Andreas Jakobsson – författare

Allt började med nyfikenhet. En kompis till Andreas Jakobsson berättade lyriskt om en matbutik i Uppsala. Inte för att butiken var extra billig eller bra utan för att dess soprum var lika lättillgängligt som välförsett. Andreas sögs in i dumpstrarnas värld och snart levde hans lilla familj i lyx och överflöd, av »saker som pappa hittat på natten«. Köket fylldes med frukt och grönt från hela världen, kaffe, Anthon Berg-marsipan, öl, dessertostar av alla sorter, pinjenötter, Pringles-chips Ju mer Andreas släpade hem desto större blev hans nyfikenhet: Vad är det som gör att alltmer utmärkt mat slängs hos bonden, i butikerna och av oss konsumenter? Andreas gav sig ut på en resa genom Svinnlandet Sverige och fick skrämmande svar från butikschefer, forskare och storföretag. I Svinnlandet berättar Andreas Jakobsson hur han under två år levt på gratis mat som andra slängt. Han dissekerar systemfelen som skapat detta vansinne och pekar samtidigt på konkreta lösningar. Dessutom: Handla smartare! Så räddar du din mat! Tips för nybörjardumpstraren!

This book reports on advanced theories and methods in two related engineering fields: electrical and electronic engineering, and communications engineering and computing. It highlights areas of global and growing importance, such as renewable energy, power systems, mobile communications, security and the Internet of Things (IoT). The contributions cover a number of current research issues, including smart grids, photovoltaic systems, wireless power transfer, signal processing, 4G and 5G technologies, IoT applications, mobile cloud computing and many more. Based on the proceedings of the Second International Conference on Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM 2018), held in Mauritius from November 28 to 30, 2018, the book provides graduate students, researchers and professionals with a snapshot of the state-of-the-art and a source of new ideas for future research and collaborations.

Periodic signals can be decomposed into sets of sinusoids having frequencies that are integer multiples of a fundamental frequency. The problem of finding such fundamental frequencies from noisy observations is important in many speech and audio applications, where it is commonly referred to as pitch estimation. These applications include analysis, compression, separation, enhancement, automatic transcription and many more. In this book, an introduction to pitch estimation is given and a number of statistical methods for pitch estimation are presented. The basic signal models and associated estimation theoretical bounds are introduced, and the properties of speech and audio signals are discussed and illustrated. The presented methods include both single- and multi-pitch estimators based on statistical approaches, like maximum likelihood and maximum a posteriori methods, filtering methods based on both static and optimal adaptive designs, and subspace methods based on the principles of subspace orthogonality and shift-invariance. The application of these methods to analysis of speech and audio signals is demonstrated using both real and synthetic signals, and their performance is assessed under various conditions and their properties discussed. Finally, the estimators are compared in terms of computational and statistical efficiency, generalizability and robustness.Table of Contents: Fundamentals / Statistical Methods / Filtering Methods / Subspace Methods / Amplitude Estimation

Periodic signals can be decomposed into sets of sinusoids having frequencies that are integer multiples of a fundamental frequency. The problem of finding such fundamental frequencies from noisy observations is important in many speech and audio applications, where it is commonly referred to as pitch estimation. These applications include analysis, compression, separation, enhancement, automatic transcription and many more. In this book, an introduction to pitch estimation is given and a number of statistical methods for pitch estimation are presented. The basic signal models and associated estimation theoretical bounds are introduced, and the properties of speech and audio signals are discussed and illustrated. The presented methods include both single- and multi-pitch estimators based on statistical approaches, like maximum likelihood and maximum a posteriori methods, filtering methods based on both static and optimal adaptive designs, and subspace methods based on the principles of subspace orthogonality and shift-invariance. The application of these methods to analysis of speech and audio signals is demonstrated using both real and synthetic signals, and their performance is assessed under various conditions and their properties discussed. Finally, the estimators are compared in terms of computational and statistical efficiency, generalizability and robustness.Table of Contents: Fundamentals / Statistical Methods / Filtering Methods / Subspace Methods / Amplitude Estimation