Employing Type-2 Fuzzy Logic Systems in the Efforts to Realize Ambient Intelligent Environments [Application Notes]

Ambient Intelligence (AmI) is an emerging vision that aims to realize intelligent environments which are sensitive and responsive to the users' needs and behaviors. This paper presents an insight on the benefits that type-2 Fuzzy Logic Systems (FLSs) can offer towards the efforts to realize Ambient Intelligent Environments (AIEs). We will introduce research results from the Scaleup project showing different type-2 FLSs based applications in AIEs. Such applications include intelligent machine vision systems, blending real and virtual realities over dispersed geographical areas and allowing natural communication between the AIE and humans

Radio Coverage and Device Capacity Dimensioning Methodologies for IoT LoRaWAN and NB-IoT Deployments in Urban Environments

This paper focuses on the study of IoT network deployments, in both unlicensed and licensed bands, considering LoRaWAN and NB-IoT standards, respectively. The objective is to develop a comprehensive and detailed network planning and coverage dimensioning methodology for assessing key metrics related to the achieved throughput and capacity for specific requirements in order to identify tradeoffs and key issues that are related to the applicability of IoT access technologies for representative use case types. This paper will provide a concise overview of key characteristics of IoT representative IoT access network standards that are considered for being deployed in unlicensed and licensed bands and will present a methodology for modeling the characteristics of both access network technologies in order to assess their coverage and capacity considering different parameters

Classifying Breast Density from Mammogram with Pretrained CNNs and Weighted Average Ensembles

We are currently experiencing a revolution in data production and artificial intelligence (AI) applications. Data are produced much faster than they can be consumed. Thus, there is an urgent need to develop AI algorithms for all aspects of modern life. Furthermore, the medical field is a fertile field in which to apply AI techniques. Breast cancer is one of the most common cancers and a leading cause of death around the world. Early detection is critical to treating the disease effectively. Breast density plays a significant role in determining the likelihood and risk of breast cancer. Breast density describes the amount of fibrous and glandular tissue compared with the amount of fatty tissue in the breast. Breast density is categorized using a system called the ACR BI-RADS. The ACR assigns breast density to one of four classes. In class A, breasts are almost entirely fatty. In class B, scattered areas of fibroglandular density appear in the breasts. In class C, the breasts are heterogeneously dense. In class D, the breasts are extremely dense. This paper applies pre-trained Convolutional Neural Network (CNN) on a local mammogram dataset to classify breast density. Several transfer learning models were tested on a dataset consisting of more than 800 mammogram screenings from King Abdulaziz Medical City (KAMC). Inception V3, EfficientNet 2B0, and Xception gave the highest accuracy for both four- and two-class classification. To enhance the accuracy of density classification, we applied weighted average ensembles, and performance was visibly improved. The overall accuracy of ACR classification with weighted average ensembles was 78.11%

A Driver Safety Information Broadcast Protocol for VANET

Due to the highly mobile nature of VANET, especially on highways, a reliable and fast penetration of emergency messages is required so that in-time decisions can be performed. A broadcast routing protocol can perform flooding in sparse network but it will suffer from high control overhead, higher delay and lower packet delivery ratio in dense environment. Since, a significant number of VANET messages including neighbour discovery, safety, destination discovery, location and service advertisements is broadcast, therefore, the area of broadcast routing is important and needs careful design considerations. In this article, we propose ZoomOut Broadcast Routing Protocol for driver safety information dissemination in VANET. In ZBRP, 1-hop neighbour discovery messages are used in an intelligent way based on the speed and inter-vehicle distance of 1-hop neighbours to select a front and a behind vehicle. A neighbour from the front area is called front relative while the neighbour from behind area is called behind relative. During the processing of multi-hop safety messages, only a front or a behind relative rebroadcasts a safety message whereas non-relatives drop it. ZBRP is compared with G-AODV, PGB and DV-CAST through ns-2 simulations. The results show that ZBRP performs better than the stated protocols in terms of network penetration time, packet delivery and broadcast suppression

An Ambient Intelligent and Energy Efficient Food Preparation System Using Linear General Type-2 Fuzzy Logic Based Computing with Words Framework [Application Notes]

Ambient Intelligence (AmI) is a multidisciplinary paradigm, which positively alters the relationship between humans and technology. Concerning home environments, the functions of AmI vision include home automation, communication, entertainment, working and learning. In the area of communication, AmI still needs better mechanisms for human-computer communication. A natural human-computer interaction necessitates having systems capable of modelling words and computing with them. For this purpose, the paradigm of Computing With Words (CWWs) can be employed to mimic human-like communication in Ambient Intelligent Environments (AIEs). This paper demonstrates the extendibility of Linear General Type-2 (LGT2) Fuzzy Logic based CWWs Framework to create an advanced real-world application, which integrates a semi-autonomous, safe and energy efficient electric hob. The motivation of this work is twofold: 1) there is a need to develop transparent human-computer communication rather than embedding obtrusive tablets and computing equipment throughout our surroundings, and 2) one of the most hazardous and energy consuming household devices, the electric hob, does not have competent levels of intelligence and energy efficiency. The proposed Ambient Intelligent Food Preparation System (AIFPS) can increase user comfort, facilitate food preparation, minimize energy consumption and be a useful tool for the elderly and people with major disabilities including vision impairment. The results of real-world experiments with various lay users in the intelligent flat (iSpace) show the success of AIFPS in providing up to 55.43% improved natural interaction (compared to Interval Type-2 based CWWs Framework) while achieving semi-autonomous, safe and energy efficient cooking that can save energy between 11.5% and 35.2%

Relay selection based clustering techniques for high density LTE networks

In very crowded areas, a large number of LTE users contained in a single cell will try to access services at the same time causing high load on the Base Station (BS). Some users may be blocked from getting their requested services due to this high load. Using a two-hop relay architecture can help in increasing the system capacity, increasing coverage area, decreasing energy consumption, and reducing the BS load. Clustering techniques can be used to configure the nodes in such two-layer topology. This paper proposes a new algorithm for relay selection based on the Basic Sequential Algorithmic Scheme (BSAS) along with power control protocol. Unlike other capacity improving techniques such as small cells and relay stations this approach does not require additional infrastructure. Instead, users themselves will act as a temporary relay stations. Modifications are implemented to the original BSAS to make it suitable for LTE environment and to improve its performance. The protocol for resource allocation and power control is implemented assuming a multi cell scenario. The algorithm is compared to other relaying and clustering schemes in addition to the conventional LTE. The simulation results show that the proposed algorithm has improved system capacity and energy consumption compared to other existing clustering/relaying schemes

Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems

In recent years, various applications have emerged requiring linear topologies of wireless sensor networks (WSN). Such topologies are used in pipeline (water/oil/gas) monitoring systems. The linear structure has a significant impact on network performance in terms of delay, throughput, and power consumption. Regarding communication efficiency, routing protocols play a critical role, considering the special requirements of linear topology and energy resources. Therefore, the challenge is to design effective routing protocols that can address the diverse requirements of the monitoring system. In this paper, we present various wireless communication technologies and existing leak detection systems. We review different routing protocols focusing on multi-hop hierarchical protocols, highlighting the limitations and design issues related to packet routing in linear pipeline leak detection networks. Additionally, we present a LoRa multi-hop model for monitoring aboveground oil pipelines. A set of model parameters are identified such as the distance between sensors. In addition, the paper determines some calculations to estimate traffic congestion and energy consumption. Several alternative model designs are investigated. The model is evaluated using different multi-hop communication scenarios, and we compare the data rate and energy to provide an energy-efficient and low-cost leak detection system

Development of Platform Independent Mobile Learning Tool in Saudi Universities

The term “mobile learning” (or “m-learning”) refers to using handheld phones to learn and wireless computing as a learning tool and connectivity technology. This paper presents and explores the latest mobile platform for teaching and studying programming basics. The M-Learning tool was created using a platform-independent approach to target the largest available number of learners while reducing development and maintenance time and effort. Since the code is completely shared across mobile devices (iOS, Android, and Windows Phone), students can use any smartphone to access the app. To make the programme responsive, scalable, and dynamic, and to provide students with personalised guidance, the core application is based on an analysis design development implementation and assessment (ADDIE) model implemented in the Xamarin framework. The application’s key features are depicted in a prototype. An experiment is carried out on BS students at a university to evaluate the efficacy of the generated application. A usefulness questionnaire is administered to an experimental community in order to determine students’ expectations of the developed mobile application’s usability. The findings of the experiment show that the application is considerably more successful than conventional learning in developing students’ online knowledge assessment abilities, with an impact size of 1.96. The findings add to the existing mobile learning literature by defining usability assessment features and offering a basis for designing platform-independent m-learning applications. The current findings are explored in terms of their implications for study and teaching practice

Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs

Background: Identifying the diagnostic value of bitewing radiographs (BW) is highly dependent on the operator’s knowledge and experience. The aim of this study is to assess the effectiveness of machine learning (ML) to classify the BW according to their diagnostic quality. Methods: 864 BW radiographs from records of 100 patients presented at King Abdulaziz University Dental Hospital, Jeddah, Saudi Arabia were assessed. The radiographic errors in representing proximal contact areas (n = 1951) were categorized into diagnostic and non-diagnostic. Labeling and training of the BW were done using Roboflow. Data were divided into validation, training, and testing sets to train the pre-trained model Efficientdet-d0 using TensorFlow. The model’s performance was assessed by calculating recall, precision, F1 score, and log loss value. Results: The model excelled at detecting “overlap within enamel” and “overlap within restoration (clear margins) with F1 score of 0.89 and 0.76, respectively. The overall system errors made by the built model showed a log loss value of 0.15 indicating high accuracy of the model. Conclusions: The model is a “proof of concept” for the effectiveness of ML in diagnosing the quality of the BW radiographs based on the contact areas. More dataset specification and optimization are needed to overcome the class imbalance