The major weakness of global navigation satellite system receivers is their vulnerability to intentional and unintentional interference. Frequency domain interference suppression (FDIS) technology is one of the most useful countermeasures. The pseudo-range measurement is unbiased after FDIS filtering given an ideal analog channel. However, with the influence of the analog modules used in RF front-end, the amplitude response and phase response of the channel equivalent filter are non-ideal, which bias the pseudo-range measurement after FDIS filtering and the bias varies along with the frequency of the interference. This paper proposes an unbiased interference suppression method based on signal estimation and spectrum compensation. The core idea is to use the parameters calculated from the tracking loop to estimate and reconstruct the desired signal. The estimated signal is filtered by the equivalent filter of actual channel, then it is used for compensating the spectrum loss caused by the FDIS method in the frequency domain. Simulations show that the proposed algorithm can reduce the pseudo-range measurement bias significantly, even for channels with asymmetrical group delay and multiple interference sources at any location.

A single-electron turnstile and electrometer circuit was fabricated on a silicon-on-insulator substrate. The turnstile, which is operated by opening and closing two metal-oxide-semiconductor field-effect transistors (MOSFETs) alternately, allows current quantization at 20 K due to single-electron transfer. Another MOSFET is placed at the drain side of the turnstile to form an electron storage island. Therefore, one-by-one electron entrance into the storage island from the turnstile can be detected as an abrupt change in the current of the electrometer, which is placed near the storage island and electrically coupled to it. The correspondence between the quantized current and the single-electron counting was confirmed.

The capacity of optical transport networks has been increasing steadily and the networks are becoming more dynamic, complex, and transparent. Though it is common to use worst case assumptions for estimating the quality of transmission (QoT) in the physical layer, over provisioning results in high margin requirements. Accurate estimation on the QoT for to-be-established lightpaths is crucial for reducing provisioning margins. Machine learning (ML) is regarded as one of the most powerful methodological approaches to perform network data analysis and enable automated network self-configuration. In this paper, an artificial neural network (ANN) framework, a branch of ML, to estimate the optical signal-to-noise ratio (OSNR) of to-be-established lightpaths is proposed. It takes account of both nonlinear interference between spectrum neighboring channels and optical monitoring uncertainties. The link information vector of the lightpath is used as input and the OSNR of the lightpath is the target for output of the ANN. The nonlinear interference impact of the number of neighboring channels on the estimation accuracy is considered. Extensive simulation results show that the proposed OSNR estimation scheme can work with any RWA algorithm. High estimation accuracy of over 98% with estimation errors of less than 0.5dB can be achieved given enough training data. ANN model with R=4 neighboring channels should be used to achieve more accurate OSNR estimates. Based on the results, it is expected that the proposed ANN-based OSNR estimation for new lightpath provisioning can be a promising tool for margin reduction and low-cost operation of future optical transport networks.

Image retouching is fundamental in photography, which is widely used to improve the perceptual quality of a low-quality image. Traditional image quality metrics are designed for degraded images, so they are limited in evaluating the quality of retouched images. This letter presents a RETouched Image QUality Evaluation (RETIQUE) algorithm by measuring structure and color changes between the original and retouched images. Structure changes are measured by gradient similarity. Color colorfulness and saturation are utilized to measure color changes. The overall quality score of a retouched image is computed as the linear combination of gradient similarity and color similarity. The performance of RETIQUE is evaluated on a public Digitally Retouched Image Quality (DRIQ) database. Experimental results demonstrate that the proposed metric outperforms the state-of-the-arts.

A novel analog-computation system using quantum-dot spin glass is proposed. Analog computation is a processing method that solves a mathematical problem by applying an analogy of a physical system to the problem. A 2D array of quantum dots is constructed by mixing two-dot (antiferromagnetic interaction) and three-dot (ferromagnetic interaction) systems. The simulation results show that the array shows spin-glass-like behavior. We then mapped two combinatorial optimization problems onto the quantum-dot spin glasses, and found their optimal solutions. The results demonstrate that quantum-dot spin glass can perform analog computation and solve a complex mathematical problem.

Reduced-reference (RR) image quality assessment (IQA) algorithm aims to automatically evaluate the distorted image quality with partial reference data. The goal of RR IQA metric is to achieve higher quality prediction accuracy using less reference information. In this paper, we introduce a new RR IQA metric by quantifying the difference of discrete cosine transform (DCT) entropy features between the reference and distorted images. Neurophysiological evidences indicate that the human visual system presents different sensitivities to different frequency bands. Moreover, distortions on different bands result in individual quality degradations. Therefore, we suggest to calculate the information degradation on each band separately for quality assessment. The information degradations are firstly measured by the entropy difference of reorganized DCT coefficients. Then, the entropy differences on all bands are pooled to obtain the quality score. Experimental results on LIVE, CSIQ, TID2008, Toyama and IVC databases show that the proposed method performs highly consistent with human perception with limited reference data (8 values).

For realization of hexagonal BDD-based digital systems, active and sequential circuits including inverters, flip flops and ring oscillators are designed and fabricated on GaAs-based hexagonal nanowire networks controlled by Schottky wrap gates (WPGs), and their operations are characterized. Fabricated inverters show comparatively high transfer gain of more than 10. Clear and correct operation of hexagonal set-reset flip flops (SR-FFs) is obtained at room temperature. Fabricated hexagonal D-type flip flop (D-FF) circuits integrating twelve WPG field effect transistors (FETs) show capturing input signal by triggering although the output swing is small. Oscillatory output is successfully obtained in a fabricated 7-stage hexagonal ring oscillator. Obtained results confirm that a good possibility to realize practical digital systems can be implemented by the present circuit approach.

In this letter, fast intra mode decision algorithms for HEVC Screen Contents Coding (SCC) are proposed. HEVC SCC has been developed to efficiently code mixed contents consisting of natural video, graphics, and texts. Comparing to HEVC version 1, the SCC encoding complexity significantly increases due to the newly added intra block copy mode. To reduce the heavy encoding complexity, the evaluation orders of multiple intra modes are rearranged and several early termination schemes based on intermediate coding information are developed. Based on our evaluation, it is found that the proposed method can achieve encoding time reduction of 13∼30% with marginal coding gain or loss, compared with HEVC SCC test model 2.0 in all intra (AI) case.