Private/local wireless networks based on LoRa with Internet of Things (IoT) devices has been paid much attention. Important issues of the LoRa based network are non-line-of-sight (NLOS) environment and inappropriate of LoRa gateway. This paper studies propagation characteristics for LoRa communications operated at 920MHz band in a harsh environment, which is assumed that i) numerous NLOS environment in the communication area and ii) high buildings surrounding the LoRa gateway. For the evaluation of the propagation characteristic in the harsh environment, we measure averaged received signal strength indicator (RSSI) values in a university campus. Based on the measurement results, we obtain the outage probability and the path loss model. These results indicate that the harsh environment causes the reduction of the communication area, i.e., the path loss model which is obtained from the measurement data almost follows COST-231 Walfisch- Ikegami model.

Maximum cyclic autocorrelation selection (MCAS)-based spectrum sensing is one of the low complexity spectrum sensing techniques in cyclostationary detection techniques. However, spectrum sensing features of MCAS- based spectrum sensing have never been theoretically derived. This paper provides a derivation result of spectrum sensing characteristics for MCAS-based spectrum sensing in cognitive radio networks. In this study, we derive closed form solutions for signal detection probability and false alarm probability for MCAS-based spectrum sensing. The theoretical values are compared with numerical examples, and the examples demonstrate that numerical and theoretical values match well with each other.

This paper presents a statistics computation method for computationally efficient spectrum sensing in cognitive radio. The presented technique computes the statistics for signal detection after combining all signals. As the computation of the statistics for all received antennas is not required, the presented technique enables the reduction of the computational complexity. Some results show that the spectrum sensing performances of the presented technique are almost the same as that of conventional techniques despite the complexity of the presented technique being almost half that of the conventional techniques.

Presents a new method utilizing a genetic algorithms to search for initial values for a nonlinear optimization process in multi-viewpoint stereo measurement. First, the authors take a one dimensional matrix called "individual" which consists of parameters to be determined. Next, the authors form a population which consists of several individuals. The authors perform three kinds of genetic operation on the population, that is, crossover, mutation and selection, which evolve the population to fit the environment. The authors reiterate this evolution process and use the evolved result as the initial values for the iteration calculation. The efficiency, of this method is confirmed by simulation and an outdoor experiment.>

We propose a new method for combining two overlapping maps made by the stereo vision technique utilizing view field information. Degree of matching is evaluated for every matching candidate, where object point pairs and solitary points in the overlapping view fields contribute positive and negative values, respectively. The threshold value used in judging pair is determined by considering the error magnitude, which depends on the distance from the view point. Various kinds of calculation are compared in terms of the freedom of threshold, which is defined as the ratio of the maximum and minimum threshold values, and with which the correct overlap is derived.>

An estimation method is presented for accurately determining the relationship between viewpoints and object positions simultaneously by integrating stereo data from multi-viewpoints whose relationship is unknown. With this method both the relationship and the object positions are estimated by minimizing the sum of square errors in the image coordinates. The calculation is performed with the nonlinear optimization method because the multi-viewpoint stereo measurement system is a nonlinear system. Therefore, this method can be applied to an arbitrary multi-viewpoint stereo measurement system, whereas almost all conventional methods can only be applied to linear cases. A simulation is used to investigate the influence of the relative viewpoint position parameters on the accuracy with which object positions are determined in a three viewpoint stereo measurement. The effectiveness of this method is confirmed experimentally by actual outdoor measurements.

We propose a new approach for mcasuring 30 positions of target objects using stereo vision system. In stereo vision metrology for natural scenes , automatic recognition of targets and detcrminination of corresponding points arc strongly desired , which technique as of today is so poor that these processes are relied on human asislance. Automatic recognition and stereo correspondensc of telephone poles in a pair of natural scenes is realized by using an algorithm considering specifications of the targets, such as diameter, height, installation condition, through four steps, that is, [ I] Search region resDiction, [ O] Recognition, [U] Correspondence and [IV] Examination.

A simple and useful calibration method for a TV camera with a high-distortion lens is presented. The parameters to be calibrated are effective focal length, one-pixel width on an image plane, image distortion center, and distortion coefficient. A simple-pattern calibration chart composed of parallel straight lines is introduced as a reference for calibration. An ordinary 2D model fitting is decomposed into two 1D model fittings on the column and row of a frame buffer across the image distortion center by ingeniously utilizing the point symmetry characteristic of image distortion. Some parameters with a calibration chart are eliminated by setting up a calibration chart precisely and by utilizing negligibly low distortion near the image distortion center. Thus, the number of unknown parameters to be calibrated is drastically decreased, enabling simple and useful calibration. The effectiveness of the proposed calibration method is confirmed by experimentation.>

Noise that replaces, rather than perturbs, a signal is not amenable to the usual approaches of noise removal. Here, multiple images of a stationary scene are used to reduce random obscuration or dropout noise by the following method. For each image coordinate, a gray-level initial histogram is computed over all the images. The initial histograms are averaged for all image coordinates, and for each image coordinate a gray-level histogram corresponding to the object is derived by subtracting the averaged histogram from the initial one. The gray level of the object is obtained from the resulting object histogram. The effectiveness of this method is confirmed through experiments using a scene obscured by air bubbles in a water tank.>