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In this paper, we present an analysis of low complexity signal processing algorithms capable of identifying special noises, such as the sounds of forest machinery (used for forestry, logging). Our objective is to find methods that are able to detect internal combustion engines in rural environment, and are also easy to implement on low power devices of WSNs (wireless sensor networks). In this context, we review different methods for detecting illegal logging, with an emphasis on autocorrelation and TESPAR audio techniques. The processing of extracted audio features is to be solved with limited memory and processor resources typical for low cost sensors modes. The representation of noise models is also considered with different archetypes. Implementations of the proposed methods were tested not by simulations but on sensor nodes equipped with an omnidirectional microphone and a low power microcontroller. Our results show that high recognition rate can be achieved using time domain algorithms and highly energy efficient and inexpensive architectures. [ABSTRACT FROM PUBLISHER]

This paper presents the design, development, and initial testing results of a small-bore railgun and test bench for the study of muzzle voltage traces. An analysis of the noise observed on voltage traces taken from the muzzle during motional and locked-armature operations is presented and discussed. The average velocities for a 3.38-g projectile were 50 m/s over a series of 21 tests. The railgun is powered by a sequentially fired pulse-forming network consisting of six electrolytic capacitor banks. Typical current and voltage characteristics are approximately 20-kA peak current at 275 V for a 6-ms pulse train length. Noise root-mean-square voltage and noise margins are extracted from the muzzle voltage waveforms and discussed. This investigation also develops a quantitative method to analyze muzzle voltage noise, which can be applied to any electromagnetic launcher. The technique reveals a new and interesting result that shows noise levels were greatest during early pulses of the firing sequence and generally decreased with subsequent pulses. This demonstrates that the technique has the potential to provide insight into the nature of the armature–rail sliding contact. [ABSTRACT FROM PUBLISHER]

Voltage source inverters (VSIs) supply nonsinusoidal voltages to induction motor drives, leading to line current distortion and torque pulsation. Conventional space vector pulsewidth modulation (PWM) techniques are widely used in VSIs on the account of good waveform quality and high dc bus utilization. In a conventional space vector PWM technique, the switching sequence begins with one zero state and ends with the other zero state in a subcycle. Some novel switching sequences have been proposed, which employ only one zero state but apply one of the two active states twice in a subcycle. One pair of such special switching sequences has recently been shown to reduce the pulsating torque considerably. In this paper, the conventional and special switching sequences are compared experimentally in terms of acoustic noise. In the low- and medium-speed ranges, the special switching sequence is seen to reduce the amplitude of the tonal component of noise at the switching frequency considerably and is also found to result in spread spectrum. [ABSTRACT FROM PUBLISHER]

Bearing faults of rotating machinery are observed as impulses in the vibration signal, but it is mostly immersed in noise. In order to effectively remove this noise and detect the impulses, a novel technique with morphological operators and fuzzy inference is proposed in this paper. The effectiveness of the morphological operators lies with the correct selection of structuring elements (SEs). This paper also proposes a new algorithm for this SE selection based on kurtosis, thereby making the analysis free of empirical methods. When analyzed with three different sets of faults, the results show that this method is effective and robust in bringing out the impulses. With fuzzy inference being coupled to this new technique, it makes the algorithm to be able to detect early faults also. [ABSTRACT FROM PUBLISHER]