Digital coherent detection research on Brillouin optical time domain reflectometry with simplex pulse codes

The digital coherent detection technique has been investigated without any frequency-scanning device in the Brillouin optical time domain reflectometry （BOTDR）, where the simplex pulse codes are applied in the sensing system. The time domain signal of every code sequence is collected by the data acquisition card （DAQ）. A shift-averaging technique is applied in the frequency domain for the reason that the local oscillator （LO） in the coherent detection is fix-frequency deviated from the primary source. With the 31-bit simplex code, the signal-to-noise ratio （SNR） has 3.5-dB enhancement with the same single pulse traces, accordant with the theoretical analysis. The frequency fluctuation for simplex codes is 14.01 MHz less than that for a single pulse as to 4-m spatial resolution. The results are believed to be beneficial for the BOTDR performance improvement.

Improving Image Copy-Move Forgery Detection with Particle Swarm Optimization Techniques

Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.

Symbol detection based on Voronoi surfaces with emphasis on superposition modulation

A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of bits per data symbol. This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector. Thus it is important to design new detection algorithms which combine a sufficient performance with low complexity. In this contribution, a detection algorithm for two- dimensional digital modulation schemes which cannot be split-up into real and imaginary parts （like phase shift keying and phase-shifted snperposition modulation （PSM）） is proposed with emphasis on PSM with equal power allocation. This algorithm exploits the relationship between Max-Log-APP detection and a Voronoi diagram to determine planar surfaces of the soft outputs over the entire range of detector input values. As opposed to state-of-the-art detectors based on Voronoi surfaces, a priori information is taken into account, enabling iterative processing. Since the algorithm achieves Max-Log-APP performance, even in the presence of a priori information, this implies a great potential for complexity reduction compared to the classical APP detection.

100G Transport Systems: Technology Bench-Mark Testing in China and Evolution to Terabit/s Interfaces

To meet the exponentially growing demand fbr bandwidth in Optical Transport Networks （OTNs）, 100-Gb/s （100G） coherent technology based oil Polarization-Division Multiplexed Quadrature Phase Shift Keying （PDM-QPSK）, which enables the capacity of a Wavelength Division Multiplexing （WDM） system to approach 10 Tb/s, is being widely deployed globally. As the first vendor to intro- duce a single-carrier 100G solution, A1- catel-Lucent has developed key transponder and network management technologies, which are reviewed in this paper together with their commercial evolution to 400G. Focusing on the Chinese market, we also review some key bench-mark testing results obtained in partner- ship with major Chinese operators. Finally, we discuss enabling technologies that are currently being researched to allow interfaces to scale to Terabit/s rates.

Micro-droplet Digital Polymerase Chain Reaction and Real-Time Quantitative Polymerase Chain Reaction Technologies Provide Highly Sensitive and Accurate Detection of Zika Virus

The establishment of highly sensitive diagnostic methods is critical in the early diagnosis and control of Zika virus(ZIKV)and in preventing serious neurological complications of ZIKV infection. In this study, we established micro-droplet digital polymerase chain reaction(ddPCR) and real-time quantitative PCR(RT-qPCR) protocols for the detection of ZIKV based on the amplification of the NS5 gene. For the ZIKV standard plasmid, the RT-qPCR results showed that the cycle threshold(Ct) value was linear from 10~1 to 10~8 copy/l L, with a standard curve R^2 of 0.999 and amplification efficiency of 92.203%;however, a concentration as low as 1 copy/l L could not be detected. In comparison with RT-qPCR, the dd PCR method resulted in a linear range of 10~1–10~4 copy/l L and was able to detect concentrations as low as 1 copy/l L. Thus, for detecting ZIKV from clinical samples, RT-qPCR is a better choice for high-concentration samples(above 10~1 copy/l L),while ddPCR has excellent accuracy and sensitivity for low-concentration samples. These results indicate that the ddPCR method should be of considerable use in the early diagnosis, laboratory study, and monitoring of ZIKV.

Design of recognition algorithm for multiclass digital display instrument based on convolution neural network

Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.