Compressive sampling for accelerometer signals in structural health monitoring

In structural health monitoring (SHM) of civil structures, data compression is often needed to reduce the cost of data transfer and storage, because of the large volumes of sensor data generated from the monitoring system. The traditional framework for data compression is to first sample the full signal and, then to compress it. Recently, a new data compression method named compressive sampling (CS) that can acquire the data directly in compressed form by using special sensors has been presented. In this article, the potential of CS for data compression of vibration data is investigated using simulation of the CS sensor algorithm. For reconstruction of the signal, both wavelet and Fourier orthogonal bases are examined. The acceleration data collected from the SHM system of Shandong Binzhou Yellow River Highway Bridge is used to analyze the data compression ability of CS. For comparison, both the wavelet-based and Huffman coding methods are employed to compress the data. The results show that the values of compression ratios achieved using CS are not high, because the vibration data used in SHM of civil structures are not naturally sparse in the chosen bases

Architecture as water-human mediator

Humans have developed a working relationship with water and the sea since ancient times. Sea level rise is going to challenge that relationship and change people’s lifestyle forever. To better adapt people physically and psychologically to their new life within or near the sea, it is necessary for the coastal building environment to mediate people’s relationship with the ocean, helping people get more comfortable with water. The Boston fish pier is an icon of the Boston fishing industry. Climate change and overfishing are causing the decline of this industry and making the pier more and more desolate. Hundreds of years ago the site was part of the sea until the man-made land expanded here. As sea level rises, the fish pier will be gradually submerged. Fishing and land expansion have undeniable relations with climate change and sea level rise. This site is a place where the most activity between land and sea took place, and proof that as we intervene and affect nature then we will all be affected by it now. Intentionally pushing out the fishing industry programs is a response to climate change and rethinking about the human-ocean relationship. Instead of keeping intervening with nature, enhancing the boundary, we will allow nature to reoccupy the place, embrace the ocean and try as a species to get along with our new wetter environment. The site is an ideal place to test this new relationship and the role of the built environment as a mediator during the transition period. The design will mainly respond to the rising water between now and the 2070s by transforming the pier into a public aquatic center with different types of pools, swim learning center, dive learning center and water therapy. The program shift and infrastructural change will create various possibilities for water-related experience. By creating an artificial topography integrated with the buildings, the rising water becomes more like a playful element going in and out of the buildings and gradually changed how people use the spaces. The design of the interior space focuses on the ground level and the swimming education center, using the space and water to control the sense of safety and to help people learn swimming. Eventually, the building environment will help to develop a more intimate water-human relationship

Coherence retrieval using trace regularization

The mutual intensity and its equivalent phase-space representations quantify an optical field's state of coherence and are important tools in the study of light propagation and dynamics, but they can only be estimated indirectly from measurements through a process called coherence retrieval, otherwise known as phase-space tomography. As practical considerations often rule out the availability of a complete set of measurements, coherence retrieval is usually a challenging high-dimensional ill-posed inverse problem. In this paper, we propose a trace-regularized optimization model for coherence retrieval and a provably-convergent adaptive accelerated proximal gradient algorithm for solving the resulting problem. Applying our model and algorithm to both simulated and experimental data, we demonstrate an improvement in reconstruction quality over previous models as well as an increase in convergence speed compared to existing first-order methods.Comment: 28 pages, 10 figures, accepted for publication in SIAM Journal on Imaging Science