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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.

Waste collection widely depends on the route optimization problem that involves a large amount of expenditure in terms of capital, labor, and variable operational costs. Thus, the more waste collection route is optimized, the more reduction in different costs and environmental effect will be. This study proposes a modified particle swarm optimization (PSO) algorithm in a capacitated vehicle-routing problem (CVRP) model to determine the best waste collection and route optimization solutions. In this study, threshold waste level (TWL) and scheduling concepts are applied in the PSO-based CVRP model under different datasets. The obtained results from different datasets show that the proposed algorithmic CVRP model provides the best waste collection and route optimization in terms of travel distance, total waste, waste collection efficiency, and tightness at 70-75% of TWL. The obtained results for 1 week scheduling show that 70% of TWL performs better than all node consideration in terms of collected waste, distance, tightness, efficiency, fuel consumption, and cost. The proposed optimized model can serve as a valuable tool for waste collection and route optimization toward reducing socioeconomic and environmental impacts.

Waste collection is an important part of waste management that involves different issues, including environmental, economic, and social, among others. Waste collection optimization can reduce the waste collection budget and environmental emissions by reducing the collection route distance. This paper presents a modified Backtracking Search Algorithm (BSA) in capacitated vehicle routing problem (CVRP) models with the smart bin concept to find the best optimized waste collection route solutions. The objective function minimizes the sum of the waste collection route distances. The study introduces the concept of the threshold waste level (TWL) of waste bins to reduce the number of bins to be emptied by finding an optimal range, thus minimizing the distance. A scheduling model is also introduced to compare the feasibility of the proposed model with that of the conventional collection system in terms of travel distance, collected waste, fuel consumption, fuel cost, efficiency and CO2 emission. The optimal TWL was found to be between 70% and 75% of the fill level of waste collection nodes and had the maximum tightness value for different problem cases. The obtained results for four days show a 36.80% distance reduction for 91.40% of the total waste collection, which eventually increases the average waste collection efficiency by 36.78% and reduces the fuel consumption, fuel cost and CO2 emission by 50%, 47.77% and 44.68%, respectively. Thus, the proposed optimization model can be considered a viable tool for optimizing waste collection routes to reduce economic costs and environmental impacts.

DNA computer based algorithm is developed and evaluated for recyclable waste paper segregation.The concepts of replication and massive parallelism operations are used.The matching stage consists of Copy, Extract, Detect and Merge or Union operations.Gel electrophoresis operation is used to identify the candidate paper object grade.Success rate are 92% for WP, 90% for ONP and 93% for OCC with template size 5i?5. This article explores the application of DNA computing in recyclable waste paper sorting. The primary challenge in paper recycling is to obtain raw materials with the highest purity. In recycling, waste papers are segregated according to their various grades, and these are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products, while saving on processing chemicals and energy. In the industry, different sensors are used in paper sorting systems, namely, ultrasonic, lignin, gloss, stiffness, infra-red, mid-infra red, and color sensors. Different mechanical and optical paper sorting systems have been developed based on the different sensors. However, due to inadequate throughput and some major drawbacks related to mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. The automated paper sorting systems offer significant advantages over the manual systems in terms of human fatigue, throughput, speed, and accuracy. This research has two objectives: (1) to use a web camera as an image sensor for the vision system in lieu of different sensors; and (2) to develop a new DNA computing algorithm based on the theme of template matching techniques for segregating recyclable waste papers according to paper grades. Using the concepts of replication and massive parallelism operations, the DNA computing algorithm can efficiently reduce the computational time of the template matching method. This is the main strength of the DNA computing algorithm in actual inspections. The algorithm is implemented by using a silicon-based computer to verify the success rate in paper grade identification.

Solid waste management is a big challenge throughout the world. It is becoming more complicated with the increased globalization and worldwide urbanization. Solid waste monitoring and management depends on the solid waste generation and collection efficiency. Without an efficient collection system, improvement of the solid waste monitoring and management system is a difficult task. Thus, it is a big hindrance on the way of development of solid waste management and sustainable environment. This paper deals with a comprehensive survey on the solid waste generation and their collection efficiencies. From the rigorous review, it is observed that the existing methods and system, however, not reliable and intelligent enough to monitor and manage the increased level of solid waste. Accordingly, this review is being highlighted on the issues and challenges for future efficient solid collection systems. This review paper may guide the reader about the basics of solid waste generation and their collection process to facilitate the planning and design of a sustainable new system

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.

Automated sorting for plastic recyclables has been seen as the way forward in the plastic recycling industry. Automated sorting provides significant improvements in terms of efficiency and consistency in the sorting process. In the case of macro sorting, which is the most common type of automated sorting, efficiency is determined by the mechanical details of the material handling system as well as the detection system. This paper provides a review on the state of-the-art technologies that have been deployed by some of the recycling facilities abroad. The design and development of a cost effective prototype automated system for sorting plastic recyclables is proposed and discussed.

The fast development of computing and communication has reformed the financial markets' dynamics. Nowadays many people are investing and trading stocks through online channels and having access to real-time market information efficiently. There are more opportunities to lose or make money with all the stocks information available throughout the World; however, one should spend a lot of effort and time to follow those stocks and the available instant information. This paper presents a preliminary regarding a multi-agent recommender system for computational investing. This system utilizes a hybrid filtering technique to adaptively recommend the most profitable stocks at the right time according to investor's personal favour. The hybrid technique includes collaborative and content-based filtering. The content-based model uses investor preferences, influencing macro-economic factors, stocks profiles and the predicted trend to tailor to its advices. The collaborative filter assesses the investor pairs' investing behaviours and actions that are proficient in economic market to recommend the similar ones to the target investor.

This paper explores the application of image processing techniques in recyclable waste paper sorting. In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams will facilitate high quality end products, and save processing chemicals and energy. Since 1932 to 2009, different mechanical and optical paper sorting methods have been developed to fill the demand of paper sorting. Still, in many countries including Malaysia, waste papers are sorted into different grades using manual sorting system. Due to inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems is increased. Automated paper sorting systems offer significant advantages over human inspection in terms of fatigue, throughput, speed, and accuracy. This research attempts to develop a smart vision sensing system that able to separate the different grades of paper using Template Matching. For constructing template database, the RGB components of the pixel values are used to construct RGBString for template images. Finally, paper object grade is identified based on the maximum occurrence of a specific template image in the search image. The outcomes from the experiment in classification for White Paper, Old Newsprint Paper and Old Corrugated Cardboard are 96%, 92% and 96%, respectively. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using simple image processing techniques.

An Automated Road Sign Recognition system using Artificial Neural Network for the Textual Information box inscribing in Bengali is presented in this paper. The system captures real time images every two seconds and saves them as JPG format files. The system processes the images to find out whether they contain images of road signs or not. The textual information of the road signs is detected and extracted from the images. The Bengali OCR system takes the textual information as an input to recognize individual Bengali characters. The Bengali OCR is implemented using Multi layer Perceptron. The output of the Bengali OCR system is compared with the previously enrolled standard Bengali textual road signs. The throughput which comes from the matching process is used as input for the speech synthesizer and finally the system delivers the audio stream to the driver, either in Bengali or in English based on the user settings. After testing this system, the obtained accuracy rate was evaluated at 91.48%.

There are four forms for most of the Arabic characters- initial, medial, final and isolated. This paper presents the design of a complete display unit for Arabic alphanumeric characters using uniform segments. The word ‘complete’ signifies that the proposed display unit can represent all forms of entire Arabic alphabets as well as Arabic numerals. The word ‘uniform’ is used because of avoiding curved segments. The first segmented display unit for Arabic characters in the world covered only the isolated form and did not follow any base line to display a sequence of characters. This work extends the capability to represent all the forms of Arabic characters and follows a base line to represent a sequence of characters. Here a grid structure of 25 non-overlapping segments has been discovered. There can be 109 alphanumeric characters (at least 99 characters such as isolated form- 30, final form-29, medial form- 21, initial form- 20; and 10 numerals) to be displayed and 7-bit input is used to represent each character. After analyzing appropriate segments to be activated for every character, respective logic functions of all segments have been derived to display the entire set of Arabic alphanumeric characters.

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.