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Intellectual property (IP0 is a legal concept which refers to creations of the mind for which exclusive rights are recognized. Under intellectual property law, owners are granted certain exclusive rights to a variety of intangible assets such as musical, literary and artistic works; discoveries and inventions; and words, phrases, symbols and design. Common types of intellectual property rights include copyright, trademarks, patents, industrial design rights. Though intellectual property rights was not so used in 19th century and not until the late 20th Century. The term Intellectual Property goes back at least as far as 1867 with the founding to The North German Confederation whose constitution granted legislative power over the protection of intellectual property to the confederation. When the administrative secretariats established by the Paris Convention (1883) and the Berne Convention (1886) merged in 1893, they located in Berne, and also adopted the term intellectual property in their new combined title.

In the field of electronic warfare (EW), one of the crucial roles of electronic intelligence is the identification of radar signals. In an operational environment, it is very essential to identify radar emitters whether friend or foe so that appropriate radar countermeasures can be taken against them. With the electromagnetic environment becoming increasingly complex and the diversity of signal features, radar emitter identification with high recognition accuracy has become a significantly challenging task. Traditional radar identification methods have shown some limitations in this complex electromagnetic scenario. Several radar classification and identification methods based on artificial neural networks have emerged with the emergence of artificial neural networks, notably deep learning approaches. Machine learning and deep learning algorithms are now frequently utilized to extract various types of information from radar signals more accurately and robustly. This paper illustrates the use of Deep Neural Networks (DNN) in radar applications for emitter classification and identification. Since deep learning approaches are capable of accurately classifying complicated patterns in radar signals, they have demonstrated significant promise for identifying radar emitters. By offering a thorough literature analysis of deep learning-based methodologies, the study intends to assist researchers and practitioners in better understanding the application of deep learning techniques to challenges related to the classification and identification of radar emitters. The study demonstrates that DNN can be used successfully in applications for radar classification and identification.

BACKGROUND: A knowledge gap exists in understanding the beneficial use and duration of domiciliary supplemental oxygen (DSO) therapy among survivors of COVID-19 hospitalisations with persistent hypoxemia upon discharge. The purpose of this single center study was to begin to address this issue. METHODS: In this retrospective study we report features of U.S. military veterans residing in metropolitan Chicago with no prior DSO therapy who survived COVID-19 hospitalisation, were discharged on DSO, and were followed for 6 months. RESULTS: We found that the majority of the 65 elderly patients (median age, 70 years), predominantly obese Black males, who survived COVID-19 hospitalisations at the Jesse Brown VA Medical Center and discharged on DSO did not undergo a formal 6-min walk test (6 MWT) to re-assess ongoing ambulatory supplemental oxygen requirements (46 patients or 71%). Nonetheless, DSO therapy was discontinued in most patients predominantly within 8 weeks of hospital discharge (34 patients, 52%). In addition, a large proportion of patients, obese Blacks in particular, who survived COVID-19 hospitalisations and were treated with DSO for at least 8 weeks thereafter developed post-acute sequelae of COVID-19 infection (PASC) (30 patients, 46%). CONCLUSIONS: Given these findings, we recommend that healthcare providers be appraised about proper monitoring and evaluation, including timely performance of 6 MWT, of patients who survived COVID-19 hospitalisations and treated with DSO for persistent hypoxemia upon discharge. Whether obese Black males who survived COVID-19 hospitalisations and are treated with DSO thereafter have an elevated risk in developing PASC remains to be determined in larger, prospective studies.