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We construct an enhanced version of knot contact homology, and show that we can deduce from it the group ring of the knot group together with the peripheral subgroup. In particular, it completely determines a knot up to smooth isotopy. The enhancement consists of the (fully noncommutative) Legendrian contact homology associated to the union of the conormal torus of the knot and a disjoint cotangent fiber sphere, along with a product on a filtered part of this homology. As a corollary, we obtain a new, holomorphiccurve proof of a result of the third author that the Legendrian isotopy class of the conormal torus is a complete knot invariant.

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As the barcode becomes more widely used, its applications and data capacity demands grow, increasing the need for barcodes with greater data density. Utilizing the quick response (QR) code–one of the many types of barcodes–we developed two algorithms. The first algorithm creates a color QR code that stores more information than a standard QR code and embeds extra data with limited access privilege. The second algorithm denoises a noisy color QR code. These algorithms consist of three techniques: (1) enlarging the data capacity of a compact QR code image by stacking multiple classical QR codes to form a color barcode, (2) embedding information into the color QR code using pseudo quantum signals in an M-band wavelet domain and selecting the discrete 4-band wavelet transforms to compress the QR images, and (3) applying Discrete M-band Wavelet Transform (DMWT) and Patch Group Prior based Denoising (PGPD) methods to denoise noisy QR code images. The peak-signal-to-noise-ratio (PSNR) summary indicates that information in a color QR code can be efficiently stored and retrieved with these methods. Moreover, it shows that our denoising algorithm effectively removes heavy noise from the noisy color QR code. Our algorithms are implemented in a flexible framework, which allows for further modifications to improve both the data capacity of a color QR code and the effectiveness of signal extraction from noisy data to meet future demands.

Riemannian geometry is characterized, and research is oriented towards and shaped by concepts (geodesics, connections, curvature,...) and objectives, in particular to understand certain classes of (compact) Riemannian manifolds defined by curvature conditions (constant or positive or negative curvature,...). By way of contrast, geometric analysis is a perhaps somewhat less systematic collection of techniques, for solving extremal problems naturally arising in geometry and for investigating and characterizing their solutions. It turns out that the two fields complement each other very well; geometric analysis offers tools for solving difficult problems in geometry, and Riemannian geometry stimulates progress in geometric analysis by setting ambitious goals.