Turbo NOC: a framework for the design of Network On Chip based turbo decoder architectures

This work proposes a general framework for the design and simulation of network on chip based turbo decoder architectures. Several parameters in the design space are investigated, namely the network topology, the parallelism degree, the rate at which messages are sent by processing nodes over the network and the routing strategy. The main results of this analysis are: i) the most suited topologies to achieve high throughput with a limited complexity overhead are generalized de-Bruijn and generalized Kautz topologies; ii) depending on the throughput requirements different parallelism degrees, message injection rates and routing algorithms can be used to minimize the network area overhead.Comment: submitted to IEEE Trans. on Circuits and Systems I (submission date 27 may 2009

Image-based Detection of Surface Defects in Concrete during Construction

Defects increase the cost and duration of construction projects. Automating defect detection would reduce documentation efforts that are necessary to decrease the risk of defects delaying construction projects. Since concrete is a widely used construction material, this work focuses on detecting honeycombs, a substantial defect in concrete structures that may even affect structural integrity. First, images were compared that were either scraped from the web or obtained from actual practice. The results demonstrate that web images represent just a selection of honeycombs and do not capture the complete variance. Second, Mask R-CNN and EfficientNet-B0 were trained for honeycomb detection to evaluate instance segmentation and patch-based classification, respectively achieving 47.7% precision and 34.2% recall as well as 68.5% precision and 55.7% recall. Although the performance of those models is not sufficient for completely automated defect detection, the models could be used for active learning integrated into defect documentation systems. In conclusion, CNNs can assist detecting honeycombs in concrete

Block product density matrix embedding theory for strongly correlated spin systems

Density matrix embedding theory (DMET) is a relatively new technique for the calculation of strongly correlated systems. Recently, block product DMET (BPDMET) was introduced for the study of spin systems such as the antiferromagnetic $J_1 - J_2$ model on the square lattice. In this paper, we extend the variational Ansatz of BPDMET using spin-state optimization, yielding improved results. We apply the same techniques to the Kitaev-Heisenberg model on the honeycomb lattice, comparing the results when using several types of clusters. Energy profiles and correlation functions are investigated. A diagonalization in the tangent space of the variational approach yields information on the excited states and the corresponding spectral functions.Comment: 12 pages, 12 figure