Abstract: Frog population has been declining the past decade for habitat loss, invasive species, climate change, and so forth. Therefore, it is becoming ever more important to monitor the frog population. Recent advances in acoustic sensors make it possible to collect frog vocalizations over large spatio-temporal scale. Through the detection of frog calling activity with collected acoustic data, frog population can be predicted. In this paper we propose a novel method for detecting frog calling activity using acoustic event detection and multi-label learning. Here, frog calling activity consists of frog abundance and frog species richness, which denotes number of individual frog calls and number of frog species respectively. To be specific, each segmented recording is first transformed to a spectrogram. Then, acoustic event detection is used to calculate frog abundance. Meanwhile, those recordings without frog calls are filtered out. For frog species richness, three acoustic features, linear predictive coefficients, Mel-frequency Cepstral coefficients and wavelet-based features are calculated. Then, multi-label learning is used to predict frog species richness. Lastly, statistical analysis is used to reflect the relationship between frog calling activity (frog abundance and frog species richness) and weather variables. Experiment results show that our proposed method can accurately detect frog calling activity and reflect its relationship with weather variables.

Abstract: In the past few years massive amounts of data have been generated for genetic analysis. Existing solutions to analyze this data concerning genome-wide gene interactions are either not powerful enough or can barely be managed with standard computers due to the tremendous amount of statistical tests to be performed. Also, common approaches using cluster or cloud technologies for parallel analysis are operating at the edge of what is currently possible. This work demonstrates how FPGAs are able to address this problem. We present a highly parallel, hardware oriented solution for genome-wide association interaction studies (GWAIS) with MB-MDR and the maxT multiple testing correction on an FPGA-based architecture. We achieve a more than 300-fold speedup over an AMD Opteron cluster with 160 cores on an FPGA-system equipped with 128 Xilinx Spartan6 LX150 low-cost FPGAs when analyzing a WTCCC-like dataset with 500,000 markers and 5,000 samples. Furthermore, we are able to keep pace with a 256-core Intel Xeon cluster running MB-MDR~4.2.2 with an approximative version of maxT, while we achieve a 190-fold speedup over the sequential execution of this version on one Xeon core.

Abstract: Computation is often seen as information processing. Many biological systems may be investigated in terms of information storage, signaling, and data processing networks. Much of this data processing activity is embodied in structural transformations in spatial scales ranging from the molecular to cellular networks. The biomedical sciences make use of an increasingly powerful arsenal of tools and technologies for obtaining structural data as well as details of mass transport and the chemical and electrical signals that underlie these fundamental biological processes. For example, new staining techniques combined with computer-based electron microscope tomography, permit the clear imaging of chromatin filaments in the cell nucleus and filament networks in the cytoplasmic and extracellular space via the electron microscope. The application of tomographic reconstruction software developed at the National Center for Microscopy and Imaging Research (NCMIR) enables detailed 3D reconstructions of the relevant biological structures and processes. In order to deal with fundamental issues related to information processing in biological systems, new data processing methods as well as advances in chemically sensitive probes and imaging technology must be applied across a wide range of spatial and temporal scales. One class of increasingly useful tools for modeling biological systems, evaluating imaging technologies and characterizing the fidelity of digital processing has its roots in theoretical investigations in statistical mechanics, which arise from the concepts of information and entropy. We review how concepts of information and entropy may give new perspectives on the flow of information within biological systems, as well as our instrumentation and computer processing.

Abstract: In recent years increased the research interest in the development of different approaches to support the mobility of the visually impaired. The automatic detection of tactile paving surface is one important topic of research, not only to help the mobility of visually impaired persons, but also for use in the displacement of autonomous robots, providing a safely route and warnings. In this paper we propose an approach for tactile paving surface detection in real-time with the purpose to assist visually impaired persons. It uses computer vision algorithms combined with decision tree to eliminate some possible false alarms. We assume the visually impaired persons holds a smartphone, which is used to obtain images, as well as to assist him by audio feedback to keep it on the tactile paving surface. This problem is very challenging, mainly due to illumination changes, occlusion, image noise and resolution, as well as different possible colors of the tactile paving surfaces. Experimental results indicate that the proposed approach works well in low resolution images, effectively detecting the tactile paving surfaces in real test scenarios.

Abstract: Inspired by the social behavior of the bird flocking or fish schooling, the particle swarm optimization (PSO) is a population based stochastic optimization method developed by Eberhart and Kennedy in 1995. It has been used across a wide range of applications. Faure, Halton and Vander Corput sequences have been used for initializing the swarm in PSO. Quasirandom(or low-discrepancy) sequences such as Faure, Halton, Vander Corput etc are deterministic and suffers from correlations between radical inverse functions with different bases used for different dimensions. In this paper, we investigate the effect of initializing the swarm with scrambled optimal Halton sequence, which is a randomized quasirandom sequence. This ensures that we still have the uniformity properties of quasirandom sequences while preserving the stochastic behavior for particles in the swarm. Numerical experiments are conducted with benchmark objective functions with high dimensions to verify the convergence and effectiveness of the proposed initialization of PSO