An Efficient Deep Learning-based Content-based Image Retrieval Framework

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology.Image retrieval has become one of the vital tools in image processing applications.Content-Based Image Retrieval(CBIR)has been widely used in varied applications.But,the results produced by the usage of a single image feature are not satisfactory.So,multiple image features are used very often for attaining better results.But,fast and effective searching for relevant images from a database becomes a challenging task.In the previous existing system,the CBIR has used the combined feature extraction technique using color auto-correlogram,Rotation-Invariant Uniform Local Binary Patterns(RULBP)and local energy.However,the existing system does not provide significant results in terms of recall and precision.Also,the computational complexity is higher for the existing CBIR systems.In order to handle the above mentioned issues,the Gray Level Co-occurrence Matrix(GLCM)with Deep Learning based Enhanced Convolution Neural Network(DLECNN)is proposed in this work.The proposed system framework includes noise reduction using histogram equalization,feature extraction using GLCM,similarity matching computation using Hierarchal and Fuzzy c-Means(HFCM)algorithm and the image retrieval using DLECNN algorithm.The histogram equalization has been used for computing the image enhancement.This enhanced image has a uniform histogram.Then,the GLCM method has been used to extract the features such as shape,texture,colour,annotations and keywords.The HFCM similarity measure is used for computing the query image vector's similarity index with every database images.For enhancing the performance of this image retrieval approach,the DLECNN algorithm is proposed to retrieve more accurate features of the image.The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy,precision,recall,f-measure and lesser complexity.From the experimental results,it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

Ultralow-noise single-photon detection based on precise temperature controlled photomultiplier with enhanced electromagnetic shielding

We demonstrate an ultralow-noise single-photon detection system based on a sensitive photomultiplier tube（PMT） with precise temperature control, which can capture fast single photons with intervals around 10 ns.By improvement of the electromagnetic shielding and introduction of the self-differencing method, the dark counts（DCs） are cut down to ~1%. We further develop an ultra-stable PMT cooling subsystem and observe that the DC goes down by a factor of 3.9 each time the temperature drops 10°C. At -20°C it is reduced 400 times with respect to the room temperature（25°C）, that is, it becomes only 2 counts per second, which is on par with the superconducting nanowire detectors. Meanwhile, despite a 50% loss, the detection efficiency is still 13%. Our detector is available for ultra-precise single-photon detection in environments with strong electromagnetic disturbances.

Retinex based low-light image enhancement using guided filtering and variational framework

A new image enhancement algorithm based on Retinex theory is proposed to solve the problem of bad visual effect of an image in low-light conditions. First, an image is converted from the RGB color space to the HSV color space to get the V channel. Next, the illuminations are respectively estimated by the guided filtering and the variational framework on the V channel and combined into a new illumination by average gradient. The new reflectance is calculated using V channel and the new illumination. Then a new V channel obtained by multiplying the new illumination and reflectance is processed with contrast limited adaptive histogram equalization(CLAHE). Finally, the new image in HSV space is converted back to RGB space to obtain the enhanced image. Experimental results show that the proposed method has better subjective quality and objective quality than existing methods.

Enhancement of the angular rotation measurement sensitivity based on SU(2)and SU(1,1) interferometers

We investigate the sensitivity of the angular rotation measurement with the method of homodyne detection in SU(2) and SU(1,1) interferometers by employing orbital angular momentum(OAM). By combining a coherent beam with a vacuum beam in an SU(2) interferometer, we get the sensitivity of the angular rotation measurement as 1/(2N^(1/2)l). We can surpass the limit of the angular rotation measurement in an SU(1,1) interferometer by combining a coherent beam with a vacuum beam or a squeezed vacuum beam when the probe beam has OAM. Without injection, the sensitivity can reach 1/(2N^(1/2)l). In addition, by employing another construction of an SU(1,1) interferometer where the pump beam has OAM, with the same injection of an SU(1,1) interferometer, the sensitivity of the angular rotation measurement can be improved by a factor of 2, reaching 1/(4Nl). The results confirm the potential of this technology for precision measurements in angular rotation measurements.

Syntheses and structures of tetranuclear Zn(Ⅱ) complexes with in situ generated macrocyclic Schiff base ligands:Applications in Zn^(2+) sensing

Three novel Zn（II） complexes,[Zn4L1Cl4]-3H2O（1）,[Zn4L2Cl4]-2DMF（2） and[Zn4L^3Cl4]H2O（3）,have been synthesized and structurally characterized.In these complexes,interesting 32-membered dodecadentate macrocyclic ligands were generated in situ by ＇2 ＋ 2＇ type condensation reactions between a tetraamine and various dialdehydes.All the complexes are isostructurally tetranuclear Zn（Ⅱ） complexes,containing endogenous alkoxo and phenoxo bridges.Applications of the macrocyclic ligands as Zn^2＋ sensors have been investigated.Take H4L^1 for example,it exhibits a 4-fold fluorescence enhancement upon the addition of 2 equiv.of Zn^2＋ in MeOH.

A method of whispered speech enhancement based on speech absence probability and modified mel-domain masking model

Whispered speech enhancement using auditory masking model in modified Mel- domain and Speech Absence Probability （SAP） was proposed. In light of the phonation char- acteristic of whisper, we modify the Mel-frequency Scaling model. Whispered speech is filtered by the proposed model. Meanwhile, the value of masking threshold for each frequency band is dynamically determined by speech absence probability. Then whispered speech enhancement is conducted by adaptively rectifying the spectrum subtraction coefficients using different masking threshold values. Results of objective and subjective tests on the enhanced whispered signal show that compared with other methods; the proposed method can enhance whispered signal with better subjective auditory quality and less distortion by reducing the music noise and background noise under the masking threshold value.

Phase-only hologram generation based on integral imaging and its enhancement in depth resolution

We introduce a phase-only hologram generation method based on an integral imaging, and propose an enhancement method in representable depth interval. The computational integral imaging reconstruction method is modified based on optical flow to obtain depth-slice images for the focused objects only. A phaseonly hologram for＇ multiple plane images is generated using the iterative Fresnel transform algorithm. In addition, a division method in hologram plane is proposed for enhancement in the representable minimum depth interval.

Noise reduction algorithm of corrosion acoustic emission signal based on short-time fractal dimension enhancement

The general corrosion and local corrosion of Q235 steel were tested by acoustic emission （AE） detecting system under 6% FeCl3.6H2O solution to effectively detect the corrosion acoustic emission signal from complex background noise. The short-time fractal dimension and discrete fractional cosine transform methods are combined to reduce noise. The input SNR is 0-15 dB while corrosion acoustic emission signals being added with white noise, color noise and pink noise respectively. The results show that the output signal-to-noise ratio is improved by up to 8 dB compared with discrete cosine transform and discrete fractional cosine transform. The above-mentioned noise reduction method is of significance for the identification of corrosion induced acoustic emission signals and the evaluation of the metal remaining life.