On coherence of quantum operations by using Choi-Jamio{\l}kowski isomorphism

In quantum information, most information processing processes involve quantum channels. One manifestation of a quantum channel is quantum operation acting on quantum states. The coherence of quantum operations can be considered as a quantum resource, which can be exploited to perform certain quantum tasks. From the viewpoint of Choi-Jamio{\l}kowski isomorphism, we study the coherence of quantum operations in the framework of resource theory. We define the phase-out superoperation and give the operation which transforms the Choi-Jamio{\l}kowski state of a quantum operation to the Choi-Jamio{\l}kowski state of the another quantum operation obtained by using the phase-out superoperation to act on the quantum operation. The set of maximally incoherent superoperations, the set of nonactivating coherent superoperations and the set of de-phase incoherent superoperations are defined and we prove that these sets are closed to compound operation and convex combination of quantum superoperations. Further, we introduce the fidelity coherence measure of quantum operations and obtain the exact form of the fidelity coherence measure of the unitary operations on the single qubit.Comment: 10 pages, no figur

An Algorithm for Idle-State Detection in Motor-Imagery-Based Brain-Computer Interface

For a robust brain-computer interface (BCI) system based on motor imagery (MI), it should be able to tell when the subject is not concentrating on MI tasks (the “idle state”) so that real MI tasks could be extracted accurately. Moreover, because of the diversity of idle state, detecting idle state without training samples is as important as classifying MI tasks. In this paper, we propose an algorithm for solving this problem. A three-class classifier was constructed by combining two two-class classifiers, one specified for idle-state detection and the other for these two MI tasks. Common spatial subspace decomposition (CSSD) was used to extract the features of event-related desynchronization (ERD) in two motor imagery tasks. Then Fisher discriminant analysis (FDA) was employed in the design of two two-class classifiers for completion of detecting each task, respectively. The algorithm successfully provided a way to solve the problem of “idle-state detection without training samples.” The algorithm was applied to the dataset IVc from BCI competition III. A final result with mean square error of 0.30 was obtained on the testing set. This is the winning algorithm in BCI competition III. In addition, the algorithm was also validated by applying to the EEG data of an MI experiment including “idle” task

Novel Wavelet Threshold Denoising Method in Axle Press-Fit Zone Ultrasonic Detection

Axles are important part of railway locomotives and vehicles. Periodic ultrasonic inspection of axles can effectively detect and monitor axle fatigue cracks. However, in the axle press-fit zone, the complex interface contact condition reduces the signal-noise ratio (SNR). Therefore, the probability of false positives and false negatives increases. In this work, a novel wavelet threshold function is created to remove noise and suppress press-fit interface echoes in axle ultrasonic defect detection. The exponential threshold function proposed by Andria [1] can\u27t get a gradual curve for later optimum searching process; and the novel wavelet threshold function with two variables is designed to ensure the precision of optimum searching process. Based on the positive correlation between the correlation coefficient and SNR [2] and with the experiment phenomenon that the defect and the press-fit interface echo have different axle-circumferential correlation characteristics, a discrete optimum searching process for two undetermined variables in novel wavelet threshold function is conducted. The performance of the proposed method is assessed by comparing it with traditional threshold methods using real data. The statistic results of the amplitude and the peak SNR of defect echoes show that the proposed wavelet threshold denoising method not only maintains the amplitude of defect echoes but also has a higher peak SNR

STUDY ON EXTRACTION PROCESS OF TANNINS FROM SEMEN CUSCUTAE AND THEIR ANTI-PAPILLOMA ACTIVITY

The objective of this paper was to study the extraction methods of tannin constituents from Semen Cuscutae and their anti-papilloma effects. Single factor test and orthogonal design methods were used to determine the optimal extraction method; the mouse skin papilloma model induced by DMBA/croton oil was established, which was a classic two-stage carcinogenesis model being used to observe and evaluate the anti-carcinogenic effects of tannins extracted from Semen Cuscutae in different stages. The optimal extraction method of Semen Cuscutae was a 20-fold volume of solvent, a temperature of 50 oC, three times of extraction, with 20 min each, skin papilloma experiment revealed that the number of bearing tumors gradually reduced, and the inhibition rate gradually increased with the increase of dose, in the high-dose group, its inhibition rate reached 70.2%. Tannin extract from Semen Cuscutae has an obvious inhibitory effect on skin papilloma development

ANTI-INFLAMMATORY AND ANALGESIC ACTIVITY OF R.A.P. (RADIX ANGELICAE PUBESCENTIS) ETHANOL EXTRACTS

The objective of this paper was to study the anti-inflammatory and analgesic effects of Radix Angelicae Pubescentis (R.A.P) ethanol extracts. Three classic anti-inflammatory models and two analgesic models were used in this research. In anti-inflammatory tests, all the extracts have a certain inhibition on the acute inflammation induced by xylene, however, 60% ethanol extract significantly inhibited the inflammation in the three models. In analgesic experiment, compared with the blank control group, the comparisons between R.A.P. groups and control group had significant difference (p﹤0.01). The incubation period in mouse writhing test or the tail-curl immersion tests could be extended greatly

The Early Stage Wheel Fatigue Crack Detection Using Eddy Current Pulsed Thermography

The in-service wheel-set quality is one of critical challenges for railway safety, especially for the high-speed train. The defect in wheel tread, initiated by rolling contact fatigue (RCF) damage, is one of the most significant phenomena and has serious influence on rail industry. Eddy current pulsed thermography (ECPT) is studied to compensate the Ultrasonic Testing (UT) method for detection these early stage of fatigue cracks in wheel tread. This paper proposes several induction coils, such as linear coil, Yoke coil and Helmholtz coils, based ECPT method to meet the imaging of multiple cracks and irregular surface in wheel tread through numerical simulation and experimental results. Some features are extracted and studied also to quantify the fatigue crack in term of UT and ECPT. The proposed method greatly enhances the capability for cracks detection and quantitative evaluation compared with previous Non-Destructive Testing (NDT) method in railway

High-performance cVEP-BCI under minimal calibration

The ultimate goal of brain-computer interfaces (BCIs) based on visual modulation paradigms is to achieve high-speed performance without the burden of extensive calibration. Code-modulated visual evoked potential-based BCIs (cVEP-BCIs) modulated by broadband white noise (WN) offer various advantages, including increased communication speed, expanded encoding target capabilities, and enhanced coding flexibility. However, the complexity of the spatial-temporal patterns under broadband stimuli necessitates extensive calibration for effective target identification in cVEP-BCIs. Consequently, the information transfer rate (ITR) of cVEP-BCI under limited calibration usually stays around 100 bits per minute (bpm), significantly lagging behind state-of-the-art steady-state visual evoked potential-based BCIs (SSVEP-BCIs), which achieve rates above 200 bpm. To enhance the performance of cVEP-BCIs with minimal calibration, we devised an efficient calibration stage involving a brief single-target flickering, lasting less than a minute, to extract generalizable spatial-temporal patterns. Leveraging the calibration data, we developed two complementary methods to construct cVEP temporal patterns: the linear modeling method based on the stimulus sequence and the transfer learning techniques using cross-subject data. As a result, we achieved the highest ITR of 250 bpm under a minute of calibration, which has been shown to be comparable to the state-of-the-art SSVEP paradigms. In summary, our work significantly improved the cVEP performance under few-shot learning, which is expected to expand the practicality and usability of cVEP-BCIs.Comment: 35 pages, 5 figure