COVID-19 Diagnosis Using Spectral and Statistical Analysis of Cough Recordings Based on the Combination of SVD and DWT

تستخدم الإشارات الصوتية التي يولدها جسم الإنسان بشكل روتيني من قبل التخصصين في البحوث والتطبيقات الصحية  للمساعدة في تشخيص بعض  الامراض أو تقييم تقدم المرض. وبالنظر إلى التقنيات الجديدة ، من الممكن في الوقت الحاضر جمع الأصوات التي يولدها الإنسان ، مثل السعال. ويمكن بعد ذلك اعتماد تقنيات التعلم الآلي المستندة إلى الصوت من أجل التحليل التلقائي للبيانات التي تم جمعها مما يوفر معلومات قيمة غنية من إشارة السعال واستخراج الميزات الفعالة من فترة زمنية محدودة الطول تتغير كدالة للوقت. في هذا البحث يتم اقتراح وتقديم خوارزمية  للكشف عن COVID-19 وتشخيصه من خلال معالجة السعال الذي يتم جمعه من المرضى الذين يعانون من الأعراض الأكثر شيوعًا لهذا الوباء. تعتمد الطريقة المقترحة على اعتماد مزيج من تحليل القيمة المفردة (SVD) وتحويل المويجات المنفصل (DWT).  وقد أدى الجمع بين هاتين التقنيتين لمعالجة الإشارات إلى اتباع نهج جيد للتعرف على السعال ، حيث يولد ويستخدم الحد الأدنى من الميزات الفعالة. وفي هذه الخوارزمية المقترحة يتم تطبيق الترددات المتوسطة (mean and median)، والمعروفة بأنها أكثر الميزات المفيدة في مجال التردد ، لإنشاء مقياس إحصائي فعال لمقارنة النتائج. بالإضافة إلى الحصول على معدل كشف وتمييز عاليين ، تتميز الخوارزمية المقترحة بكفاءتها حيث يتم تحقيق تخفيض 200 مرة، من حيث عدد العمليات. على الرغم من حقيقة أن أعراض الأشخاص المصابين وغير المصابين في الدراسة بها الكثير من أوجه التشابه ، فإن نتائج التشخيص التي تم الحصول عليها من تطبيق نهجنا تُظهر معدل تشخيص مرتفعًا، والذي تم إثباته من خلال مطابقتها مع اختبارات PCR ذات الصلة. نعتقد أنه يمكن تحقيق أداء أفضل من خلال توسيع مجموعة البيانات ، مع تضمين الأشخاص الأصحاء.Healthcare professionals routinely use audio signals, generated by the human body, to help diagnose disease or assess its progression. With new technologies, it is now possible to collect human-generated sounds, such as coughing. Audio-based machine learning technologies can be adopted for automatic analysis of collected data. Valuable and rich information can be obtained from the cough signal and extracting effective characteristics from a finite duration time interval that changes as a function of time. This article presents a proposed approach to the detection and diagnosis of COVID-19 through the processing of cough collected from patients suffering from the most common symptoms of this pandemic. The proposed method is based on adopting a combination of Singular Value Decomposition (SVD), and Discrete Wavelet Transform (DWT). The combination of these two signal processing techniques is gaining lots of interest in the field of speaker and speech recognition. As a cough recognition approach, we found it well-performing, as it generates and utilizes an efficient minimum number of features. Mean and median frequencies, which are known to be the most useful features in the frequency domain, are applied to generate an effective statistical measure to compare the results. The hybrid structure of DWT and SVD, adopted in this approach adds to its efficiency, where a 200 times reduction, in terms of the number of operations, is achieved. Despite the fact that symptoms of the infected and non-infected people used in the study are having lots of similarities, diagnosis results obtained from the application of the proposed approach show high diagnosis rate, which is proved through the matching with relevant PCR tests.  The proposed approach is open for more improvements with its performance further assured by enlarging the dataset, while including healthy people

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques

The growing use of voice user interfaces has led to a surge in the collection and storage of speech data. While data collection allows for the development of efficient tools powering most speech services, it also poses serious privacy issues for users as centralized storage makes private personal speech data vulnerable to cyber threats. With the increasing use of voice-based digital assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the increasing ease with which personal speech data can be collected, the risk of malicious use of voice-cloning and speaker/gender/pathological/etc. recognition has increased. This thesis proposes solutions for anonymizing speech and evaluating the degree of the anonymization. In this work, anonymization refers to making personal speech data unlinkable to an identity while maintaining the usefulness (utility) of the speech signal (e.g., access to linguistic content). We start by identifying several challenges that evaluation protocols need to consider to evaluate the degree of privacy protection properly. We clarify how anonymization systems must be configured for evaluation purposes and highlight that many practical deployment configurations do not permit privacy evaluation. Furthermore, we study and examine the most common voice conversion-based anonymization system and identify its weak points before suggesting new methods to overcome some limitations. We isolate all components of the anonymization system to evaluate the degree of speaker PPI associated with each of them. Then, we propose several transformation methods for each component to reduce as much as possible speaker PPI while maintaining utility. We promote anonymization algorithms based on quantization-based transformation as an alternative to the most-used and well-known noise-based approach. Finally, we endeavor a new attack method to invert anonymization.Comment: PhD Thesis Pierre Champion | Universit\'e de Lorraine - INRIA Nancy | for associated source code, see https://github.com/deep-privacy/SA-toolki

Европейский и национальный контексты в научных исследованиях

В настоящем электронном сборнике «Европейский и национальный контексты в научных исследованиях. Технология» представлены работы молодых ученых по геодезии и картографии, химической технологии и машиностроению, информационным технологиям, строительству и радиотехнике. Предназначены для работников образования, науки и производства. Будут полезны студентам, магистрантам и аспирантам университетов.=In this Electronic collected materials “National and European dimension in research. Technology” works in the fields of geodesy, chemical technology, mechanical engineering, information technology, civil engineering, and radio-engineering are presented. It is intended for trainers, researchers and professionals. It can be useful for university graduate and post-graduate students

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization