Guaranteed Change Point Detection of Linear Autoregressive Processes with Unknown Noise Variance

SECTION 2 STATISTICAL ANALYSIS OF TIME SERIES AND SPATIAL DAT

Classification of Motion Regions with Convolutional Networks, Support Vector Machines, and Random Forests in Video-Based Analysis of Bee Traffic

Bee traffic is the number of bees moving in a given area in front of a specific hive over a given period of time. Video-based bee traffic analysis has the potential to automate the assessment of bee traffic levels, which, in turn, may lead to the automation of honeybee colony health assessment. In this paper, we evaluate several convolutional networks to classify regions of detected motion as BEE or NO-BEE in videos captured by BeePi, an electronic beehive monitoring system. We compare the performance of several convolutional neural networks with the performance of support vector machines and random forests on the same image datase

Classification of Motion Regions with Convolutional Networks, Support Vector Machines, and Random Forests in Video-Based Analysis of Bee Traffic

Bee traffic is the number of bees moving in a given area in front of a specific hive over a given period of time. Video-based bee traffic analysis has the potential to automate the assessment of bee traffic levels, which, in turn, may lead to the automation of honeybee colony health assessment. In this paper, we evaluate several convolutional networks to classify regions of detected motion as BEE or NO-BEE in videos captured by BeePi, an electronic beehive monitoring system. We compare the performance of several convolutional neural networks with the performance of support vector machines and random forests on the same image datase

Spherical nanocomposite particles prepared from mixed cellulose–chitosan solutions

Novel cellulose–chitosan nanocomposite particles with spherical shape were successfully prepared via mixing of aqueous biopolymer solutions in three different ways. Macroparticles with diameters in the millimeter range were produced by dripping cellulose dissolved in cold LiOH/urea into acidic chitosan solutions, inducing instant co-regeneration of the biopolymers. Two types of microspheres, chemically crosslinked and non-crosslinked, were prepared by first mixing cellulose and chitosan solutions obtained from freeze thawing in LiOH/KOH/urea. Thereafter epichlorohydrin was applied as crosslinking agent for one of the samples, followed by water-in-oil (W/O) emulsification, heat induced sol–gel transition, solvent exchange, washing and freeze-drying. Characterization by X-ray photoelectron spectroscopy, total elemental analysis, and Fourier transform infrared spectroscopy confirmed the prepared particles as being true cellulose–chitosan nanocomposites with different distribution of chitosan from the surface to the core of the particles depending on the preparation method. Field emission scanning electron microscopy and laser diffraction was performed to study the morphology and size distribution of the prepared particles. The morphology was found to vary due to different preparation routes, revealing a core shell structure for macroparticles prepared by dripping, and homogenous nanoporous structure for the microspheres. The non-crosslinked microparticles exhibited a somewhat denser structure than the crosslinked ones, which indicated that crosslinking restricts packing of the chains before and under regeneration. From the obtained volume-weighted size distributions it was found that the crosslinked microspheres had the highest median diameter. The results demonstrate that not only the mixing ratio and distribution of the two biopolymers, but also the morphology and nanocomposite particle diameters are tunable by choosing between the different routes of preparation.First Online: 05 August 2016</p