Production planning and control systems are known for their complexity, especially for large scale production units. The Unified Modeling Language (UML) is known for its efficiency in modeling such complex systems for better visualization and as an initial step for software implementation. In this paper, UML is utilized to model production planning and control systems. The models developed include functional, and behavioral models represented through a use case diagram, an activity diagram, and a communication diagram. The proposed models serve as the first step towards implementing a software

This work is fulfilled in the context of the optimized monitoring of Internet of Things (IoT) networks. IoT networks are faulty; Things are resource-constrained in terms of energy and computational capabilities; they are also connected via lossy links. For IoT systems performing a critical mission, it is crucial to ensure connectivity, availability, and network reliability, which requires proactive network monitoring. The idea is to oversee the network state and functioning of the nodes and links; to ensure the early detection of faults and decrease node-unreachability times. It is imperative

The Maximum Power Point Tracker (MPPT) provides the most efficient use of a Photo-voltaic system independent of irradiance or temperature fluctuations. This paper introduces the modeling and control of a photo-voltaic system operating at MPPT using the arithmetic optimization algorithm (AOA). The single and double Photo-voltaic models are investigated. Their optimal unknown parameters are extracted using AOA based on commercial Photo-voltaic datasheets. A comparison is performed between these optimal parameters extracted by AOA and other optimization techniques presented in the literature

A novel algorithm for view-invariant human action recognition is presented. This approach is based on Two-Dimensional Principal Component Analysis (2DPCA) applied directly on the Motion Energy Image (MEI) or the Motion History Image (MHI) in both the spatial domain and the transform domain. This method reduces the computational complexity by a factor of at least 66, achieving the highest recognition accuracy per camera, while maintaining minimum storage requirements, compared with the most recent reports in the field. Experimental results performed on the Weizmann action and the INIRIA IXMAS

Objectives: The purpose of this study was to determine the prognostic value of strain-encoded magnetic resonance imaging (SENC) during high-dose dobutamine stress cardiac magnetic resonance imaging (DS-MRI) compared with conventional wall motion readings. Background: Detection of inducible ischemia by DS-MRI on the basis of assessing cine images is subjective and depends on the experience of the readers, which may influence not only the diagnostic classification but also the risk stratification of patients with ischemic heart disease. Methods: In all, 320 consecutive patients with suspected or

Time series classification is a supervised learning problem used in many vital applications. Classification of data varying with time is considered an important and challenging pattern recognition task. The temporal aspect and lack of features in time series data makes the learning process different from traditional classification problems. In this paper we propose a multiple classifier system approach for time series classification. The proposed approach adaptively integrates extracted local and global features together with distance similarity based methods. A feature extraction process is

In this paper, we report our experience in working with Network Address Translators (NATs). Traditionally, there were only 4 types of NATs. For each type, the (im)possibility of traversal is well-known. Recently, the NAT community has provided a deeper dissection of NAT behaviors resulting into at least 27 types and documented the (im)possibility of traversal for some types. There are, however, two fundamental issues that were not previously tackled by the community. First, given the more elaborate set of behaviors, it is incorrect to reason about traversing a single NAT, instead combinations

In this paper, we describe an application-level emulator for P2P systems with a special focus on high reproducibil-ity. We achieve reproduciblity by taking control over the scheduling ofconcurrent events from the operating system. We accomplish that for inter-and intra-peer concurrency. The development ofthe system was driven by the need to enhance the testing process ofan already-developed industrial product. Therefore, we were constrained by the architecture ofthe overlying application. However, we managed to provide highly transparent emulation by wrapping standard/widely-used networking

Objectives: This study sought to evaluate the diagnostic accuracy of strain-encoded cardiac magnetic resonance (SENC) for the detection of inducible ischemia during intermediate stress. Background: High-dose dobutamine stress cardiac magnetic resonance (DS-CMR) is a well-established modality for the noninvasive detection of coronary artery disease (CAD). However, the assessment of cine scans relies on the visual interpretation of wall motion, which is subjective, and modalities that can objectively and quantitatively assess the time course of myocardial strain response during stress are

In this paper, we study a wireless powered cellular network (WPCN) supported with network coding capability. In particular, we consider a network consisting of k cellular users (CUs) served by a hybrid access point (HAP) that takes over energy transfer to the users on top of information transmission over both the uplink (UL) and downlink (DL). Each CU has k+1 states representing its communication behavior, and collectively are referred to as the user demand profile. Opportunistically, when the CUs have information to be exchanged through the HAP, it broadcasts this information in coded format