This study proposes the design and implementation details of cascade predictive current- and speed-loop controllers for a dual-permanent magnet synchronous motor (PMSM) drive system. The systematic design procedures for the proposed cascade predictive controllers are presented here. By using the proposed predictive controllers, the dual-PMSM drive system can attain superior performance, including good transient responses, good load disturbance responses, good tracking responses, and good robustness. To enhance the output torque of the dual-PMSM drive system, a maximum torque/ampere control method is proposed. In addition, a detailed analysis of the parameter variations of the drive system is discussed. A digital signal processor, a TMS 320-F-2808, manufactured by the Texas Instruments, is used as a control centre that executes the control algorithms. As a result, the hardware circuit is simple. Several experimental results reveal that the proposed predictive controllers perform better than proportional-integral (PI) controllers in transient responses and steady-state characteristics. The proposed predictive controllers for dual-PMSM drive systems, which are also investigated in this study, can be installed in electric vehicles and electric ships.

One of the adverse effects of unbalanced three-phase voltages on the induction motors (IMs) is overheating of the windings. The IMs should be loaded at less than their rated power in case of unbalanced supply voltages to prevent this overheating. Recent studies have pointed out that the maximum allowable loading ratio or derating factor (DF) of the IMs have various values for several combinations of the magnitude and angle of the complex voltage unbalance factor (CVUF) operating with a combination of unbalanced over- and under-voltage cases. This means that determination of DF requires plenty of experimental efforts for all possible unbalanced voltage conditions. In this study, the effective root-mean-square voltage definition which is defined in IEEE Standard 1459 is combined with the CVUF for proper identification of over and under unbalanced voltage conditions. An algorithm based on response surface methodology is proposed to estimate a precise DF for a broad range of unbalanced supply voltages. The simulation results are presented to validate the effectiveness of the proposed algorithm. It is apparently figured out that the proposed algorithm has better accuracy compared with the conventional approach reported in National Equipment Manufacturer's Association Standard MG1.

Singing voice recognition is very different from speech recognition or automatic speech recognition because there are distinct differences between speaking and singing voices. The problem is complex because music audio signals with their background instrumental accompaniments are regarded as noise sources that degrade the performance of the recognition system. This study proposes a statistical learning method to recognise words in a vocal audio signal with background music and to classify the region of a singing voice in a polyphonic audio signal. The goal of this study is to solve the problem of recognising words from sung input without using any method to separate instrumental from the background. This study also applies a concept from image recognition by using a spectrogram feature as an image to solve the problem. An audio signal with accompanying music was analysed and transformed into a spectrogram feature. To recognise it, the entire spectrogram feature was sliced, forming a feature vector for a feed-forward neural network classifier. Several classification functions were compared, including K-Nearest Neighbour, Fisher Linear Classifier, Linear Bayes Normal Classifier, Naive Bayes Classifier, Parzen Classifier and Decision Tree. The results show that using a feed-forward neural network can effectively recognise sung words at an accuracy rate of more than 93.0%. In particular, this system can recognise cross-language music data.

The 60 GHz pulse is more practical for the indoor localisation system due to its better time resolution. Usually, time-of-arrival (TOA) estimation with higher accuracy is critical to the indoor localisation. To acquire precise TOA estimations, a novel threshold crossing technique using neural network (NN) is discussed via analysing the characteristics of the received pulses based on the energy detection receiver. The relationship between the optimum thresholds and the signal-to-noise ratios (SNRs) are researched. Meanwhile, the influences caused by changing integration periods and channel models are examined. Results show that the proposed NN method can provide better accuracy and robustness to the lower SNR in the models designed by TG 3c.

This present study reports a new type of inductor-free simplified Chua's circuit only using two-op-amps-based realisation, which is implemented by cascading a Sallen–Key low-pass filter with a simplified Chua's diode. With mathematical model-based numerical simulations and hardware circuit-based experimental measurements, it is demonstrated that the reported circuit exhibits the similar dynamical behaviours of the classical Chua's circuit such as point attractor, limit cycles, period doubling route, chaotic spiral attractors, double-scroll chaotic attractor, boundary crisis, coexisting bifurcation modes, and so on. Furthermore, the proposed circuit enriches the realisation forms of Chua's circuit family.

The implication of multilevel fast multipole algorithm (MLFMM) is applied to the analysis of large antenna array of the dual-polarised log-periodic dipole antenna in the presence of dielectric half-space and arranged in a random array configuration for square kilometre array (SKA) radio telescope. The MLFMM is applied to accelerate the matrix–vector product operations. The findings are compared with the results obtained by standard method-of-moment solver. Numerical results are given to demonstrate the accuracy and capability of this algorithm.

A fully-integrated complementary metal–oxide semiconductor sensor using NPN transistors for temperature measurement is presented. The main purpose of the device is to monitor the temperature for implanted integrated circuits and to ensure the safe operation by monitoring the temperature on-chip. This study adopted a behavioural simulator that uses LTSPICE for a temperature sensor for performing the rapid and accurate simulation. It is advantageous in 50 nm technique to design the circuits. The integrated sensor circuit consists of a bias circuit, an operational amplifier, and a thermal module. A high sensitivity of 5.9 mV/°C in the output voltage circuit has been measured, the linear dependence of voltage drop across the bipolar junction transistor on temperature, in a range from −30 to 125°C, has been used for thermal sensing.

An implementation technique for traffic splitting using a meter table of a standard OpenFlow in a software-defined network is proposed. The proposed technique uses different differentiated services code point (DSCP) numbers, which are generally used to classify network traffic for quality of service (QoS) levels, in a packet header to decide an output port for a corresponding route. The meter table is generally designed for a QoS purpose. The OpenFlow standard provides two types of meter band, ‘drop’ and ‘dscp_remark’. The proposed technique adopts the ‘dscp_remark’ type for a splitting traffic purpose, instead of the QoS purpose. The DSCP number of the packet is modified when the packet rate exceeds a pre-defined traffic rate. Packets with different DSCP numbers are sent out to the neighbour switches from their corresponding output ports. The original DSCP number is returned to the packets when they are sending out to the neighbour switches, so that the QoS is not affected.