On the dynamics and control of a new fractional difference chaotic map

In this paper, we propose and study a fractional Caputo-difference map based on the 2D generalized Hénon map. By means of numerical methods, we use phase plots and bifurcation diagrams to investigate the rich dynamics of the proposed map. A 1D synchronization controller is proposed similar to that of Pecora and Carrol, whereby we assume knowledge of one of the two states at the slave and replicate the second state. The stability theory of fractional discrete systems is used to guarantee the asymptotic convergence of the proposed controller and numerical simulations are employed to confirm the findings.

Unsupervised defect detection algorithm for printed fabrics using content-based image retrieval techniques

Automatic detection of printing defect technology is significant for improving printing fabrics’ appearance and quality. In this research, we proposed an unsupervised printing defect detection method by processing the difference map between the test image and the reference image. Aimed at this, we adopted a content-based image retrieval (CBIR) method to retrieve the reference image, which includes an image database, a convolutional denoising auto-encoder (CDAE) and a hash encoder (HE): the elements of image database are extracted from only one defect-free sample image of the test fabric; the CDAE prevents the system being affected by the texture of the fabric and provides a reliable feature description of the patterns; the HE indexes the feature vectors to binary code while maintaining their similarity; both CDAE and HE are trained in an unsupervised manner. With the retrieved reference image, the defect is determined by applying the Tsallis entropy thresholding and opening operation on the difference map. The method can be implemented without labeled and defective samples, and without consideration of the periodical primitive of patterns. Experimental results demonstrate the effectiveness and efficiency of the proposed method in defect detection for printed fabrics with complex patterns.

Detection of parking slots occupation by temporal difference of inverse perspective mapping from vehicle-borne monocular camera

Detection of parking slots occupation is a crucial task for parking assistance, automatic parking, and autonomous driving systems. This paper proposed a novel method, called Temporal Difference of Inverse Perspective Mapping Difference (TD-IPM), without explicit 3D reconstruction or objection detection. In this method, temporal images from monocular camera are first inverse perspective mapped (IPM) onto the ground plane based on camera calibration results. Second, we proposed an algorithm, called Block Consensus based on Rotation Invariance Phase-Only Correlation (BC-RIPOC), for fast and robust motion estimation. From the estimated motion, we can align these two IPM images and generate IPM difference map. Third, the IPM difference map is segmented and filtered to generate a binary map that can distinguish objects on the ground plane or not for occupation detection. The obstacle is readily localized from the difference map as well. The proposed TD-IPM method has been validated in both underground and outdoor parking lots. Experimental results demonstrate that the proposed TD-IPM method can successfully detect various occupation objects, such as vehicles, cones, lockers, and others, with 97.9% average detection accuracy and speed of 17.5 frames per second (fps). The proposed method suggests an effective and low-cost solution to intelligent parking systems.

Experimental phasing of MicroED data using radiation damage

We present an example of an experimentally phased structure using only MicroED. The structure of a seven-residue peptide is determined starting from differences to the diffraction intensities induced by structural changes due to radiation damage. The same wedge of reciprocal space was recorded twice by continuous rotation MicroED from a set of 11 individual crystals. The data from the first pass were merged to make a “low-dose dataset”. The data from the second pass were similarly merged to form a “damaged dataset.” Differences between these two datasets were used to calculate a Patterson difference map and to identify a single heavy atom site, from which initial phases were generated. The structure was then completed by iterative cycles of modeling and refinement.