Cognitive radios have been proposed as a means to implement efficient reuse of the licensed spectrum. The key feature of a cognitive radio is its ability to recognize the primary (licensed) user and adapt its communication strategy to minimize the interference that it generates. We consider a communication scenario in which the primary and the cognitive user wish to communicate to different receivers, subject to mutual interference. Modeling the cognitive radio as a transmitter with side-information about the primary transmission, we characterize the largest rate at which the cognitive radio can reliably communicate under the constraint that (i) no interference is created for the primary user, and (ii) the primary encoder-decoder pair is oblivious to the presence of the cognitive radio.

Cognitive Radio: An Information-Theoretic Perspective

Emerging technologies such as the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. One of the key challenges in running IoT applications in a Fog computing environment are resource allocation and task scheduling. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This survey will help the industry and research community synthesize and identify the requirements for Fog computing. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architectures and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction for the Fog computing paradigm.

Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions

Lung cancer is the most common cancer that cannot be ignored and cause death with late health care. Currently, CT can be used to help doctors detect the lung cancer in the early stages. In many cases, the diagnosis of identifying the lung cancer depends on the experience of doctors, which may ignore some patients and cause some problems. Deep learning has been proved as a popular and powerful method in many medical imaging diagnosis areas. In this paper, three types of deep neural networks (e.g., CNN, DNN, and SAE) are designed for lung cancer calcification. Those networks are applied to the CT image classification task with some modification for the benign and malignant lung nodules. Those networks were evaluated on the LIDC-IDRI database. The experimental results show that the CNN network archived the best performance with an accuracy of 84.15%, sensitivity of 83.96%, and specificity of 84.32%, which has the best result among the three networks.

/pdf/using-deep-learning-for-classification-of-lung-nodules-on-3uvmuasu29.pdf

Using Deep Learning for Classification of Lung Nodules on Computed Tomography Images.

Simple Object Access Protocol と,その応用としてのソフトウェアの組み合わせについて (渡邉昭夫教授退任記念号)

An efficient vehicle tracking system is designed and implemented for tracking the movement of any equipped vehicle from any location at any time. The proposed system made good use of a popular technology that combines a Smartphone application with a microcontroller. This will be easy to make and inexpensive compared to others. The designed in-vehicle device works using Global Positioning System (GPS) and Global system for mobile communication / General Packet Radio Service (GSM/GPRS) technology that is one of the most common ways for vehicle tracking. The device is embedded inside a vehicle whose position is to be determined and tracked in real-time. A microcontroller is used to control the GPS and GSM/GPRS modules. The vehicle tracking system uses the GPS module to get geographic coordinates at regular time intervals. The GSM/GPRS module is used to transmit and update the vehicle location to a database. A Smartphone application is also developed for continuously monitoring the vehicle location. The Google Maps API is used to display the vehicle on the map in the Smartphone application. Thus, users will be able to continuously monitor a moving vehicle on demand using the Smartphone application and determine the estimated distance and time for the vehicle to arrive at a given destination. In order to show the feasibility and effectiveness of the system, this paper presents experimental results of the vehicle tracking system and some experiences on practical implementations.

Design and implementation of vehicle tracking system using GPS/GSM/GPRS technology and smartphone application

Health monitoring systems have rapidly evolved recently, and smart systems have been proposed to monitor patient current health conditions, in our proposed and implemented system, we focus on monitoring the patient's blood pressure, and his body temperature. Based on last decade statistics of medical records, death rates due to hypertensive heart disease, shows that the blood pressure is a crucial risk factor for atherosclerosis and ischemic heart diseases; thus, preventive measures should be taken against high blood pressure which provide the ability to track, trace and save patient's life at appropriate time is an essential need for mankind. Nowadays, Globalization demands Smart cities, which involves many attributes and services, such as government services, Intelligent Transportation Systems (ITS), energy, health care, water and waste. This paper proposes a system architecture for smart healthcare based on GSM and GPS technologies. The objective of this work is providing an effective application for Real Time Health Monitoring and Tracking. The system will track, trace, monitor patients and facilitate taking care of their health; so efficient medical services could be provided at appropriate time. By Using specific sensors, the data will be captured and compared with a configurable threshold via microcontroller which is defined by a specialized doctor who follows the patient; in any case of emergency a short message service (SMS) will be sent to the Doctor's mobile number along with the measured values through GSM module. furthermore, the GPS provides the position information of the monitored person who is under surveillance all the time. Moreover, the paper demonstrates the feasibility of realizing a complete end-to-end smart health system responding to the real health system design requirements by taking in consideration wider vital human health parameters such as respiration rate, nerves signs … etc. The system will be able to bridge the gap between patients — in dramatic health change occasions- and health entities who response and take actions in real time fashion.

/pdf/smart-real-time-healthcare-monitoring-and-tracking-system-4do5eymp8q.pdf

Smart real-time healthcare monitoring and tracking system using GSM/GPS technologies

WiFi-enabled buses and stops may form the backbone of a metropolitan delay-tolerant network, which exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real-time information. This paper studies the routing problem in such a network. Assuming that the bus schedule is known, we maximize the delivery probability by a given deadline for each packet. Our approach takes the randomness into account, which stems from road traffic conditions, passengers boarding and alighting, and other factors that affect bus mobility. In this sense, this paper is one of the first to tackle quasi-deterministic mobility scenarios. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: 1) minimizing the expected traversal time over our graph; 2) maximizing the delivery probability over an infinite time-horizon; and 3) a recently proposed heuristic based on bus frequencies. We show that our optimal algorithm shows the best performance, but it essentially reduces to minimizing the expected traversal time. When transmissions frequently fail (more than half of the times), the algorithm behaves similarly to a heuristic that maximizes the delivery probability over an infinite time horizon. For reliable transmissions and values of deadlines close to the expected delivery time, the multicopy extension requires only ten copies to almost reach the performance of the costly flooding approach.

/pdf/timely-data-delivery-in-a-realistic-bus-network-2p0btwst85.pdf

Timely Data Delivery in a Realistic Bus Network

/pdf/smart-real-time-healthcare-monitoring-and-tracking-system-am14ad1rs1.pdf

Smart Real-Time Healthcare Monitoring and Tracking System using GSM/GPS Technologies

Nowadays, the evolution in transportation technologies makes the necessity for increasing road safety. In this context, we propose the implementation of a smart onboard GPS/GPRS system to be attached to vehicles for monitoring and controlling their speed. In case of traffic speed violation, a GPRS message containing information about the vehicle such as location and maximum speed is sent to a hosting server located in an authorized office so that the violated vehicle is ticketed. Moreover, this system can also track the vehicle’s current location on a Google Map, which is mostly beneficial when vehicles should follow a specific road and in case of robbery. Also geo-casting can have a major role in this model. Some sensors, such as shock/vibration sensor usually attached to the air-bags in vehicles, are attached to the system that in case of accident, it will send notifications to the nearest hospital, police station and civil defense. Our proposed model can be utilized for different implementations, both in public and private sectors. While similar existing systems in Palestine have focalized just on the tracking aspect of vehicles’ monitoring, it would be the first system supporting both ticketing and tracking.

Smart on-board transportation management system using gps/gsm/gprs technologies to reduce traffic violation in developing countries

Recently, many applications have been defined in order to track, monitor and notify vehicles either online or offline, in this work we focus on notifying subset of the vehicles based on their geographical location; such feature is called Geo- Casting, which is the process by which a traffic accident is automatically detected and relayed to the nearest hospital or health center, police station as well as to all vehicles in geographical proximity. In this paper, we study application layer solutions handling Geo-Casting feature as an additive value to vehicles' Smart on-Board Unit (SBU). Moreover, we provide a SBU design that integrates this feature, also we report fully functional end-to-end system implementation including the proposed SBU module and web based application meeting the functional requirements of Geo-Casting.

/pdf/smart-on-board-transportation-management-system-geo-casting-2qixusx5wh.pdf

Saed Tarapiah

Papers

Smart real-time healthcare monitoring and tracking system using GSM/GPS technologies

Timely Data Delivery in a Realistic Bus Network

Smart Real-Time Healthcare Monitoring and Tracking System using GSM/GPS Technologies

Smart on-board transportation management system using gps/gsm/gprs technologies to reduce traffic violation in developing countries

Smart on-board transportation management system Geo-Casting featured