Search results

Integrated circuit miniaturization is driven by the demand of increased speed and low power consumption, required for future computing paradigms like cognitive computing and massive data processing.
A comparison with existing experimental data proves the outlined criteria correct without exception. 2.
Sub-micrometer size metallic wires Size reduction and speed maximization have been the major drive for the steadily growing high integration density of semiconductor devices on computer chips.
Many previous electromigration data with pure metals and dilute alloys of Al, Cu, Ag, and others point toward a superior performance of single crystalline interconnects [5].
The electron density n(r) near the surface of the jellium experiences a steep, although not abrupt reduction toward an embedded vacancy.

Error bars for the hardness represent standard deviations in the respective data sets, which included at least 50 indentations per processing condition (Table 2).
The plastic strain magnitudes achieved for the chips here appear to saturate at values of a bout 2.1 and exhibited a reduction with increasing tool velocity, which was more significantly for the 20˚- than the 0˚-angle tool.
Notably, room temperature cold-rolling reductions to strains equivalent to those imparted here have been shown to result in ~30-50vol.% SIM for 316L [16].
The resulting TEM OIM data sets (e.g.
Johnson, A Constitutive model and data for metals subjected to large strains and high strain rates and high temperatures, 7th International Symposium on Ballistics (1983) p541

In this method, wavelet function is performed as kernel function for mapping input data in SVM theory.
In the training process, the data set was also trained using RBF kernel function as comparison.
In this work, WSVM can accurately recognize the conditions of gearbox from all input data except data kernel IC with 85% accuracy of training and testing.
All data set come from component analysis are accurately classified using Haar wavelet kernel and SVM.
Vapnik, Estimation Dependences Based on Empirical Data, Springer Verlag, Berlin, 1982.

It was designed a measurement chain for the noise level evaluation of the mufflers including a microphone Bruel&Kjaer type 4133, a Impulse Precision Sound Level Meter Bruel&Kjaer, type 2209, with standard filters and a multifunctional external data acquisition board type-NI DAQPad-6015 .
As is known, the noise reduction is mainly by fitting silencers, also called mufflers.
B Heywood, Internal Combustion Engine Fundamentals, in : McGraw – Hill Series (Eds), Mechanical Engineering, Library of Congress Cataloging-in-Publication Data, 1988

● Read The Serial Data The environmental data collection module achieves serial communication with Android software, and sends sensor data to the serial port.
Android application reads serial data(i.e.
,all environmental data).
The sensors collect data once per 10 seconds.
The environmental data and video data could be received by the server and stored in the database.

Introduction Polyimide coatings are successfully being used as inter-metal dielectrics [1, 2], for planarization of surface topography and overburden variation reduction [3] and for MEMS applications as structure or sacrificial material [4].
(a) SEM image of the cavity filled with polyimide and excess polyimide etched in the field area; (b) SEM image of the cavity filled with polyimide after CMP The surface profiling data of the densely placed and isolated test structures, before and after CMP are depicted in Figure 4 and 5 respectively.
The surface profiling data of the densely placed test structures, before and after CMP Fig. 5.
The surface profiling data of the isolated placed test structures, before and after CMP Conclusions The paper elaborates a wafer level polyimide filling and planarization process scheme, where the test structures are realized in 200mm wafer in different pattern densities, leading to over-burden variation of more than 6mm.

Fig. 2 : XRD data for samples prepared in air at 700oC, 750 oC and 800oC.
Fig. 3 : XRD data for samples heated between 800 and 1000oC in O2.
Fig. 4 : XRD data for samples heated between 800 and 950oC in N2.
Fig. 8 : Refined XRD data for samples that heated at 800oC in N2. .
Fig. 7 : Refined XRD data for samples that heated at 800oC in N2. .

Meanwhile, MOFAT simulator was selected to carry out numerical simulation research, and simulator accuracy was analyzed by comparing to physical simulation data.
Numerical modeling was an important means to simulate[6-8]and predict PHCs transport in subsurface, so MOFAT simulator was introduced to PHCs transport research, and used physical simulation experimental data to analysis the accuracy of the MOFAT simulator.
Numerical simulation of benzene transport （a） (b) （c） (d) Fig. 5 Benzene transport simulations after PHCs leaking at 12h(a), 24(b), 36h(c) and 48h(d) Table 2 Accuracy analysis of MOFAT simulator Simulation time RMSE (mg/g） RRMSE (%) 12h 3.643 329.0 24h 3.427 73.8 36h 4.395 184.0 48h 3.099 109.7 Average 3.641 174.1 (a) (b) Fig. 6 Benzene transport prediction after PHCs leaking at 72h(a), 96h(b) Benzene contents changed in sandbox were gotten from output data of MOFAT numerical simulator.
Compared to physical simulation experimental data, the average RMSE and RRMSE of numerical simulation were 3.641 mg/g and 174.1% respectively(Table 2).
PHCs transport prediction in saturated zone was also carry out by MOFAT simulator(fig.6), results showed that benzene contents tent to steady with reduction of concentration gradient.

The function of model simulator is building time Petri net model for collaborative design process and displaying the implement circumstance and data result with dynamic mode in simulation environment according to the setting original condition.
Output manager takes charge of system result output and display data result in dynamic.
Temporary inference rule of fair reduction is shown as follow [ ] [ ] 1 2 min max min 1 min 2 max 1 max 2 ( , ) ( ), ( ) max( ( ), ( )),max( ( ), ( )) t t t TC t TC t t TC t TC t TC t TC t → = （2） Conditional selecting relation model Conditional selecting relation structure is shown in Fig.4.
Temporary inference rule for its equivalent capability reduction is shown following: [ ] [ ] 1 2 min max min 1 max 1 min 2 max 2 ( , ) ( ), ( ) ( ( ), ( )) ( ( ), ( )) t t t TC t TC t t TC t TC t TC t TC t → = ∪ （4） Circling relation model Circling relation structure is shown in Fig.5.
Capability fair reduction and temporary inference rules for four basic structures for collaborative design process time Petri net are defined.

Clinical decision-making in health care is even now inspired by data-driven computer forecasts or suggestions.
We investigate the state of the art in relevant subjects such as data point treatment, interpretation, and simulation assessment in the framework of outcome prediction models improved utilizing data as automated health data.
Machine learning is an AI product that identifies shapes in hugeamounts of health data to make estimates for the future.
Cisler, and Michael Cantor developed machine learning classifiers for predicting mortality events in clinical trials by exploiting clinical trials' large data.
No security for user’s data 4 Artificial intelligence in healthcare Artificial intelligence Kun Hsing Yu et al.