There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Thank you very much for downloading table of integrals series and products. Maybe you have knowledge that, people have look hundreds times for their chosen books like this table of integrals series and products, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. table of integrals series and products is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the table of integrals series and products is universally compatible with any devices to read.

Table Of Integrals Series And Products

The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

/pdf/towards-6g-wireless-communication-networks-vision-enabling-2tj7a36yux.pdf

Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

/pdf/a-comprehensive-survey-on-machine-learning-for-networking-3t5h3kbpe8.pdf

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.

/pdf/smart-manufacturing-systems-for-industry-4-0-conceptual-1tb0xiq5x0.pdf

Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives

LoRa is a new ISM band wireless technology designed for low power, unlicensed, Long Range operation. LoRaWAN is a Wide Area Network protocol that incorporates the LoRa wireless into a networked infrastructure. The indoor and outdoor performance of these technologies, the physical layer wireless and multi-gateway wide area network, was evaluated across the central business district (CBD) of Glasgow city (Scotland). The results indicated that this technology can be a reliable link for low cost remote sensing applications.

Evaluation of LoRa and LoRaWAN for wireless sensor networks

A new paradigm to support the communication among modules dynamically placed on a reconfigurable device at run-time is presented. Based on the network on chip (NoC) infrastructure, we developed a dynamic communication infrastructure as well as routing methodologies capable to handle routing in a NoC with obstacles created by dynamically placed components. We prove the unrestricted reachability of components and pins, the deadlock-freeness and we finally show the feasibility of our approach by means on real life example applications.

DyNoC: A Dynamic Infrastructure for Communication in Dynamically Reconfigurable Devices

https://www12.informatik.uni-erlangen.de/publications/ahmadinia/BAMKFTV05.pdf

DyNoC: A dynamic infrastructure for communication in dynamically reconfugurable devices

Computer architects have been studying the dynamically reconfigurable computer (Schaumont, Verbauwhede, Keutzer, and Sarrafzadeh, "A Quick Safari through the Reconfiguration Jungle," in Proc. of the 38th Design Automation Conference, Las Vegas, pp. 127---177, 2001) for a number of years. New capabilities such as on-demand computing power, self-adaptiveness and self-optimization capabilities by restructuring the hardware on the fly at run-time is seen as a driving technology factor for current research initiatives such as autonomic (Kephart and Chess, Computer, 36:41---52, 2003; IBM Autonomic Computing Initiative, (http://www.research.ibm.com/autonomic/)) and organic computing (Muller-Schloer, von der Malsburg, and Wurtz, Inform.-Spektrum, 27:332---336, 2004; The Organic Computing Page, (http://www.organic-computing.org)). Much research work is currently devoted to models for partial hardware module relocation (SPP1148 Reconfigurable Computing Priority Program, (http://www12.informatik.uni-erlangen.de/spprr/)) and dynamically reconfigurable hardware reconfiguration on e.g., FPGA-based platforms. However, there are many physical restrictions and technical problems limiting the scope or applicability of these approaches. This led us to the development of a new FPGA-based reconfigurable computer called the Erlangen Slot Machine. The architecture overcomes many architectural constraints of existing platforms and allows a user to partially reconfigure hardware modules arranged in so-called slots. The uniqueness of this computer stems from (a) a new slot-oriented hardware architecture, (b) a set of novel inter-module communication paradigms, and (c) concepts for dynamic and partial reconfiguration management.

/pdf/the-erlangen-slot-machine-a-dynamically-reconfigurable-fpga-4mgxxhtn62.pdf

The Erlangen Slot Machine: A Dynamically Reconfigurable FPGA-based Computer

Smart homes reduce human intervention in controlling the heating ventilation and air conditioning (HVAC) systems for maintaining a comfortable indoor environment. The embedded intelligence in the sensor nodes is limited due to the limited processing power and memory in the sensor node. Cloud computing has become increasingly popular due to its capability of providing computer utilities as internet services. In this study, a model for the intelligent controller by integrating internet of things (IoT) with cloud computing and web services is proposed. The wireless sensor nodes for monitoring the indoor environment and HVAC inlet air, and wireless base station for controlling the actuators of HVAC have been developed. The sensor nodes and base station communicate through RF transceivers at 915 MHz. Random neural network (RNN) models are used for estimating the number of occupants, and for estimating the predicted-mean-vote-based setpoints for controlling the heating, ventilation, and cooling of the building. Three test cases are studied (Case 1—Data storage and implementation of RNN models on the cloud, Case 2—RNN models implementation on base station, Case 3—Distributed implementation of RNN models on sensor nodes and base stations) for determining the best architecture in terms of power consumption. The results have shown that by embedding the intelligence in the base station and sensor nodes (i.e., Case 3), the power consumption of the intelligent controller was 4.4% less than Case 1 and 19.23% less than Case 2.

/pdf/smart-random-neural-network-controller-for-hvac-using-cloud-5djd4a2f2c.pdf

Ali Ahmadinia

Papers

Evaluation of LoRa and LoRaWAN for wireless sensor networks

DyNoC: A Dynamic Infrastructure for Communication in Dynamically Reconfigurable Devices

DyNoC: A dynamic infrastructure for communication in dynamically reconfugurable devices

The Erlangen Slot Machine: A Dynamically Reconfigurable FPGA-based Computer

Smart Random Neural Network Controller for HVAC Using Cloud Computing Technology