China
Africa
Explore chapters and articles related to this topic
Introduction
Published in K. C. Raveendranathan, Neuro-Fuzzy Equalizers for Mobile Cellular Channels, 2017
The mobile cellular channel is known to be a Linear Time Variant (LTV) channel in general. It is also known that it is either a Rayleigh fading or Ricean fading channel, depending on the number of modes that reach the receiver through multiple paths. The fading characteristics will be those of a Ricean distribution, if apart from the major ray, one more component reaches the receiver (two-ray model). It will exhibit a Rayleigh distribution if three or more multipath components reach the receiver. Typically, mobile channels are severely affected by CCI due to frequency re-use and ACI, due to the leakage of spectrum (due to imperfect receiver filtering) from adjacent channels allocated within a cell. They are also affected by noise, which is normally modeled as AWGN. Several models are available for mobile cellular channels, thereby characterizing the Channel Impulse Response (CIR). The Inter-Channel Interference (ICI) and ACI contribute to reducing the output Signal-to-Noi: (SNR). Active Noise Cancellation (ANC) uses artificial signals to cancel undesired noise. A modified fuzzy adaptive filtered-X algorithm was considered by Chang (Chang and Shyu 2003). The modified fuzzy adaptive filtered-X algorithm can be applied to a mobile cellular channel in the indoor environment. Adaptive noise cancellation using Fuzzy Neural Networks has recently come into limelight again (Meng 2005). Space-Time Equalization for mobile broadband communication in an industrial indoor environment is discussed in Trautwein et al. 1999.
Noise Reduction Techniques
Published in David C. Swanson, ®, 2011
We present some very useful techniques for the cancellation of noise coherent with an available reference signal or when the noise can be modeled in a predictive way. This can also be seen as adaptive signal separation, where an undesirable signal can be removed using adaptive filtering. Adaptive noise cancellation is presented in two forms: electronic noise cancellation and active noise cancellation. The distinction is very important. For active noise cancellation, one physically cancels the unwanted signal waves in the physical medium using an actuator, rather than electrically “on the wire” after the signal has been acquired. But where does the energy go? This is a great physical question. When active cancellation occurs, the energy conversion efficiency is reduced, usually by the active system having an effect on the coupling or radiation impedance for the noise. One can’t cancel energy but one can affect the efficiency of energy conversion or propagation. Active noise cancellation has wider uses beyond sensor systems and can provide a true physical improvement to sensor dynamic range and SNR if the unwanted noise waveform is much stronger than the desired signal waveform. The use of adaptive and active noise cancellation techniques gives an intelligent sensor system the capability to counter poor signal measuring environments. However, one must also model how the SNR and signal quality may be affected by noise cancellation techniques for these countermeasures to be truly useful to an intelligent sensor system. Active noise control is mostly an academic curiosity at the time of the writing of this second edition, but its commercial limitations are primarily a cost and maintenance issue, not a feasibility concern. There are a few new techniques presented along with some very practical guidelines for active noise control.
Design and implementation of adaptive FxLMS on FPGA for online active noise cancellation
Published in Journal of the Chinese Institute of Engineers, 2018
Active Noise Cancellation (ANC) is a technique Lueg (1936) proposed. It cancels unwanted noise by adding an anti-noise signal to the primary signal. ANC is a fully adaptive system because it adjusts the parameters according to the changes in the algorithm to reduce noise (Sankar, Kumar, and Seethalakshmi 2010). The adaptive filters are useful in minimizing the error signal (Manolakis, Ingle, and Kogon 2000; Moschytz, El.-Ing, and Hofbauer 2000). Adaptive ANC algorithms process and analyze a source signal to create a signal with anti-noise characteristics. These algorithms make anti-noise signals with the same amplitude but from an opposite phase to remove the noise signals completely. Two different acoustic paths are determined in ANC algorithms. First, a reference microphone samples the primary signal. Then, the speaker sends out the anti-noise signal and an error microphone measures the residual error signal. This algorithm P(z), known as the primary path, is the transfer function of the acoustic path between the reference noise source and the error microphone. S(z), known as the secondary path, is the transfer function of the electrical and acoustic path between the speaker and the error microphone. Figure 1 shows the diagram of an acoustic ANC system. The error microphone estimates the residual error e(n), which is used to adapt the control filter coefficients W(z) (Chang and Kuo 2013; Colin 2003). The residual error signal is written as:
Acoustics and Heat Transfer Characteristics of Piezoelectric Driven Central Orifice Synthetic Jet Actuators
Published in Experimental Heat Transfer, 2022
Muhammad Ikhlaq, Muhammad Yasir, Omidreza Ghaffari, Mehmet Arik
One of the major issues with piezoelectric-driven SJAs is the noise produced by the piezoelectric diaphragm near to resonance frequency where it gives the maximum cooling performance. This noise creates an obstacle for the implementation of SJAs into practical applications. A large number of studies have shown that an ultrasonics blower can be a potential solution for this noise problem, but it has a small size and operates at the third vibrating mode of a diaphragm which compromises its performance and makes it ideal for spot cooling problems. In order to cover a larger area (e.g. a microprocessor’s surface) a thin but relatively bigger orifice size and the flow rate is necessary, which makes low and medium frequency SJAs a possible candidate for microelectronic devices [3]. Arik et al. [21], examined noise levels of low frequency (<1000 Hz) SJAs, around 73 dBA (A-weighted decibels), whereby exploiting a muffler system the self-noise of the SJAs was reduced to 30 dBA. The use of a muffler may be restricted for many modern-day applications, where design compaction is one of the main design parameters. This limitation creates a need for either a noise-free operating range for synthetic jet actuators without a muffler or for developing an active noise cancellation method. Bhapkar et al. [22] have evaluated different orifice sizes with respect to their noise levels for electromagnetic actuators, showing that reduction in orifice size can reduce SJA noise by 17%. However, this noise was associated with jet noise, not with the inherent noise of the diaphragm. Jeyalingam and Jabbal [23] have also probed the acoustics of jet actuators alone by using the electromagnetic actuation with a latex membrane. The researchers isolated the noise of the piezoelectric diaphragm and solely measured the effect of whistling generated because of flow and orifice interaction using acoustic measurements.
Technical characterisation of digital stethoscopes: towards scalable artificial intelligence-based auscultation
Published in Journal of Medical Engineering & Technology, 2023
Youness Arjoune, Trong N. Nguyen, Robin W. Doroshow, Raj Shekhar
Electronic stethoscopes were developed to address these issues [5]. The first-generation electronic stethoscopes were susceptible to ambient noise due to their chest piece being a contact microphone and having a single-tube. Furthermore, the limited device memory allowed saving only a handful of recordings. Since their early days, electronic stethoscopes have undergone several technological advances, with adaptive noise cancellation being the most significant yet only a few provide real active noise cancellation.