Artificial Neural Network Based Vertical Handoff Algorithm for Reducing Handoff Latency

One of the most challenging topics for next generation wireless networks is vertical handoff concept since several wireless technologies are assumed to cooperate. Plenty of parameters related to user preferences, application requirements, and network conditions, such as; data rate, service cost, network latency, speed of mobile, battery level, interference ratio and etc. must be considered in vertical handoff process along with traditional RSSI information. In this study, a new artificial neural network based handoff decision algorithm is proposed in order to reduce the handoff latency of smart terminal deployed in aforementioned wireless heterogeneous infrastructures. The prominent parameters data rate, monetary cost and RSSI information are taken as inputs of the developed vertical handoff decision system. Performance results of the proposed system are also compared with those of classical Multiple Attribute Decision Making method Simple Additive Weighting, and of some other artificial intelligence based algorithms. According to the results obtained, the proposed neural network based vertical handoff decision algorithm is able to determine whether a handoff is necessary or not properly, and selects the best candidate access network considering the abovementioned parameters. The results also show that, the neural network based algorithm developed significantly reduces the handoff latency while the number of handoffs, which is another vital performance metric, is still reasonable.     

Probe: Noise-and-rotation resistance of Hopfield Neural Network in imaged traffic sign recall

This paper examines the noise and rotation resistance capacity of Hopfield Neural Network (HNN) given four corrupted traffic sign images. In the study, Signal-to-Noise Ratio (SNR), recall rate and pattern complexity are defined and employed to evaluate the recall performance. The experimental results indicate that the HNN possesses significant recall capacity against the strong noise corruption, and certain restoring competence to the rotation. It is also found that combining noise with rotation does not further challenge the HNN corruption resistance capability as the noise or rotation alone does.     

Effect of mold treatment by solvent on PDMS molding into nanoholes

Polydimethylsiloxane (PDMS) is the most popular and versatile material for soft lithography due to its flexibility and easy fabrication by molding process. However, for nanoscale patterns, it is challenging to fill uncured PDMS into the holes or trenches on the master mold that is coated with a silane anti-adhesion layer needed for clean demolding. PDMS filling was previously found to be facilitated by diluting it with toluene or hexane, which was attributed to the great reduction of viscosity for diluted PDMS. Here, we suggest that the reason behind the improved filling for diluted PDMS is that the diluent solvent increases in situ the surface energy of the silane-treated mold and thus the wetting of PDMS to the mold surface. We treated the master mold surface (that was already coated with a silane anti-adhesion monolayer) with toluene or hexane, and found that the filling by undiluted PMDS into the nanoscale holes on the master mold was improved despite the high viscosity of the undiluted PDMS. A simple estimation based on capillary filing into a channel also gives a filling time on the millisecond scale, which implies that the viscosity of PMDS should not be the limiting factor. We achieved a hole filling down to sub-200-nm diameter that is smaller than those of the previous studies using regular Sylgard PDMS (not hard PDMS, Dow Corning Corporation, Midland, MI, USA). However, we are not able to explain using a simple argument based on wetting property why smaller, e.g., sub-100-nm holes, cannot be filled, for which we suggested a few possible factors for its explanation.     

Neural models for quasi-static analysis of conventional and supported coplanar waveguides

Neural models based on multilayered perceptrons (MLPs) for computing the effective permittivities and the characteristic impedances of both the conventional coplanar waveguides (CCPWs) and the supported coplanar waveguides (SCPWs) are presented. Six learning algorithms, Levenberg–Marquardt (LM), Bayesian regularization (BR), quasi-Newton (QN), scaled conjugate gradient (SCG), conjugate gradient of Fletcher–Powell (CGF), and resilient propagation (RP), are used to train the MLPs. The results of neural models presented in this paper are compared with the results of the experimental works, the conformal mapping technique (CMT), the spectral domain approach (SDA), and three commercial electromagnetic simulators such as IE3D, CAPIND2D, and MMICTL. The neural results are in very good agreement with the theoretical and experimental results. When the performances of neural models are compared with each other, the best result is obtained from the MLPs trained by the LM algorithm.     

Simultaneous separation and preconcentration of Ni(II) and Cu(II) ions by coprecipitation without any carrier element in some food and water samples

A simple and highly sensitive separation and preconcentration procedure, which has minimal impact on the environment, has been developed. The procedure is based on the carrier element‐free coprecipitation of Ni(II) and Cu(II) ions by using 2‐{4‐[2‐(1H‐Indol‐3‐yl)ethyl]‐3‐(4‐chlorobenzyl)‐5‐oxo‐4,5‐dihydro‐1H‐1,2,4‐triazol‐1‐yl}‐N′‐(pyrrol‐2‐ylmethyliden)acetohydrazide (ICOTPA), as an organic coprecipitant. The levels of analyte ions were determined by flame atomic absorption spectrometry. The detection limits for Ni(II) and Cu(II) ions were found to be 0.27 and 0.58 μg L^?1, respectively, and the relative standard deviations for the analyte ions were lower than 4.0%. Spike tests and certified reference material analyses were performed to validate the method. The method was successfully applied for the determination of Ni(II) and Cu(II) ions levels in sea and stream water as liquid samples and red lentil and rice as solid samples.     

Simultaneous preconcentration of Co(II), Ni(II), Cu(II), and Cd(II) from environmental samples on Amberlite XAD-2000 column and determination by FAAS

A new method for the preconcentration of some trace metals (Co, Ni, Cu, and Cd) as complexed with ammonium pyrrolidynedithiocarbamate (APDC) was developed using a mini-column filled with Amberlite XAD-2000 resin. Metal contents were determined by flame atomic absorption spectrometry (FAAS) after the metal complexes accumulated on the resin were eluted with 1M HNO(3) in acetone. The effects of the analytical parameters such as sample pH, quantity of complexing agent, eluent type, resin quantity, sample volume, sample flow rate, and matrix ions were investigated on the recovery of the metals from aqueous solutions. The relative standard deviation (R.S.D.) of the method was <6%. The validation of the method was confirmed using two certified reference materials (CRM TMDW-500 Drinking Water and CRM SA-C Sandy Soil C). The method was successfully applied to some stream waters and mushroom samples from Eastern Black Sea Region (Trabzon city) of Turkey.     

热脱水钠硼解石在醋酸铵溶液中的溶解(英文)

钠硼解石含有大量的结晶水。采用煅烧法可以将固体组分中的一部水脱去,得到一种多孔结构物质,以提高浸出反应速率。采用醋酸铵作为浸出溶剂,研究煅烧钠硼解石样品在不同温度下的溶解情况。研究了反应参数对煅烧钠硼解石溶解的影响。研究发现,煅烧样品的溶解速率高于未煅烧样品的溶解速率。在煅烧温度低于150℃时,煅烧钠硼解石的溶解速率随着煅烧温度的升高而加快。钠硼解石的浸出反应速率符合化学反应控制模型。溶解过程中的活化能为41.5kJ/mol。