scholarly journals FUNCTIONAL DAMAGE OF RADIO ELECTRONIC SYSTEMS

2021 ◽  
Vol 26 (4) ◽  
pp. 358-369
Author(s):  
L. F. Chernogor ◽  

Purpose: The most important problem of any state is protection of the control and management systems used for the country, national armed forces, high-risk facilities (nuclear power plants, large chemical plants, airports, etc.). Here, the fact that the means of attack can be deployed on ballistic and cruise missiles, aircraft, and drones should be accounted for. The flight altitude of these vehicles varies from ≈300 km to ≈ 10 m. Any attack vehicle is equipped with complex avionics consisting of circuit elements sensitive to electromagnetic fields. Since the 1980s, a new scientific and engineering direction has been developing, being termed as a “functional damage to avionics”. It is based on the creation of powerful means of electromagnetic radiation possessing the energetic capabilities of incapacitating avionics at significant distances (from ~ 100 m to ~ 1000 km). The purpose of this work is to analyze the possible functional damage to avionics with account for the tendencies in avionics technologies. Design/methodology/approach: The analysis is made on the capability of inflicting functional damage to avionics accounting for the modern trends in developing the powerful means of electromagnetic energy generation in the microwave and shorter wavelength ranges, miniaturization and integration of avionics circuit elements. The regression is constructed for the critical energy time dependence. It has been determined that for decades the critical energy required to damage the circuit elements shows a tendency to decrease. This is due to the further miniaturization and integration of microcircuits according to the Moore’s law, which is still valid for now. For a number of circuit elements, the critical energy is found to be in the range of 10-11–10-10 J. At the same time, a reverse tendency arises to protect avionics from being functionally damaged. In this case, the critical energy makes 10-7–10-6 J and greater. From the derived version of the basic equation of functional damage to avionics, the maximum distance at which the damage is possible with the energetics of the existing radio systems is estimated. For the ground-based facilities, this distance can attain hundreds of kilometers. For mobile vehicles, it can reach 10–100 km. Combining target detection, identification and avionics damage capabilities in one radio system has been validated and advised. The transition from the first mode of operation to the second one occurs at shorter distances with an increase of 2–3 orders of magnitude in the pulse energy. Findings: The regression equation has been obtained for the time dependence of the critical energy required for inflicting functional damage to avionics. Its constant decrease has been confirmed. Such a behavior is closely related to the Moore’s law, which characterizes the degree of miniaturization and integration of avionics circuit elements. It has been predicted that for a number of instruments the critical energy can be smaller than 10-11–10-10 J. A version of the basic equation of functional damage to avionics has been obtained. The maximum distance for a modern radio system to damage the avionics has been shown to attain many hundreds of kilometers. For the radio systems installed on mobile vehicles, this distance makes 10–100 km. Target detection, tracking and identification, as well as avionics damage capabilities, have been proved to be rationally combined in one radio system. To cause damage at a corresponding range, the pulse energy needs to be increased by a factor of 102–103. Conclusions: There are all science and technology prerequisites for developing effective radio systems inflicting functional damage to avionics and for the state defense and protection, armed forces, and high-risk facility controlling systems. Key words: functional damage; avionics; critical energy; Moore’s law; functional damage equation; radiolocation equation; detection and destruction range

Author(s):  
David Segal

Chapter 3 highlights the critical role materials have in the development of digital computers. It traces developments from the cat’s whisker to valves through to relays and transistors. Accounts are given for transistors and the manufacture of integrated circuits (silicon chips) by use of photolithography. Future potential computing techniques, namely quantum computing and the DNA computer, are covered. The history of computability and Moore’s Law are discussed.


Author(s):  
Daniel Pargman ◽  
Aksel Biørn-Hansen ◽  
Elina Eriksson ◽  
Jarmo Laaksolahti ◽  
Markus Robèrt
Keyword(s):  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Prasanna Date ◽  
Davis Arthur ◽  
Lauren Pusey-Nazzaro

AbstractTraining machine learning models on classical computers is usually a time and compute intensive process. With Moore’s law nearing its inevitable end and an ever-increasing demand for large-scale data analysis using machine learning, we must leverage non-conventional computing paradigms like quantum computing to train machine learning models efficiently. Adiabatic quantum computers can approximately solve NP-hard problems, such as the quadratic unconstrained binary optimization (QUBO), faster than classical computers. Since many machine learning problems are also NP-hard, we believe adiabatic quantum computers might be instrumental in training machine learning models efficiently in the post Moore’s law era. In order to solve problems on adiabatic quantum computers, they must be formulated as QUBO problems, which is very challenging. In this paper, we formulate the training problems of three machine learning models—linear regression, support vector machine (SVM) and balanced k-means clustering—as QUBO problems, making them conducive to be trained on adiabatic quantum computers. We also analyze the computational complexities of our formulations and compare them to corresponding state-of-the-art classical approaches. We show that the time and space complexities of our formulations are better (in case of SVM and balanced k-means clustering) or equivalent (in case of linear regression) to their classical counterparts.


2015 ◽  
Vol 59 (1) ◽  
pp. 33-35 ◽  
Author(s):  
Michael A. Cusumano ◽  
David B. Yoffie
Keyword(s):  

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