The output of an N-point FFT (Fast Fourier Transform) is zero at odd positions if its inputs at odd positions with N/2 distance are equal and this property is exploited in order to reduce the power consumed by the FFT circuitry when operating on sparse data. An OFDM (Orthogonal Frequency Division Multiplexing) transceiver with such a configurable FFT is described where undersampling is applied in order to reduce the ADC (Analog Digital Conversion) power and the buffering requirements by up to 25% or even 37.5% if additional FFT properties are exploited. As will be shown in this paper up to 3/8 of the FFT butterfly operations can be deactivated in this case without any effect on the error caused by the undersampling procedure. Deactivating a higher number of FFT operations is also possible with a small error overhead. The reconstruction of the sparse images captured by a surveillance camera as well as X-ray images is also used as case studies demonstrating the use of the proposed method in image transfer applications over OFDM infrastructures. The Normalized Mean Square Error (NMSE) in these case studies is as low as 0.01 if 12.5% of the FFT input samples are omitted by the undersampling process.

The monitoring of the power level during nuclear reactors operation is done by instruments which measure the neutron flux. The calibration of this power is carried out by thermal means. Its accurate knowledge is critical to the fuel burnup calculations and mainly to electrical power. Nuclear fuel burning is proportional to the thermal power released by the reactor and is extremely relevant for the calculation of uranium-235 mass burned profile knowledge of fission products the value of the radioactive decay heat the activity and radiotoxicity. The objective of this paper is to present two methods for real time monitoring of nuclear reactor power using thermal processes. These methods have been developed and experimentally validated in the TRIGA nuclear research reactor at Nuclear Technology Development Center – CDTN (Belo Horizonte Brazil) and are the focus of the paper described here. One uses the temperature difference between an instrumented fuel element and the pool water cooling below the reactor core. This method is innovative and the most reliable way of on-line power monitoring of the CDTN TRIGA reactor. For this methodology a patent was deposited at the Brazilian Institute of Industrial Property (INPI) in 2012. The other method consists in real-time monitoring of the steady-state energy balance of the primary and secondary reactor cooling loops. This process is now the standard methodology for power calibration used in the CDTN TRIGA reactor. It also presents the uncertainty analysis for these thermal power measure procedures.

The “Internet-of-Things" (IoT) paradigm has been extensively focused in the last years since it covers a wide scope of applications that range from spam detection in e-mail datasets to on-body sensors. However this data flooding also known as “big data" phenomenon has required high levels of security since private and important data are now surfing over our heads. In this paper we introduced a recently developed pattern recognition technique called Optimum-Path Forest (OPF) for the task of data mining in IoT-oriented applications such as spam detection in e-mail- and web-content as well as intrusion detection in computer networks. An extensive comparison against fourteen classification techniques can drive us a picture about the effectiveness and efficiency of OPF classifier in that context.

Mostly pure tone fault of loudspeakers in the world is detected by human hearing. Obviously the accuracy could not be guaranteed due to subjectivity and it is easy to cause auditory fatigue. Based on the characteristics of loudspeaker’s pure tone detection we propose that the response signal of frequency sweep can be converted into two-dimension timefrequency image signal to enhance the characteristics of fault information through wavelet packet transform. Then time-frequency images are pretreated into contours by binarization and edge extraction. The boxcounting dimensions of time-frequency image by image fractal method is proposed and regarded as the fault characteristics for loudspeaker detection. Through the verification of on-line experiments in workshop the fractal dimension which regarded as complexity of the time-frequency image contours can be the feature for failure determination and the fault identification accuracy rate can reach 95%. It fully meets the requirements of loudspeakers fault detection on-line and better than other recent patents.

The THREAD Act (Transportation Recall Enhancement Accountability and Documentation) mandated the use of a suitable tyre pressure monitoring system (TPMS) technology in all light motor vehicles under 5 tons. In the United States as of 2008 and the European Union as of November 1 2012 all new passenger car models released must be equipped with a TPMS. This would alert drivers of under-inflation events. Many countries followed the adoption of TPMS into vehicles. The existing systems depend on pressure sensors strapped on the rim of the tyre. These sensors read the pressure information inside the tyre and transmit it to the receiver in the car. Some systems depend on wheel speed data from the wheel speed sensor. A difference in wheel speed would trigger an alarm based on the algorithm implemented. This paper proposes a new method to monitor tyre pressure by utilising the machine learning approach. Vertical vibrations are extracted from a wheel hub of a moving vehicle using an accelerometer and are classified using machine learning techniques. Statistical features are used to represent the data in the signal. The logistic model tree (LMT) was used as the classifier and attained an accuracy of 92.5% with 10 fold cross validation and 98.5 when tested.

The scientific and accurate prediction for state of bearing is the key to ensure its safe operation. A rotating bearing monitoring system was presented in U.S. Patent 7606673 and a bearing condition monitoring apparatus was presented in U.S. Patent 8229682 however the system or apparatus lacks state prediction function of bearing. State prediction of bearing based on relevance vector regression algorithm with RBF kernel is proposed in this paper. Kurtosis of bearing vibration signal can excellently reflect the state of bearing so the future state of bearing can be excellently reflected by predicting the kurtosis of bearing vibration signal. Thus kurtosis prediction of bearing vibration signal based on relevance vector regression algorithm with RBF kernel is studied. Finally the experiments are adopted to demonstrate the feasibility of the proposed method for state prediction of bearing.

Biometric systems use automated methods for pattern recognition in determining the authenticity of a specific physiological or behavioral characteristic of an individual to determine or verify identity. In this work 2D and 3D palmprints are integrated in order to construct an efficient multi-biometric identification system based on matching score level fusion. In this paper we try to evaluate the usefulness of the 2D and 3D palmprints for improving the palmprint based person identification systems. For that purpose we propose several systems of exploiting the palmprint modalities. In this study the Gabor filter response and the wavelet transform are used for representing the palmprint traits. First the 2D palmprint is filtered by the Gabor filter. The real and imaginary responses of the filtered image are used to create three different vectors. Second the 2D palmprint is transformed into several sub-bands using wavelet transform. After that some sub-bands are used to create several vectors. Subsequently the Radial Basis Function (RBF) is used for modeling and classifying the feature vectors. The results of some classifiers are combined using matching score level fusion strategy. The proposed system was tested and evaluated for its efficacy on the available PolyU 2D and 3D palmprint database of 300 persons. The obtained experimental results show that the system yields the best performance for identifying palmprint and it is able to provide the highest degree of security.