The asymmetrical halo and dual-material gate structure is used in the surrounding-gate metal-oxide-semiconductor field effect transistor (MOSFET) to improve the performance. By treating the device as three surrounding-gate MOSFETs connected in series and maintaining current continuity, a comprehensive drain current model is developed for it. The model incorporates not only channel length modulation and impact ionization effects, but also the influence of doping concentration and vertical electric field distributions. It is concluded that the device exhibits increased current drivability and improved hot carrier reliability. The derived analytical model is verified with numerical simulation.

This paper studies a novel iterative detection algorithm for data detection in orthogonal frequency division multiplexing systems in the presence of phase noise (PHN) and channel estimation errors. By simplifying the maximum a posteriori algorithm based on the theory of variational inference, an optimization problem over variational free energy is formulated. After that, the estimation of data, PHN and channel state information is obtained jointly and iteratively. The simulations indicate the validity of this algorithm and show a better performance compared with the traditional schemes.

In this paper, an analytical threshold voltage model of the strained gate-all-around MOSFET fabricated on the Si1-xGex virtual substrate is presented by solving the two-dimensional Poisson equation. The impact of key parameters such as the strain, channel length, gate oxide thickness and radius of the silicon cylinder on the threshold voltage has been investigated. It has been demonstrated that the threshold voltage decreases as the strain in the channel increases. The threshold voltage roll-off becomes severe when increasing the Ge content in the Si1-xGex virtual substrate. The model is found to tally well with the device simulator.

There is a strong push towards the ultra-realistic presentation of multimedia contents made possible by the latest advances in computational and signal processing technologies. Three-dimensional sound presentation is necessary to convey a natural and rich multimedia experience. Promising ways to achieve this include the sound field reproduction technique known as high-order Ambisonics (HOA). While these advanced methods are now within the capabilities of consumer-level processing systems, their adoption is hindered by the lack of contents. Production and coding of the audio components in multimedia focus on traditional formats such as stereophonic sound. Mainstream audio codecs and media such as CDs or DVDs do not support advanced, rich contents such as HOA encodings. To ameliorate this problem and speed up the adoption of spatial sound technologies, this paper proposes a novel way to downmix HOA contents into a stereo signal. The resulting data can be distributed using conventional methods such as audio CDs or as the audio component of an internet video stream. The results can be listened to using legacy stereo reproduction systems. However, they include spatial information encoded as the inter-channel level and phase differences. The proposed method consists of a downmixing filterbank which independently modulate inter-channel differences at each frequency bin. The proposal is evaluated using simple test signals and found to outperform conventional methods such as matrix-encoded surround and the Ambisonics UHJ format in terms of spatial resolution. The proposal can be coupled with a previously presented method to recover HOA signals from stereo recordings. The resulting system allows for the preservation of full-surround spatial information in ultra-realistic contents when they are transferred using a stereo stream. Simulation results show that a compatible decoder can accurately recover up to five HOA channels from a stereo signal (2nd order HOA data in the horizontal plane).

This paper proposes a novel RBF training algorithm based on immune operations for dynamic problem solving. The algorithm takes inspiration from the dynamic nature of natural immune system and locally-tuned structure of RBF neural network. Through immune operations of vaccination and immune response, the RBF network can dynamically adapt to environments according to changes in the training set. Simulation results demonstrate that RBF equalizer based on the proposed algorithm obtains good performance in nonlinear time-varying channels.

Traditional two-microphone noise reduction algorithms to deal with highly nonstationary directional noises generally use the direction of arrival or phase difference information. The performance of these algorithms deteriorate when diffuse noises coexist with nonstationary directional noises in realistic adverse environments. In this paper, we present a two-channel noise reduction algorithm using a spatial information-based speech estimator and a spatial-information-controlled soft-decision noise estimator to improve the noise reduction performance in realistic non-stationary noisy environments. A target presence probability estimator based on Bayes rules using both phase difference and magnitude squared coherence is proposed for soft-decision of noise estimation, so that they can share complementary advantages when both directional noises and diffuse noises are present. Performances of the proposed two-microphone noise reduction algorithm are evaluated by noise reduction, log-spectral distance (LSD) and word recognition rate (WRR) of a distant-talking ASR system in a real room's noisy environment. Experimental results show that the proposed algorithm achieves better noises suppression without further distorting the desired signal components over the comparative dual-channel noise reduction algorithms.

In the task of face recognition, a challenging issue is the one sample problem, namely, there is only one training sample per person. Principal component analysis (PCA) seeks a low-dimensional representation that maximizes the global scatter of the training samples, and thus is suitable for one sample problem. However, standard PCA is sensitive to the outliers and emphasizes more on the relatively distant sample pairs, which implies that the close samples belonging to different classes tend to be merged together. In this paper, we propose two-stage block-based whitened PCA (TS-BWPCA) to address this problem. For a specific probe image, in the first stage, we seek the K-Nearest Neighbors (K-NNs) in the whitened PCA space and thus exclude most of samples which are distant to the probe. In the second stage, we maximize the “local” scatter by performing whitened PCA on the K nearest samples, which could explore the most discriminative information for similar classes. Moreover, block-based scheme is incorporated to address the small sample problem. This two-stage process is actually a coarse-to-fine scheme that can maximize both global and local scatter, and thus overcomes the aforementioned shortcomings of PCA. Experimental results on FERET face database show that our proposed algorithm is better than several representative approaches.

Link and node trustworthiness are important metrics in wireless ad hoc networks. Many existing wireless ad hoc network routing algorithms assume the availability of precise trustworthiness information. This, however, is an unrealistic assumption given the dynamics of wireless ad hoc networks. Therefore, a realistic method is needed to evaluate trustworthiness by mitigating uncertainty in the estimation process. In this paper, we propose a novel trustworthiness estimation model that accounts for uncertainty as well as two uncertainty mitigation schemes. We then illustrate the effectiveness of our schemes using a utility-oriented routing algorithm as a sample application. An extensive simulation study shows that these two uncertainty mitigation schemes significantly increase path stability and the long-term total benefit of the wireless ad hoc network.