has been compared with some of recently presented high step‐up structures, from viewpoints of producible voltage gain, number of components, and normalized voltage stress (NVS) on switches/diodes. Finally, the prototype of 2‐input version has been experimentally implemented. Obtained experimental results confirm appropriate performance of the proposed topology.     

Effect of seed layers on low-temperature,chemical bath deposited ZnO nanorods-based near UV-OLED performance

Low-temperature wet chemical bath deposition (CBD) method is one of the most efficient and least hazardous solution-based techniques which is widely employed to grow ZnO NRs. In CBD method, a seed layer is usually deposited on the substrate. In this paper, high quality ZnO and aluminum doped ZnO (AZO) seed layers are sputtered on the indium tin oxide (ITO) coated glass. In continue, aligned ZnO NRs are grown on the AZO and ZnO seed layers via CBD technique. The effect of the growth time and seed layer on the physical properties of as-grown ZnO NRs are investigated. According to the results, the seed layer plays an essential role on the growth orientation and growth rate of the ZnO NRs. The ZnO NRs grown on AZO seed layer are more aligned rather than ZnO seed layer due to their higher texture coefficients. The relative photoluminescence (PL) intensity ratio of near band emission (NBE) to deep level emission (DLE) (I_NBE/I_DLE) for the ZnO NRs grown on AZO and ZnO seed layers are calculated as 7.45 and 2.62, respectively. To investigate the performance of the as-grown ZnO NRs, near ultraviolet organic light-emitting diodes (UV-OLEDs) using ZnO NRs array as n-type material and poly [2-methoxy-5-(2-ethyl-hexyloxy)-1,4-phenylene-vinylene] (MEH-PPV) conjugated polymer as p-type material have been fabricated. The total concentration of traps ($N_t$), the characteristic energies ($E_t$) and the turn-on voltages for the devices with the structures of ITO/AZO/ZnO NRs/MEH-PPV/Al (device A) and ITO/ZnO/ZnO NRs/MEH-PPV/Al (device B) are attained 7.65 × 10¹⁶ and 7.75 × 10¹⁶ cm^?3, 0.232 and 0.206 eV, 23 and 21 V, respectively. Moreover, based on the electroluminescence (EL) spectra, the NBE peaks for device A and B are obtained nearly in the wavelengths of 382 and 388 nm, respectively. Finally, various charge carrier transportation processes of prepared UV-OLEDs have been studied, systematically.     

Fabricating and robust artificial neural network modeling nanoscale polyurethane fiber using electrospinning method

With regard to the fact that currently there is no comprehensive method to predict diameter of polyurethane/solvent fiber from electrospinning, in this study, diameter prediction of polyurethane/solvent fiber was conducted using neural networks and an error of 166 nm was observed. This error shows that artificial neural networks (ANNs) can predict diameter of electrospinning polyurethane fibers well. Then, considering weak repeatability nature of electrospinning in fabricating fibers with desired diameter, least mean square is used to improve stability of neural network model that shows an error of 113 nm, which represented better results compared to common ANN. To investigate the effect of each one of parameters affecting fiber diameter, sensitivity analysis was conducted. Along with this predicting model, sensitivity analysis can be used to reduce parameters space before conducting future studies. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 45116.