As the development of data visualization technology, it becomes more and more convenient to commit data analyzing and mining in some research areas. Data visualization paves the way to understand the abstract numbers in an intuitive manner. This paper focuses on the hyperspectral data analysis by visualization. We research the ray casting based volume data rendering method and propose the method termed Volume Rendering of Hyperspectral Data(VRHD) to visualize the hyperspectral data. The triple control elements including color, transparency and visual district are proposed to do the data analysis and solve the overlapping problem in cubic data visualization by traditional screen. The results prove that the proposed algorithm can help the data user or analyzer to grasp the character of the data such as noise distribution in an integral view.
The use of data fusion algorithms for fusion processing of the obtained multivariate measurement data is a common method to improve measurement accuracy. This paper designs a multivariate measurement data fusion method based on the principle of EMBET, which avoids the limitation of the conventional EMBET method for measuring equipment error models and effectively improves the accuracy of multivariate measurement data fusion. The effectiveness of this method is verified by measured data.
A method for enhancing the resolution of 3D imaging reconstruction by employing the polarization modulation of electro-optical crystals is proposed. This technique utilizes two polarizers oriented perpendicular to each other along with an electro-optical modulation crystal to achieve high repetition frequency and narrow pulse width gating. By varying the modulation time series of the electro-optical crystal, three-dimensional gray images of the laser at different distances are acquired, and the three-dimensional information of the target is reconstructed using the range energy recovery algorithm. This 3D imaging system can be implemented with large area detectors, independent of the an Intensified Charge-Coupled Device (ICCD) manufacturing process, resulting in improved lateral resolution. Experimental results demonstrate that when imaging a target at the distance of 20 m, the lateral resolution within the region of interest is 2560 × 2160, with a root mean square error of 3.2 cm.
This paper proposed a CS (compressed sensing) based dynamic magnetic resonance imaging (d-MRI) method, which exploits partial separability (PS) and the redundant wavelet tight frame-SIDWT (shift invariant discrete wavelet transform). Since the time domain smoothness corresponds to the frequency domain sparsity, sparsity in Fourier domain along the time dimension is widely used in d-MRI method. In redundant wavelet tight frame, SIDWT can enhance the smoothness of images in one or two dimensions, that is an image in SIDWT domain is of lower rank. We show that the Fourier coefficients along the time dimension of d-MR image in SIDWT domain are sparser than the SIDWT coefficients of d-MR image, then introduce SIDWT to enforce the sparse L1 constraint in the classical PS-L1 d-MRI method. Lastly, the proposed d-MRI model exploiting PS and SIDWT is solved by the efficient alternating direction method of multipliers (ADMM) algorithm. Experimental results verify the superior performance of the proposed method.
Among pathological images, there are usually some typical patient structures. For these typical images, template matching can be used for diagnosis, thereby the workload of doctors can be reduced. In this paper, pathological images of breast masses are studied in depth. A typical breast masses pathological template is established. Three template matching methods, correlation coefficient matching, correlation matching method as well as square difference matching method are used for experiments separately, and the timeliness and effectiveness of them are evaluated in terms of computing time and matching accuracy. The experimental results show that among these three template matching methods, the accuracy of correlation coefficient matching is the highest, and it is able to overcome the interference of angel rotation and noise. However, it is also consumes the longest time.
Communication signal reconstruction technology represents a critical area of research within communication countermeasures and signal processing. Considering traditional OFDM signal reconstruction methods' intricacy and suboptimal reconstruction performance, a dual discriminator CGAN model incorporating LSTM and Transformer is proposed. When reconstructing OFDM signals using the traditional CNN network, it becomes challenging to extract intricate temporal information. Therefore, the BiLSTM network is incorporated into the first discriminator to capture timing details of the IQ (In-phase and Quadrature-phase) sequence and constellation map information of the AP (Amplitude and Phase) sequence. Subsequently, following the addition of fixed position coding, these data are fed into the core network constructed based on the Transformer Encoder for further learning. Simultaneously, to capture the correlation between the two IQ signals, the VIT (Vision in Transformer) concept is incorporated into the second discriminator. The IQ sequence is treated as a single-channel two-dimensional image and segmented into pixel blocks containing IQ sequence through Conv2d. Fixed position coding is added and sent to the Transformer core network for learning. The generator network transforms input noise data into a dimensional space aligned with the IQ signal and embedding vector dimensions. It appends identical position encoding information to the IQ sequence before sending it to the Transformer network. The experimental results demonstrate that, under commonly utilized OFDM modulation formats such as BPSK, QPSK, and 16QAM, the time series waveform, constellation diagram, and spectral diagram exhibit high-quality reconstruction. Our algorithm achieves improved signal quality while managing complexity compared to other reconstruction methods.
In this paper, the classic template matching sequential similarity detection algorithm(SSDA) has a large amount of calculation and slow operation time, and an improved SSDA is proposed. By introducing the integral image, the point-by-point summation in the matching template window is transformed into the addition and subtraction of four positions in the integral image, which reduces the amount of calculation for point-by-point accumulation and summation. The laser image and the classic lena image are used as the experimental images to be matched, and the image with gray scale changes and noise added as the template image is used for image matching. Compared with the traditional normalized gray cross-correlation(NCC) algorithm and the improved NCC algorithm for integrating images, the experimental results show that the improved algorithm can shorten the running time, have a certain anti-interference ability against noise, and achieve image matching.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)