Aperture synthesis technology represents an effective approach to millimeter-wave radiometers for high-resolution observations. However, the application of synthetic aperture imaging radiometer (SAIR) is limited by its large number of antennas, receivers and correlators, which may increase noise and cause the image distortion. To solve those problems, this letter proposes a compressive regularization imaging algorithm, called CRIA, to reconstruct images accurately via combining the sparsity and the energy functional of target space. With randomly selected visibility samples, CRIA employs l1 norm to reconstruct the target brightness temperature and l2 norm to estimate the energy functional of it simultaneously. Comparisons with other algorithms show that CRIA provides higher quality target brightness temperature images at a lower data level.

We propose a video magnification method for magnifying subtle color and motion changes under the presence of non-meaningful background motions. We use frequency variability to design a filter that passes only meaningful subtle changes and removes non-meaningful ones; our method obtains more impressive magnification results without artifacts than compared methods.

The quality of edge detection is related to detection angle, scale, and threshold. There have been many algorithms to promote edge detection quality by some rules about detection angles. However these algorithm did not form rules to detect edges at an arbitrary angle, therefore they just used different number of angles and did not indicate optimized number of angles. In this paper, a novel edge detection algorithm is proposed to detect edges at arbitrary angles and optimized number of angles in the algorithm is introduced. The algorithm combines singularity detection with Gaussian wavelet transform and edge detection at arbitrary directions and contain five steps: 1) An image is divided into some pixel lines at certain angle in the range from 45° to 90° according to decomposition rules of this paper. 2) Singularities of pixel lines are detected and form an edge image at the certain angle. 3) Many edge images at different angles form a final edge images. 4) Detection angles in the range from 45° to 90° are extended to range from 0° to 360°. 5) Optimized number of angles for the algorithm is proposed. Then the algorithm with optimized number of angles shows better performances.

Object detection in millimeter-wave Interferometric Synthetic Aperture Radiometer (InSAR) imaging is always a crucial task. Facing unpredictable and numerous objects, traditional object detection models running after the InSAR system accomplishing imaging suffer from disadvantages such as complex clutter backgrounds, weak intensity of objects, Gibbs ringing, which makes a general purpose saliency detection system for InSAR necessary. This letter proposes a spectrum-based saliency detection algorithm to extract the salient regions from unknown backgrounds cooperating with sparse sensing InSAR imaging procedure. Directly using the interferometric value and sparse information of scenes in the basis of the Discrete Cosine Transform (DCT) domain adopted by InSAR imaging procedure, the proposed algorithm isolates the support of saliency region and then inversely transforms it back to calculate the saliency map. Comparing with other detecting algorithms which run after accomplishing imaging, the proposed algorithm will not be affected by information-loss accused by imaging procedure. Experimental results prove that it is effective and adaptable for millimeter-wave InSAR imaging.

The consistent alignment of point clouds obtained from multiple scanning positions is a crucial step for many 3D modeling systems. This is especially true for environment modeling. In order to observe the full scene, a common approach is to rotate the scanning device around a rotation axis using a turntable. The final alignment of each frame data can be computed from the position and orientation of the rotation axis. However, in practice, the precise mounting of scanning devices is impossible. It is hard to locate the vertical support of the turntable and rotation axis on a common line, particularly for lower cost consumer hardware. Therefore the calibration of the rotation axis of the turntable is an important step for the 3D reconstruction. In this paper we propose a novel calibration method for the rotation axis of the turntable. With the proposed rotation axis calibration method, multiple 3D profiles of the target scene can be aligned precisely. In the experiments, three different evaluation approaches are used to evaluate the calibration accuracy of the rotation axis. The experimental results show that the proposed rotation axis calibration method can achieve a high accuracy.

TV white space (TVWS) brings potential opportunities to relieve the growing spectrum scarcity. Therefore organizations like the FCC have suggested the co-channel deployment of cellular networks (CNs) on condition that a keep-out distance from the protected region of TV receivers is maintained. However the consequent CN interference has not been described. In addition, considering the wide range of TV coverage, it is also inefficient and wasteful not applying the vacant spectra for secondary user (SU) communication by opportunistic access inside the TV coverage zone. In this paper, we first investigate the aggregate interference from CNs outside the protected area to find out how the interference is generated, and then research the available spectrum resource distribution for SUs inside the TV coverage zone under aggregate interference constraints to utilize TVWS more efficiently. Specifically, we model CN in three aspects. A close-form interference probability distribution function (PDF) is proposed. Since the PDF is too complex to analyze, we approximate it as Gaussian and prove the accuracy of our approximation with Kolmogorov-Smirnov test. Then, available spectra maximization is formulated as an optimization problem under both TV and SU receiver outage probability constraints. We find that available spectra demonstrate a volcano-shaped geographical distribution and optimal network-status-aware SU transmit power exists to maximize the spectra. Our analysis reveals the characteristics of interference in TVWS and contributes to the utilization improvement of white space.

Broadband wireless channels are frequency selective in nature. In this paper, a novel precoder with finite impulse response (FIR) structure is proposed to maximize the throughput of the multiple-input multiple-output (MIMO) frequency-selective multicast channel. An iteration mechanism is investigated to obtain the desired FIR precoding matrix. In the iterative process, two associated parameters, namely the innovation orientation and the iteration step size, are jointly derived by the convex optimization program and the traditional Gauss-Newton algorithm. Convergence and complexity analyses are presented, and the numerical simulations indicate that the proposed method outperforms the existing schemes in the moderate to high signal to noise ratio (SNR) regime.