A quick neural network for computer vision of gray images

This paper presents a comparison between the traditional image processing method and the area vector concept as well as the new technique of artificial neural networks. Freeman chain coding is considered in the study, and the principle of segmentation may be based and implemented for further investigations resulting from the proposed work. The pattern recognition concept is analyzed and defined through the sigmoid function and the determination of the threshold of a gray image for an object. The block schemes for the given protocols are summarized in a single scheme for illustration and comparison purposes. The synthetic pictures are generated and investigated regarding the dependence of computer vision on the contents of the artificial neural network. The normalization technique is included to eliminate noise and zooming problems. The minimum computational time for image processing with the generated pictures is also determined. The rate of deflection in the computational time is recommended for sensing the minimum computational time according to the variation of the number of hidden units in the hidden layer. A three-layer neural network has been used. The study of gray binary imaging for color pictures is illustrated to save computational time and effort.     

Treating smokers before the quit date: can nicotine patches and denicotinized cigarettes reduce cravings?

The present study investigated whether treatment with the combination of denicotinized cigarettes and 21-mg nicotine patch for 2 weeks before a designated quit date could lessen cravings for smoking, thereby helping smokers abstain from smoking. The study was a randomized controlled clinical trial conducted at Roswell Park Cancer Institute, Buffalo, New York, in 2004 and 2005. Patients included 98 adult heavy smokers (using 20 or more cigarettes/day). Half of the subjects received 2 weeks of combination of denicotinized cigarettes (Quest 3) and 21-mg nicotine patch for 2 weeks before the quit date. The remaining smokers were switched to light cigarettes (Quest 1) during the 2 weeks before the quit date. After the quit date, all subjects received counseling for smoking cessation and were provided nicotine patches for up to 8 weeks after the quit date. Self-reported cravings for smoking, withdrawal symptoms, and smoking abstinence were measured at predetermined intervals using phone-based surveys and in clinical visits. The group that used denicotinized cigarettes and nicotine patch before quitting reported less frequent and less intense cravings for cigarettes in the 2 weeks before and after the designated quit date. Self-reported withdrawal symptoms and quit rates did not differ significantly between the groups. The use of a denicotinized cigarette combined with the nicotine patch appears to lessen cravings to smoke in the immediate postcessation period. A larger, better-powered study is needed to test if this treatment combination has merit for increasing quit rates.     

Intelligent non-linear modelling of an industrial winding process using recurrent local linear neuro-fuzzy networks

This study deals with the neuro-fuzzy (NF) modelling of a real industrial winding process in which the acquired NF model can be exploited to improve control performance and achieve a robust fault-tolerant system. A new simulator model is proposed for a winding process using non-linear identification based on a recurrent local linear neuro-fuzzy (RLLNF) network trained by local linear model tree (LOLIMOT), which is an incremental tree-based learning algorithm. The proposed NF models are compared with other known intelligent identifiers, namely multilayer perceptron (MLP) and radial basis function (RBF). Comparison of our proposed non-linear models and associated models obtained through the least square error (LSE) technique (the optimal modelling method for linear systems) confirms that the winding process is a non-linear system. Experimental results show the effectiveness of our proposed NF modelling approach.     

The Impact of Metastable Intermolrecular Nanocomposite Particles on Kinetic Decomposition of Heterocyclic Nitramines Using Advanced Solid‐Phase Decomposition Models

Journal of Inorganic and Organometallic Polymers and Materials - Surface oxygen of oxide catalyst has low coordination number; they are negatively charged. Surface oxygen can act active site for...     

A game attention model for efficient bit rate allocation in cloud gaming

The widespread availability of broadband internet access and the growth in server-based processing have provided an opportunity to run games away from the player into the cloud and offer a new promising service known as cloud gaming. The concept of cloud gaming is to render a game in the cloud and stream the resulting game scenes to the player as a video sequence over a broadband connection. To meet the stringent network bandwidth requirements of cloud gaming and support more players, efficient bit rate reduction techniques are needed. In this paper, we introduce the concept of game attention model (GAM), which is basically a game context-based visual attention model, as a means for reducing the bit rate of the streaming video more efficiently. GAM estimates the importance of each macro-block in a game frame from the player’s perspective and allows encoding the less important macro-blocks with lower bit rate. We have evaluated nine game video sequences, covering a wide range of game genre and a spectrum of scene content in terms of details, motion and brightness. Our subjective assessment shows that by integrating this model into the cloud gaming framework, it is possible to decrease the required bit rate by nearly 25 % on average, while maintaining a relatively high user quality of experience. This clearly enables players with limited communication resources to benefit from cloud gaming with acceptable quality.     

ADHESION OF ELASTOMERS: DWELL TIME EFFECTS

The strength of adhesion of elastomers to rigid substrates generally increases with time of contact. This effect has been studied for samples of butyl and chlorobutyl rubber adhering to some rigid substrates. The peel strength increased continuously over long periods of contact until in some cases failure became cohesive within the elastomer layer. At higher temperatures the strength increased more rapidly, consistent with the WLF relation governing molecular motions. It is postulated that slow molecular rearrangements occur at the interface and increase the bond strength. A criterion for the observed transition from interfacial to cohesive failure is suggested.     

Unveiling the presence of mixed oxidation states of Europium in Li7+δEuxLa3−δZr2−δO12−δ garnet and its impact on the Li-ion conductivity

Recently, lanthanides have been employed by researchers to examine their impact on the structure and properties of Li₇La₃Zr₂O₁₂ (LLZO) garnets. In this regard, we developed Europium oxide (Eu₂O₃) doped LLZO (Li_7+δEu_xLa_3−δZr_2−δO_12−δ) solid electrolyte which demonstrates a cubic phase with the symmetry of Iad (No.230) at room temperature. In this investigation, different concentrations of Eu ranging from 0.1 to 0.6 atoms per formula unit (pfu) were doped into Li₇La₃Zr₂O₁₂ to evaluate the impact of Eu on the stability of the cubic phase and thereby the ionic conductivity. The results unveiled that upon doping Eu³⁺ ions, the Eu²⁺ state is also formed and is then self-doped into the structure in which Rietveld refinement coupled with XPS, EPR, and solid-state NMR suggests that Eu³⁺ ions most probably partially occupy Zr⁴⁺ (16a) site, the Eu²⁺ ions occupy La³⁺ (24d) site, and the Li⁺ ions occupy two different sites (24d and 96h). It was further found that such a site preference induces distortion at LaO₈ polyhedrons opening up the neck for Li-ions diffusion, thereby enhancing the ionic conductivity. Moreover, it was revealed that Li-ions probably hop from 96h to 24d and then to 96h site to generate the Li-ion movement. Overall, by introducing Eu ions into the LLZO structure, an enhanced bulk ionic conductivity of 0.30 × 10⁻³ S/cm at 298 K with a minimum electronic conductivity of 2.547 × 10⁻⁹ S/cm at 298 K was achieved.     

Predictive control for voltage collapse avoidance using a modified discrete multi-valued PSO algorithm

Voltage stability is one of the most challenging concerns that power utilities are confronted with, and this paper proposes a voltage control scheme based on Model Predictive Control (MPC) to overcome this kind of instability. Voltage instability has a close relation with the adequacy of reactive power and the response of Under Load Tap Changers (ULTCs) to the voltage drop after the occurrence of a contingency. Therefore, the proposed method utilizes reactive power injection and tap changing to avoid voltage collapse. Considering discrete nature of the changes in the tap ratio and also in the reactive power injected by capacitor banks, the search area for the optimizer of MPC will be an integer area; consequently, a modified discrete multi-valued Particle Swarm Optimization (PSO) is considered to perform this optimization. Simulation results of applying the proposed control scheme to a 4-bus system confirm its capability to prevent voltage collapse.