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In indoor environments, accurate location or positioning becomes an essential requirement, driven by the need for autonomous moving devices, or to identify the position of people in large spaces. Single technology schemes which use WiFi and Bluetooth are affected by fading effects as well as by signal noise, providing inaccuracies in location estimation. Hybrid locating or positioning schemes have been used in indoor situations and scenarios in order to improve the location accuracy. Hence, this paper proposes a hybrid scheme (technique) to implement fingerprint-based indoor positioning or localization, which uses the Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points as well as Wireless Sensor Networks (WSNs) technologies. Our approach consists of performing a virtual tessellation of the indoor surface, with a set of square tiles encompassing the whole area. The model uses an Artificial Neural Network (ANN) approach for position estimate, in which related RSS is associated to a 1 m × 1 m tile. The ANN was trained to match the RSS signal strength to the corresponding tile. Experimental results indicate that the average distance error, based on tile identification accuracy, is 0.625 m from tile-to-tile, showing a remarkable improvement compared to previous approaches.

Problem statement: Segmentation is the first and fundamental step in the process of computer vision and object classification. However, complicate or similar colour pattern add complexity to the segmentation of touching objects. The objective of this study was to develop a robust technique for the automatic segmentation and classification of touching plastic bottles, whose features were previously stored in a database. Approach: Our technique was based on the possibility to separate the two objects by means of a segment of straight line, whose position was determined by a genetic approach. The initial population of the genetic algorithm was heuristically determined among a large set of cutting lines, while further generations were selected based on the likelihood of the two objects with the images stored in the database. Results: Extensive testing, which was performed on random couples out of a population of 50 bottles, showed that the correct segmentation could be achieved in success rates above 90% with only a limited number of both chromosomes and iterations, thus reducing the computing time. Conclusion: These findings proved the effectiveness of our method as far as touching plastic bottles are concerned. This technique, being absolutely general, can be extended to any situation in which the properties of single objects were previously stored in a database.

An experimental machine vision apparatus was used to identify and extract recyclable plastic bottles out of a conveyor belt. Color images were taken with a commercially available Webcam, and the recognition was performed by our homemade software, based on the shape and dimensions of object images. The software was able to manage multiple bottles in a single image and was additionally extended to cases involving touching bottles. The identification was fulfilled by comparing the set of measured features with an existing database and meanwhile integrating various recognition techniques such as minimum distance in the feature space, self-organized maps, and neural networks. The recognition system was tested on a set of 50 different bottles and provided so far an accuracy of about 97% on bottle identification. The extraction of the bottles was performed by means of a pneumatic arm, which was activated according to the plastic type; polyethylene-terephthalate (PET) bottles were left on the conveyor belt, while non-PET bottles were extracted. The software was designed to provide the best compromise between reliability and speed for real-time applications in view of the commercialization of the system at existing recycling plants.

This paper presents a genetic algorithm (GA) based segmentation technique that can separate two touching objects intended for an automatic recognition of plastic bottles moving on a conveyor belt. The proposed method is based on the possibility to separate the two objects by means of a straight line, whose position is determined by a GA. Extensive testing shows that the proposed method is fast and yields high success rate of correct segmentation with only a limited number of both chromosomes and iterations.