A Deep Learning Approach to Radio Signal Denoising

This paper proposes a Deep Learning approach to radio signal de-noising. This approach is data-driven, thus it allows de-noising signals, corresponding to distinct protocols, without requiring explicit use of expert knowledge, in this way granting higher flexibility. The core component of the Artificial Neural Network architecture used in this work is a Convolutional De-noising AutoEncoder. We report about the performance of the system in spectrogram-based denoising of the protocol preamble across protocols of the IEEE 802.11 family, studied using simulation data. This approach can be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A further perspective advantage of using the AutoEncoders in such a pipeline is that they can be co-trained with the downstream classifier (protocol detector), to optimize its accuracy

Using autoencoders for radio signal denoising

We investigated the use of a Deep Learning approach to radio signal de-noising. This data-driven approach has does not require explicit use of expert knowledge to set up the parameters of the denoising procedure and grants great flexibility across many channel conditions. The core component used in this work is a Convolutional De-noising AutoEncoder, known to be very effective in image processing. The key of our approach consists in transforming the radio signal into a representation suitable to the CDAE: we transform the time-domain signal into a 2D signal using the Short Time Fourier Transform. We report about the performance of the approach in preamble denoising across protocols of the IEEE 802.11 family, studied using simulation data. This approach could be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A perspective advantage of using the AutoEncoders in that pipeline is that they can be co-trained with the downstream classifier, to optimize the classification accuracy

Analysis of Improved µ-Law Companding Technique for OFDM Systems

YesHigh Peak-to-Average-Power Ratio (PAPR) of transmitted signals is a common problem in broadband telecommunication systems using an orthogonal frequency division multiplexing (OFDM) modulation scheme, as it increases transmitter power consumption. In consumer applications where it impacts mobile terminal battery life and infrastructure running costs, this is a major factor in customer satisfaction. Companding techniques have been recently used to alleviate this high PAPR. In this paper, a companding scheme with an offset, amidst two nonlinear companding levels, is proposed to achieve better PAPR reduction while maintaining an acceptable bit error rate (BER) level, resulting in electronic products of higher power efficiency. Study cases have included the effect of companding on the OFDM signal with and without an offset. A novel closed-form approximation for the BER of the proposed companding scheme is also presented, and its accuracy is compared against simulation results. A method for choosing best companding parameters is presented based on contour plots. Practical emulation of a real time OFDM-based system has been implemented and evaluated using a Field Programmable Gate Array (FPGA)

Notes for genera: basal clades of Fungi (including Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota)

Compared to the higher fungi (Dikarya), taxonomic and evolutionary studies on the basal clades of fungi are fewer in number. Thus, the generic boundaries and higher ranks in the basal clades of fungi are poorly known. Recent DNA based taxonomic studies have provided reliable and accurate information. It is therefore necessary to compile all available information since basal clades genera lack updated checklists or outlines. Recently, Tedersoo et al. (MycoKeys 13:1--20, 2016) accepted Aphelidiomycota and Rozellomycota in Fungal clade. Thus, we regard both these phyla as members in Kingdom Fungi. We accept 16 phyla in basal clades viz. Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota. Thus, 611 genera in 153 families, 43 orders and 18 classes are provided with details of classification, synonyms, life modes, distribution, recent literature and genomic data. Moreover, Catenariaceae Couch is proposed to be conserved, Cladochytriales Mozl.-Standr. is emended and the family Nephridiophagaceae is introduced

NSC324621

Abstract — This paper presents an online controller for tracking power-budgets in multicore processors using dynamic voltage-frequency scaling. The proposed control law comprises an integral controller whose gain is adjusted online based on the derivative of the power-frequency relationship. The control law is designed to achieve rapid settling time, and its tracking property is formally proven. Importantly, the controller design does not require off-line analysis of application workloads making it feasible for emerging heterogeneous and asymmetric multicore processors. Simulation results are presented for controlling power dissipation in multiple cores of an asymmetric multicore processor. Each core is i) equipped with the controller, ii) assigned a power budget, and iii) operates independently in tracking to its power budget. We use a cycle-level multicore simulator driven by traces from SPEC2006 benchmarks demonstrating that the proposed algorithm achieves a faster settling time than examples of a static setting of the controller gain. I

Selection of Information Streams in Social Sensing: an Interdependence- and Cost-aware Ranking Method

In this work we address the problem of critical source selection in social sensing. We propose an approach to the ranking of information streams, which is aware of the interdependence among streams (redundancy and synergies), of the cost of individual streams, and of the cost related to the integration of multiple streams. The method is based on the use of the Coalitional Game Theory concept of Power Index, and relies on the polynomial-time estimate of the stream sets characteristics. With respect to other works using a power index, the method takes into account that the problem has a non-trivial cost structure