Noise Reduction in Industry Based on Virtual Instrumentation

This paper discusses the reduction of background noise in an industrial environment to extend human-machine-interaction.In the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G deployment.This study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a dataset.The experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from Microsoft.We tested a hybrid algorithm for noise reduction and its impact on voice command recognition efficiency.Using virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested algorithms.Laser welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+ICA.Press workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

Enhancements of SDR-Based FPGA System for V2X-VLC Communications

This pilot study focuses on a real measurements and enhancements of a software defined radio-based system for vehicle-to everything visible light communication(SDR-V2X-VLC).The presented system is based on a novel adaptive optimization of the feed-forward software defined equalization(FFSDE)methods of the least mean squares(LMS),normalized LMS(NLMS)and QR decomposition-based recursive least squares(QR-RLS)algorithms.Individual parameters of adaptive equalizations are adjusted in real-time to reach the best possible results.Experiments were carried out on a conventional LED Octavia III taillight drafted directly from production line and universal software radio peripherals(USRP)from National Instruments.The transmitting/receiving elements used multistate quadrature amplitude modulation(M-QAM)implemented in LabVIEW programming environment.Experimental results were verified based on bit error ratio(BER),error vector magnitude(EVM)and modulation error ratio(MER).Experimental results of the pilot study unambiguously confirmed the effectiveness of the proposed solution(longer effective communication range,higher immunity to interference,deployment of higher state QAM modulation formats,higher transmission speeds etc.),as the adaptive equalization significantly improved BER,MER and EVM parameters.The best results were achieved using the QR-RLS algorithm.The results measured on deployed QR-RLS algorithm had significantly better Eb/N0(improved by approx.20 dB)and BER values(difference by up to two orders of magnitude).

Pm21 from Haynaldia villosa Encodes a CC-NBS- LRR Protein Conferring Powdery Mildew Resistance in Wheat

Dear Editor Wheat powdery mildew, caused by Blumeria graminis f. sp. tritici （Bgt）, is a destructive disease of wheat throughout the world. One of the most important environmental-friendly and economical methods to reduce wheat loss caused by Bgt is to develop highly resistant varieties （Kuraparthy et al., 2007）. Pm21 from the wild species Haynaldia villosa （also known as Dasypyrum villosum） confers high resistance to Bgt in wheat throughout all growth stages. It has now become one of the most highly effective genetic loci introgressed into wheat from wild species, and the commercial varieties harboring Pm21 have been widely used in wheat production with more than 4 million hectares in China.