Abdulsalam Yassine

An RFID-Based Position and Orientation Measurement System for Mobile Objects in Intelligent Environments

Ambient intelligence (AmI) considers responsive environments in which applications and services adapt their behavior according to the users needs and changing context. One of the most challenging aspects for many applications in AmI environments is location and orientation of the surrounding objects. This is especially important for effective cooperation among mobile physical objects in such smart environments. In this paper, we propose a robust indoor positioning system that provides 2-D positioning and orientation information for mobile objects. The system utilizes low-range passive radio frequency identification (RFID) technology. The proposed system, which consists of RFID carpets and several peripherals for sensor data interpretation, is implemented and tested through extensive experiments. Our results show that the proposed system outperforms similar existing systems in minimizing the average positioning error.

Equipment Location in Hospitals Using RFID-Based Positioning System

Throughout various complex processes within hospitals, context-aware services and applications can help to improve the quality of care and reduce costs. For example, sensors and radio frequency identification (RFID) technologies for e-health have been deployed to improve the flow of material, equipment, personal, and patient. Bed tracking, patient monitoring, real-time logistic analysis, and critical equipment tracking are famous applications of real-time location systems (RTLS) in hospitals. In fact, existing case studies show that RTLS can improve service quality and safety, and optimize emergency management and time critical processes. In this paper, we propose a robust system for position and orientation determination of equipment. Our system utilizes passive (RFID) technology mounted on flooring plates and several peripherals for sensor data interpretation. The system is implemented and tested through extensive experiments. The results show that our systems average positioning and orientation measurement outperforms existing systems in terms of accuracy. The details of the system as well as the experimental results are presented in this paper.

A novel multi-agent system utilizing quantum-inspired evolution for demand side management in the future smart grid

The Smart Grid has become the future choice by many utility departments to attain bottom line goals of energy management. The Smart Grid will depend on a large number of renewable energy resources which require sophisticated control and coordination mechanisms for efficient and reliable demand side management DSM. In this paper, we propose a multi-agent based system to control and coordinate the operation among different entities within the Smart Grid. Specifically, the agents autonomously coordinate their activities to satisfy the local constraints of different entities while at the same time satisfying the underlying global goal of energy management. The novelty of our system is in formulating the coordination problem among the agents as a multi-objective optimization problem solved by a quantum-inspired evolution algorithm. We have extensively evaluated the system using the JIAC-V multi-agent platform. Experimental results show that our system is feasible and effective. Our method of coordinating the energy consumption of consumers using controller agents provides the basis for energy management at peak times. Thus, it promotes the wide application of the proposed system.

Cloud-based SVM for food categorization

As people across the globe are becoming more interested in watching their weight, eating more healthily, and avoiding obesity, a system that can measure calories and nutrition in everyday meals can be very useful. Recently, due to ubiquity of mobile devices such as smart phones, the health monitoring applications are accessible by the patients practically all the time. We have created a semi-automatic food calorie and nutrition measurement system via mobile that can help patients and dietitians to measure and manage daily food intake. While segmentation and recognition are the two main steps of a food calorie measurement system, in this paper we have focused on the recognition part and mainly the training phase of the classification algorithm. This paper presents a cloud-based Support Vector Machine (SVM) method for classifying objects in cluster. We propose a method for food recognition application that is referred to as the Cloud SVM training mechanism in a cloud computing environment with Map Reduce technique for distributed machine learning. The results show that by using cloud computing system in classification phase and updating the database periodically, the accuracy of the recognition step has increased in single food portion, non-mixed and mixed plate of food compared to LIBSVM.

Using Graph Cut Segmentation for Food Calorie Measurement

Calorie measurement systems that run on smart phones allow the user to take a picture of the food and measure the number of calories automatically. In order to identify the food accurately in such systems, image segmentation, which partitions an image into different regions, plays an important role. In this paper, we present the implementation of Graph cut segmentation as a means of improving the accuracy of our food classification and recognition system. Graph cut based method is well-known to be efficient, robust, and capable of finding the best contour of objects in an image, suggesting it to be a good method for separating food portions in a food image for calorie measurement. In this paper, we provide the analysis of the Graph cut algorithm as applied to food recognition. We also perform a number of experiments where we used results from the segmentation phase to the Support Vector Machine (SVM) classification model. The results show an improvement in the accuracy of food recognition, especially mixed food where accuracy increases by 15% compared to our previous work [10].

Software defined network traffic measurement: Current trends and challenges

Next generation networks such as Software Defined Networks (SDN) must support the integration of new paradigms of service offerings such as virtual cloud computing, big data applications, data centers services, and rich multimedia content. Operators of next generation SDNs are responsible for configuring policies that employ traffic monitoring tools and measurement mechanisms to detect and react to a wide range of network events and applications. In this article, we take a look at traffic measurement methods in SDNs, cover their strengths and weaknesses, point to open issues, and remaining future challenges.

Knowledge-empowered agent information system for privacy payoff in eCommerce

Today, many online companies are gathering information and assembling sophisticated databases that know a great deal of information about many people, generally without the knowledge of those people. Such endeavor has resulted in the unprecedented attrition of individual’s right to informational self-determination. On the one hand, Consumers are powerless to prevent the unauthorized dissemination of their personal information, and on the other, they are excluded from its profitable commercial exchange. This paper focuses on developing knowledge-empowered agent information system for privacy payoff as a means of rewarding consumers for sharing their personal information with online businesses. The design of this system is driven by the following argument: if consumers’ personal information is a valuable asset, should they not be entitled to benefit from their asset as well? The proposed information system is a multi-agent system where several agents employ various knowledge and requirements for personal information valuation and interaction capabilities that most users cannot do on their own. The agents in the information system bear the responsibility of working on behalf of consumers to categorize their personal data objects, report to consumers on online businesses’ trust and reputation, determine the value of their compensation using risk-based financial models, and finally negotiate for a payoff value in return for the dissemination of users’ information. The details of the system as well as a proof-of-concept implementation using JADE (Java Agent Development Environment) are presented here.

Mobile cloud based food calorie measurement

Mobile-based applications have become ubiquitous in many aspects of peoples lives over the past few years. Harnessing the potential of this trend for healthcare purposes has become a focal point for researchers and industry, in particular designing applications that can be used by patients as part of their wellness, prevention, or treatment process. Along the way, mobile cloud computing (MCC) has been introduced to be a potential paradigm for mobile health services to overcome the interoperability issues across different information formats. In this paper, we propose a mobile cloud-based food calorie measurement system. Our system provides users with convenient and intelligent mechanisms that allow them to track their food intake and monitor their calorie count. The food recognition technique in our system uses cloud Support Vector Machine (SVM) training mechanism in a cloud computing environment with Map Reduce technique for distributed machine learning. The details of the system and its implementation results are recorded in this paper.

An intelligent cloud-based data processing broker for mobile e-health multimedia applications

Abstract Mobile e-health applications provide users and healthcare practitioners with an insightful way to check users/patients’ status and monitor their daily calorie intake. Mobile e-health applications provide users and healthcare practitioners with an insightful way to check users/patients’ status and monitor their daily activities. This paper proposes a cloud-based mobile e-health calorie system that can classify food objects in the plate and further compute the overall calorie of each food object with high accuracy. The novelty in our system is that we are not only offloading heavy computational functions of the system to the cloud, but also employing an intelligent cloud-broker mechanism to strategically and efficiently utilize cloud instances to provide accurate and improved time response results. The broker system uses a dynamic cloud allocation mechanism that takes decisions on allocating and de-allocating cloud instances in real-time for ensuring the average response time stays within a predefined threshold. In this paper, we further demonstrate various scenarios to explain the workflow of the cloud components including: segmentation, deep learning, indexing food images, decision making algorithms, calorie computation, scheduling management as part of the proposed cloud broker model. The implementation results of our system showed that the proposed cloud broker results in a 45% gain in the overall time taken to process the images in the cloud. With the use of dynamic cloud allocation mechanism, we were able to reduce the average time consumption by 77.21% when 60 images were processed in parallel.

Using distance estimation and deep learning to simplify calibration in food calorie measurement

High calorie intake in the human body on the one hand, has proved harmful in numerous occasions leading to several diseases and on the other hand, a standard amount of calorie intake has been deemed essential by dietitians to maintain the right balance of calorie content in human body. As such, researchers have proposed a variety of automatic tools and systems to assist users measure their calorie in-take. In this paper, we consider the category of those tools that use image processing to recognize the food, and we propose a method for fully automatic and user-friendly calibration of the dimension of the food portion sizes, which is needed in order to measure food portion weight and its ensuing amount of calories. Experimental results show that our method, which uses deep learning, mobile cloud computing, distance estimation and size calibration inside a mobile device, leads to an accuracy improvement to 95 percent on average compared to previous work.