Current constant speed IPO’s, usually, use Sampled-data IPO’s and constant speed lines use the wrong initialized software DDA-ipo’s, which make these IPO’s unusable. The Bresenham- and midpoint IPO’s are non-constant speed reference pulse IPO’s with bounded inaccuracy. By adding an ultra-fast 3-lines algorithm “PRM-cs” to the actual midpoint or Bresenham algorithms, we convert these midpoint-ipo’s to very fast, constant speed, reference pulse IPO’s. This applies to 2D-lines, 3D-lines, 2D-curves and 2D-NURBS. The PRM-cs measures, in real-time, the length of the discrete curve and the PRM-cs is completely new. We define the best IPO, the major axis principle and the LSD-priority. The major axis principle holds for the actual 3D-line IPO’s. These IPO’s are, generally, inaccurate, but they can be updated to constant speed 3D-line IPO’s, when the production manager agrees. The Digital Geometric Geometry (DAG) defines the discrete lines globally, but this global definition of a discrete 3D-line, gives discrete 3D-lines whose accuracy is much less than the accuracy of the best discrete 3D-lines (e.g. 37% worse). We describe the three causes of the inaccurate (imperfect) discrete 3D-lines. All existing pulse-rate or PRM-ipo’s use a wrong initialization, which deteriorates the accuracy. We determine the right initialization for the new PRM-cs and the updated PRM-ipo. We propose the benchmark-ipo “listSIM-ipo”. This constant speed IPO can, also, be used in real- time for every 2D- and 3D-curve. The 3rd-degree Trident NURB shows that the constant speed reference pulse method is much better than the existing sampled-data methods.

Communication breakdowns during natural disasters can significantly restrict disaster management operations. Furthermore, cellular networks may also be unreliable in such scenarios. Hence, establishing communication using alternative means is of importance in these scenarios. In this paper, we propose a prototype system to establish communication using wireless mesh network, through the use of stationary and mobile ground nodes, and aerial nodes using unmanned aerial vehicles (UAVs). This network is ad hoc and establishes connectivity without the usage of a cellular network or internet. Our system provides a complete end to end architecture, where we deploy an android application on smartphones at the user-end, the ad hoc network comprising of stationary and mobile nodes, and a graphical user interface (GUI) at the base station that facilitates situational awareness. We use the Robot Operating System (ROS) as the middleware for message synchronization between the nodes as well for UAV control. We evaluate the system for different system configurations by using UAVs and a semi-autonomous car. Our experimental results show that the system could be indispensable in providing large scale connectivity.

JPL’s own data correlate to 1% NEAR and Rosetta trajectory discrepancies to an unexpected doubling of path times in phase locked tracking. NEAR’s radar residuals illustrate the doubling to 5σ. Analysis of these and other NASA-tracked flybys shows that a distance sensitive anomalous signal generally exists. (Presented at IEEE NAECON 2019)

Smart building (SB), a promising solution to fast-paced and continuous urbanization around the world, includes the integration of a wide range of systems and services and involves the construction of multiple layers. SB is capable of sensing, acquiring, and processing a very large amount of data as well as performing appropriate actions and adaptation. Rapid increases in the number of connected nodes and thereby the data transmission demand of SB have led to conventional transmission and processing techniques becoming insufficient to provide satisfactory services. In order to enhance the intelligence of SBs and achieve efficient monitoring and control, sixth generation (6G) communication technologies, particularly indoor visible light communications (VLC) and machine learning (ML), are required to be incorporated in SBs. Herein, we envision a novel SB framework featuring a reliable data transmission network, powerful data processing, and reasoning abilities, all of which are enabled by 6G communications. Primary simulation results support the promising visions of the proposed SB framework.

In this paper, internet of things (IoT) connectivity in rural areas is investigated. Both fronthaul and backhaul considerations are studied. First, intelligent radio resource management (RRM) and network planning techniques are discussed for IoT access/fronthaul networks. The proposed RRM scheduling approach was shown to lead to good performance in scheduling IoT devices. Then, several backhauling techniques for providing connectivity to rural areas are investigated and their cost efficiency is analyzed. Techniques based on free space optics with solar powered devices are found to be a suitable backhaul solution.

This paper introduces a virtual synchronous machine (VSM) control scheme which is based on a minimized emulation of the model of a synchronous generator (SG) for a grid-forming wind turbine (WT) inverter. Based on this VSM control scheme, state-space representation and block diagram of the small-signal model for the VSM-controlled grid-forming inverter connected to an infinite bus are derived, which are used for small-signal stability analysis of the power angle in the VSM control scheme. This power angle small-signal stability (PAS) analysis is proposed as a simpler way for analyzing the influence on the rotor angle stability of power system from large share of VSM-controlled grid-forming inverters. Influence of parameters in the power loop of the control scheme on the PAS is investigated by analyzing the eigenvalue trajectories of the state matrix.

The controversy and argument on the usefulness of the physical layer (PHY) academic research for wireless communications are long-standing since the cellular communication paradigm gets to its maturity. In particular, researchers suspect that the performance improvement in cellular communications is primarily attributable to the increases in telecommunication infrastructure and radio spectrum instead of the PHY academic research, whereas concrete evidence is lacking. To respond to this controversy from an objective perspective, we employ econometric approaches to quantify the contributions of the PHY academic research and other performance determinants. Through empirical analysis and the quantitative evidence obtained, albeit preliminary, we shed light on the following issues: 1) what determines the cross-national differences in cellular network performance; 2) to what extent the PHY academic research and other factors affect cellular network performance; 3) what suggestions we can obtain from the data analysis for the stakeholders of the PHY research. To the best of our knowledge, this article is the first `empirical telecommunication research,' and the first effort to involve econometric methodologies to evaluate the usefulness of the PHY academic research.

The standardization of fifth generation (5G) communications has been completed, and the 5G network should be commercially launched in 2020. As a result, the visioning and planning of sixth generation (6G) communications has begun, with an aim to provide communication services for the future demands of the 2030s. Here we provide a vision for 6G that could serve a research guide in the post-5G era. We suggest that human-centric mobile communications will still be the most important application of 6G and the 6G network should be human centric. Thus, high security, secrecy, and privacy should be key features of 6G and should be given particular attention by the wireless research community. To support this vision, we provide a systematic framework in which potential application scenarios of 6G are anticipated and subdivided. We subsequently define key potential features of 6G and discuss the required communication technologies. We also explore the issues beyond communication technologies that could hamper research and deployment of 6G.

Despite the continued evolution of information systems methodologies for more than three decades, the rates of software rejection and failure are still high. This paper investigates the technological environment as a major cause of such disruptions. Additionally, the paper evaluates Agile and DevOps as remedial methodologies for managing the adverse impact of technological disruptions. The main findings affirm both Agile and DevOps as methodologies that emanated from improvements or re-engineering of earlier methodologies. Further findings discern most methodologies; including agile and DevOps; as not strategically focused but appraise DevOps as the most progressive methodology towards this respect. Rather than re-invent the wheel and come up with a new methodology, a framework that aligns DevOps for use in strategic information systems development is proposed. Besides, a more realistic definition of operations is postulated to bolster the alignment.  Keywords software

To meet the increasing demands for passenger data rates, modern railway communication networks face significant challenges. The advent of 5G communications after the long-term evolution (LTE) and LTE-Advanced (LTE-A) systems provides several technological advances to address these challenges. In this paper, after reviewing the main 5G communication aspects for modern railways, we present methods to ensure connectivity and energy efficiency for the passengers’ user equipment (UE) through the use of mobile relays (MRs) on top of the train wagons in conjunction with intelligent resource allocation. Relevant challenges to train connectivity are also presented, and suitable solutions are outlined.

Providing connectivity to around half of the World population living in rural or underprivileged areas is a tremendous challenge, but also a unique opportunity. In this paper, a survey of technologies for providing connectivity to rural areas, and that can help address this challenge, is provided. Fronthaul and backhaul techniques are discussed. In addition, energy and cost efficiency of the studied technologies are analyzed. Typical application scenarios in rural areas are discussed, and several country-specific use cases are surveyed. Directions for future evolution of rural connectivity are outlined.

Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames because the representation of each frame may vary significantly within a short time period. This also would inaccurately represent the traffic over a longer period of time such as the case of video. We propose a novel bio-inspired methodology that integrates analysis of the previous image frames of the video to represent the analysis of the current image frame, the same way a human being analyzes the current situation based on past experience. In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases. The proposed approach could be used for TMAV using Convolutional Neural Network (CNN) for applications where the number of objects in an image is the deciding factor for prediction and results also show that our proposed approach outperforms the state-of-the-art for the chosen test case. We also introduce a new metric named, Energy Consumption per Training Image (ECTI). Since, different CNN based models have different training capability and computing resource utilization, some of the models are more suitable for embedded device implementation than the others, and ECTI metric is useful to assess the suitability of using a CNN model in multi-processor systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in terms of lifespan of the embedded device using these MPSoCs.

The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.

Great people like Stephen Hawking and Max Brito have been suffering from this crippling phenomenon. Our project is an attempt to make lives of the physically challenged people by our extravagant project. We mean self-reliant, which will thereby reinstate their confidence and their happiness. The idea is to create an Eye Monitored System which allows movement of the patient‟s wheelchair depending on the eye movements. We know that a person suffering from quadriplegia can partially move his eyes and tilt his head, thus presenting an opportunity for detecting those movements. We have created a device where a patient sitting on the Wheel Chair assembly looking directly at the camera, is able to move in a direction just by looking in that direction. The camera signals are monitored by a MATLAB script, which will then guide the motors wired to the AtMega1284P Microcontroller over the Serial Interface to move in a particular direction. The system is cost effective and thus can be used by patients spread over a large economy range. We have implement a Smart mobile application to paramount our project and thereby we justify our project name “The Smart Mobility For Physically Challenged”

Malware behavioral graphs provide a rich source of information that can be leveraged for detection and classification tasks. In this paper, we propose a novel behavioral malware detection method based on Deep Graph Convolutional Neural Networks (DGCNNs) to learn directly from API call sequences and their associated behavioral graphs. In order to train and evaluate the models, we created a new public domain dataset of more than 40,000 API call sequences resulting from the execution of malware and goodware instances in a sandboxed environment. Experimental results show that our models achieve similar Area Under the ROC Curve (AUC-ROC) and F1-Score to Long-Short Term Memory (LSTM) networks, widely used as the base architecture for behavioral malware detection methods, thus indicating that the models can effectively learn to distinguish between malicious and benign temporal patterns through convolution operations on graphs. To the best of our knowledge, this is the first paper that investigates the applicability of DGCNN to behavioral malware detection using API call sequences.

As the end-users increasingly can provide flexibility to the power system, it is important to consider how this flexibility can be activated as a resource for the grid. Electricity network tariffs are one option that can be used to activate this flexibility. Therefore, by designing efficient grid tariffs, it might be possible to reduce the total costs in the power system by incentivizing a change in consumption patterns. This paper provides a methodology for optimal grid tariff design under decentralized decision-making and uncertainty in demand, power prices, and renewable generation. A bilevel model is formulated to adequately describe the interaction between the end-users and a distribution system operator. In addition, a centralized decision-making model is provided for benchmarking purposes. The bilevel model is reformulated as a mixed-integer linear problem solvable by branch-and-cut techniques. Results for a deterministic example and a stochastic case study are presented and discussed.

Before diode rectifier (DR) technology for connecting offshore wind farms (OWFs) to HVdc is deployed, in-depth studies are needed to assess the actual capabilities of DR-connected OWFs to contribute to the secure operation of the networks linked to them. This study assesses the capability of such an OWF to provide communication-less frequency support (CLFS) to an onshore ac network. It is shown that the HVdc link’s offshore terminal direct voltage can be estimated from measurements at the OWF’s point of connection with the DR platform. Two different methods are proposed for implementing CLFS in the OWF active power controls. In Method 1, the estimated offshore terminal direct voltage is used for estimating the onshore frequency deviation. In Method 2, the actual offshore terminal direct voltage measurement is used instead. Unique features of the provision of CLFS from OWFs connected to HVdc via DRs are highlighted, and the dynamic and static performance of the CLFS control scheme is compared to that of the communication-based frequency support scheme. To assess the impact of parameter estimation errors on the provision of CLFS, a parametric sensitivity study is presented as well, and recommendations are given to increase accuracy. The manuscript has been accepted for publication in IEEE Transactions on Sustainable Energy.

The recovery of sparse signals given their linear mapping on lower-dimensional spaces can be partitioned into a support estimation phase and a coefficient estimation phase. We propose to estimate the support with an oracle based on a deep neural network trained jointly with the linear mapping at the encoder. The divination of the oracle is then used to estimate the coefficients by pseudo-inversion. This architecture allows the definition of an encoding-decoding scheme with state-of-the-art recovery capabilities when applied to biological signals such as ECG and EEG, thus allowing extremely low-complex encoders. As an additional feature, oracle-based recovery is able to self-assess, by indicating with remarkable accuracy chunks of signals that may have been reconstructed with a non-satisfactory quality. This self-assessment capability is unique in the CS literature and paves the way for further improvements depending on the requirements of the specific application. As an example, our scheme is able to satisfyingly compress by a factor of 2.67 an ECG or EEG signal with a complexity equivalent to only 24 signed sums per processed sample.