A statistical model based on a partitioned Markov-chains model has previously been developed to represent time domain behavior of the asynchronous impulsive noise over a broadband power line communication (PLC) network. However, the estimation of its model parameters using the Simplex method can easily trap the final solution at a local optimum. This study proposes an estimation scheme based on the genetic algorithm (GA) to overcome this difficulty. Experimental results show that the proposed scheme yields estimates that more closely match the experimental data statistics.

Quantifying user satisfaction is essential, because the results can help service providers deliver better services. In this work, we propose a generalizable methodology, based on survival analysis, to quantify user satisfaction in terms of session times, i.e., the length of time users stay with an application. Unlike subjective human surveys, our methodology is based solely on passive measurement, which is more cost-efficient and better able to capture subconscious reactions. Furthermore, by using session times, rather than a specific performance indicator, such as the level of distortion of voice signals, the effects of other factors like loudness and sidetone, can also be captured by the developed models. Like survival analysis, our methodology is characterized by low complexity and a simple model-developing process. The feasibility of our methodology is demonstrated through case studies of ShenZhou Online, a commercial MMORPG in Taiwan, and the most prevalent VoIP application in the world, namely Skype. Through the model development process, we can also identify the most significant performance factors and their impacts on user satisfaction and discuss how they can be exploited to improve user experience and optimize resource allocation.

Pattern extraction is an indispensable step in bare printed circuit board (PCB) inspection and plays an important role in automatic inspection system design. A good approach for pattern definition and extraction will make the following PCB diagnosis easy and efficient. The window-based technique has great potential in PCB patterns extraction due to its simplicity. The conventional window-based pattern extraction methods, such as Small Seeds Window Extraction method (SSWE) and Large Seeds Window Extraction method (LSWE), have the problems of losing some useful copper traces and splitting slanted-lines into too many small similar windows. These methods introduce the difficulty and computation intensive in automatic inspection. In this paper, a novel method called Contour Based Window Extraction (CBWE) algorithm is proposed for improvement. In comparison with both SSWE and LSWE methods, the CBWE algorithm has several advantages in application. Firstly, all traces can be segmented and enclosed by a valid window. Secondly, the type of the entire horizontal or vertical line of copper trace is preserved. Thirdly, the number of the valid windows is less than that extracted by SSWE and LSWE. From the experimental results, the proposed CBWE algorithm is demonstrated to be very effective in basic pattern extraction from bare PCB image analysis.

In wireless communications, a smart antenna system utilizes an antenna array to acquire the spatial signatures of transmitted signals. This system uses the difference in the spatial signatures or the direction of arrival (DOA) of signals to correctly obtain the desired signal. This can reduce co-channel interference, mitigate the fading phenomenon caused by multipath transmissions, improve the communication quality and increase the system capacity. The purpose of this paper is to evaluate the performance of smart antennas using four beamforming algorithms applied to a wideband code division multiple access (W-CDMA) system. The simulation results show that, based on the same power consumption at the transceiver and using a Rake receiver, a W-CDMA system with a smart antenna can operate at a lower bit error rate at the specific signal to noise ratio (SNR). Moreover, the smart antenna system accommodates more users at the specific signal to interference ratio (SIR), even though a certain angle difference exists between the actual DOAs and the estimated DOAs.

In this work, a simple method for extracting MOSFET threshold voltage, effective channel length and channel mobility by using S-parameter measurement is presented. In the new method, the dependence between the channel conductivity and applied gate voltage of the MOSFET device is cleverly utilized to extract the threshold voltage, while biasing the drain node of the device at zero voltage during measurement. Moreover, the effective channel length and channel mobility can also be obtained with the same measurement. Furthermore, all the physical parameters can be extracted directly on the modeling devices without relying on specifically designed test devices. Most important of all, only one S-parameter measurement is required for each device under test (DUT), making the proposed extraction method promising for automatic measurement applications.

In recent years, the increasing complexity of deep network structures has hindered their application in small resource constrained hardware. Therefore, we urgently need to compress and accelerate deep network models. Channel pruning is an effective method to compress deep neural networks. However, most existing channel pruning methods are prone to falling into local optima. In this paper, we propose a channel pruning method via Improved Grey Wolf Optimizer Pruner which called IGWO-Pruner to prune redundant channels of convolutional neural networks. It identifies pruning ratio of each layer by using Improved Grey Wolf algorithm, and then fine-tuning the new pruned network model. In experimental section, we evaluate the proposed method in CIFAR datasets and ILSVRC-2012 with several classical networks, including VGGNet, GoogLeNet and ResNet-18/34/56/152, and experimental results demonstrate the proposed method is able to prune a large number of redundant channels and parameters with rare performance loss.

In this work, fluctuation patterns of ReRAM current are classified automatically by proposed fluctuation pattern classifier (FPC). FPC is trained with artificially created dataset to overcome the difficulties of measured current signals, including the annotation cost and imbalanced data amount. Using FPC, fluctuation occurrence under different write conditions is analyzed for both HRS and LRS current. Based on the measurement and classification results, physical models of fluctuations are established.

Nonprimitive non-narrow-sense BCH codes have been studied by many scholars. In this paper, we utilize nonprimitive non-narrow-sense BCH codes to construct a family of asymmetric quantum codes and two families of quantum convolutional codes. Most quantum codes constructed in this paper are different from the ones in the literature. Moreover, some quantum codes constructed in this paper have good parameters compared with the ones in the literature.