In this study, boron-doped poly(vinyl) alcohol/HfO₂ nanofibers were prepared by electrospinning using PVA as a precursor. The effect of boron doping was investigated in terms of solution properties, morphological changes and thermal characteristics. Nanofiber mats were calcined first at 800°C. Then, the calcined products were pelleted and sintered at a rate of 8°C/min and remained 2 h at 850°C at atmospheric conditions to produce ultrafine ceramic nanocrystalline powders. The fibers and nanocrystalline powders were characterized by FT-IR, XPS, XRD and SEM. The addition of boron did not only increase the thermal stability of the fibers, but also their diameters, which yielded stronger fibers. The addition of boron did not only increase the thermal stability of the fibers, but also their diameters, which yielded stronger fibers.

Brain computer interface (BCI) allows people to communicate with machines without the use of muscle systems. Although there are various kind of techniques to understand intend of the BCI user, electroencephalography (EEG) is the most popular, practical and widely implemented one. The performance of the EEG based BCI highly depends on extracting effective features. However, there is no a general feature extraction method which provides satisfied performance for all various kind of BCI applications. Therefore, it is very valuable to develop a new feature extraction method. In this paper, we proposed a novel Fast Walsh Hadamard Transform based feature extraction method for classification of EEG signals recorded during right/left hand movement imagery. It does not only provide well-discriminative attributes but also the computational time of extracting the features from a single EEG trial is fast. The proposed method was successfully applied to Data Set III of BCI competition 2003, and achieved a classification accuracy of 88.87% on the test data. The obtained satisfactory results proved that this method can be a successful alternative to the existing feature extraction methods.

Three-dimensional (3D) spatial information of object points is a vital requirement for many disciplines. Laser scanning technology and techniques based on image matching have been used extensively to produce 3D dense point clouds. These data are used frequently in various applications, such as the generation of digital surface model (DSM)/digital terrain model (DTM), extracting objects (e.g., buildings, trees, and roads), 3D modelling, and detecting changes. The aim of this study was to extract the building roof points automatically from the 3D point cloud data created via the image matching techniques with optical aerial images (with red, green, and blue band (RGB) and infrared (IR)). In the first stage of the study, as an alternative to laser scanning technology, which is more expensive than optical imaging systems, the 3D point clouds were produced by matching high-resolution images using a Semi Global Matching algorithm. The normalized difference vegetation index (NDVI) values for each point were calculated using the spectral information (RGB + IR) in the 3D point cloud data, and the points that represented the vegetation cover were determined using these values. In the second stage, existing ground and non-ground points that were free of vegetation cover were determined within the point cloud. Subsequently, only the points on the roof of the building were detected automatically using the proposed algorithm. Thus, points of the roofs of buildings located in areas with different topographic characteristics were detected automatically detected using only images. It was determined that the average values of correctness (Corr), completeness (Comp), and quality (Q) of the pixel-based accuracy analysis metrics were 95%, 98%, and 93%, respectively, in the selected test areas. According to the results of the accuracy analysis, it is clear that the proposed algorithm is very successful in automatic extraction of building roof points.