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The need to provide efficient public transport services in urban areas has led to the implementation of bus priority measures in many congested cities. Much interest has recently centred on priority at signal controlled junctions, including the concept of pre-signals, where traffic signals are installed at or near the end of a with-flow bus lane to provide buses with priority access to the downstream junction. Although a number of pre-signals have now been installed in the UK, particularly in London, there has been very little published research into the analysis of benefits and disbenefits to both buses and non-priority vehicles at pre-signalised intersections. This paper addresses these points through the development of analytical procedures which allow pre-implementation evaluation of specific categories of pre-signals.

In today’s digital world, real-time health monitoring is becoming a most important challenge in the field of medical research. Body signals such as electrocardiogram (ECG), electromyogram and electroencephalogram (EEG) are produced in human body. This continuous monitoring generates huge count of data and thus an efficient method is required to shrink the size of the obtained large data. Compressed sensing (CS) is one of the techniques used to compress the data size. This technique is most used in certain applications, where the size of data is huge or the data acquisition process is too expensive to gather data from vast count of samples at Nyquist rate. This paper aims to propose Lion Mutated Crow search Algorithm (LM-CSA), to improve the performance of the LMCSA model.

A new CS algorithm is exploited in this paper, where the compression process undergoes three stages: designing of stable measurement matrix, signal compression and signal reconstruction. Here, the compression process falls under certain working principle, and is as follows: signal transformation, computation of Θ and normalization. As the main contribution, the theta value evaluation is proceeded by a new “Enhanced bi-orthogonal wavelet filter.” The enhancement is given under the scaling coefficients, where they are optimally tuned for processing the compression. However, the way of tuning seems to be the great crisis, and hence this work seeks the strategy of meta-heuristic algorithms. Moreover, a new hybrid algorithm is introduced that solves the above mentioned optimization inconsistency. The proposed algorithm is named as “Lion Mutated Crow search Algorithm (LM-CSA),” which is the hybridization of crow search algorithm (CSA) and lion algorithm (LA) to enhance the performance of the LM-CSA model.

Finally, the proposed LM-CSA model is compared over the traditional models in terms of certain error measures such as mean error percentage (MEP), symmetric mean absolute percentage error (SMAPE), mean absolute scaled error, mean absolute error (MAE), root mean square error, L1-norm and L2-normand infinity-norm. For ECG analysis, under bior 3.1, LM-CSA is 56.6, 62.5 and 81.5% better than bi-orthogonal wavelet in terms of MEP, SMAPE and MAE, respectively. Under bior 3.7 for ECG analysis, LM-CSA is 0.15% better than genetic algorithm (GA), 0.10% superior to particle search optimization (PSO), 0.22% superior to firefly (FF), 0.22% superior to CSA and 0.14% superior to LA, respectively, in terms of L1-norm. Further, for EEG analysis, LM-CSA is 86.9 and 91.2% better than the traditional bi-orthogonal wavelet under bior 3.1. Under bior 3.3, LM-CSA is 91.7 and 73.12% better than the bi-orthogonal wavelet in terms of MAE and MEP, respectively. Under bior 3.5 for EEG, L1-norm of LM-CSA is 0.64% superior to GA, 0.43% superior to PSO, 0.62% superior to FF, 0.84% superior to CSA and 0.60% better than LA, respectively.

This paper presents a novel CS framework using LM-CSA algorithm for EEG and ECG signal compression. To the best of the authors’ knowledge, this is the first work to use LM-CSA with enhanced bi-orthogonal wavelet filter for enhancing the CS capability as well reducing the errors.

This paper aims to conceptualize corporate reference management as a strategic signaling activity in business networks. While research has extensively outlined how firms develop and maintain social capital through business-to-business (B2B) relationships, less is known about how they signal their participation in business networks to develop this social capital. Therefore, this paper conceptualizes B2B references, in particular corporate online references (COR), as a tool through which firms “borrow” attractiveness from their business network. Through the lens of structural social capital theory, COR is shown to capture advantages related to interconnectedness between firms.

The paper reports on a two-step qualitative and quantitative research design. First, the authors undertook a qualitative study that reports on the COR practices of senior business managers. A quantitative study then uses social network analysis (SNA) to audit a digital business network comprising 1,098 firms in a metropolitan area of the UK, referencing to each other through their corporate websites using COR.

The analyses find that COR practices contribute to building structural social capital in networks through strategic signaling. Firms do so by managing B2B references to craft strategic signals, using five steps: requesting, granting, curating, coding and decoding references. While the existing literature on business marketing portrays reference management as a routine and operational management practice, this investigation conceptualizes reference management, in particular COR, as a strategic activity.

To the best of the authors’ knowledge, this is the first study to use SNA to represent B2B references in the form of COR as a network, which overlaps with (but is not entirely identical to) the business network. Further, the study re-conceptualizes reference management as a strategic signaling activity that leverages the firm’s participation in business networks to build structural social capital by borrowing attractiveness of prestigious business partners that leverages existing structural social capital. Finally, the paper coins and conceptualizes COR as an exemplar of referencing management and offers propositions for further research.

This paper proposes a design of an efficient and automated experimental platform for frequency modulated continuous wave (FMCW) radars. The platform can quickly flexibly generate the waveform that meets measurement requirements and significantly improve experimental efficiency.

This platform not only includes radio frequency devices but also integrates a programmable transmitter based on field programmable gate array. By configuring the waveform data, the experimental platform can generate waveforms with adjustable parameters and realize automatic emission, reception and processing of signals. Different from traditional fast Fourier transform, this paper uses a discrete-time Fourier transform to process low-frequency signals to get more accurate results.

The authors demonstrate the effectiveness of the platform through a single-path cable experiment, an indoor ranging experiment by using different modulating waveforms and a speed measurement experiment. With complete functions and strong flexibility, the platform can operate effectively in various conditions and greatly improve the efficiency of research and study.

The platform can accelerate the research studies and applications of FMCW radars in the fields of automatic drive, through-wall detection and health-care applications.

Cost and functionality are taken into account in the platform, which can significantly improve the efficiency of research. The proposed signal processing method improves the accuracy while its computation complexity does not increase significantly.

Despite the prevalent use of professional status and service feedback in online healthcare markets, the potential interaction relationship between two types of information is still unknown. This study used the signaling theory to examine the substitute relationship between professional status and service feedback in patients' doctor choice, as well as the moderating effect of illness severity.

To test the paper's hypotheses, we constructed a panel data model using 418 doctors' data collected over a period of six months from an online healthcare market in China. Then, according to the results of the Hausman test, we estimated a fixed-effects model of patients' choice in online healthcare markets.

The empirical results showed that the effect of a doctor's professional status and service feedback on a patient's doctor choice was substitutable. Moreover, patients' illness severity played a moderating role, in that the influence of professional status on a patient with high-severity illness was higher than that on a patient with low-severity illness, whereas the influence of service feedback on a patient with low-severity illness was higher than that of a patient with high-severity illness. In addition, we found that illness severity negatively moderated the substitute relationship between professional status and service feedback on a patient's choice.

These findings not only contribute to signaling theory and research on online healthcare markets, but also help us understand the importance of professional status and service feedback on a patient's choice when seeking a doctor online.

The purpose of this paper is to suggest new method for improving the condition indicators (CIs) used in health and usage monitoring system based on signal separation of gears.

The research method is based on employing signal separation techniques to improve gears signal and fault signature. The signal separation is based on adaptive filters concept.

CIs estimated for the deterministic part of vibration signal show higher sensitivity to gears faults in comparison to indicators estimated based on the original signal. This method proposed could enhance early fault detection in gears, particularly for those applications where strong background noise from other sources in the machine masks the characteristics fault components.

The contribution of this research is improving the CIs currently used for helicopter gearboxes. As consequence the safe operation and availability will be improved.

The purpose of this paper is to detect the periodic signal under strong noise background, and estimate its amplitude/phase.

Melnikov method is adopted as calculating the threshold value when chaos occurs, and the detected signal is taken as a system parameter. The system's output state is changed if the parameter has a slight change near the threshold. Meantime, the phase of system's output is recognized to judge whether the output state changes, and the signal parameter is estimated according to the necessary condition.

A small periodic signal in noise can be detected by Duffing oscillator via a transition from chaotic motion to periodic motion.

The paper shows how to calculate the amplitude/phase in low signal‐to‐noise ratios.

The Duffing system is sensitive to the weak periodic signal and has definite immunity to noise, so it is easy to construct a system composed of many oscillators that could process complex signals, even though the environmental noise is intense.

This paper presents a nonlinear method for detecting and extracting the weak signal.

The purpose of this paper is to present a compressed sensing (CS)-based sampling system for ultra-wide-band (UWB) signal. By exploiting the sparsity of signal, this new sampling system can sub-Nyquist sample a multiband UWB signal, whose unknown frequency support occupies only a small portion of a wide spectrum.

A random Rademacher sequence is used to sense the signal in the frequency domain, and a matrix constructed by Hadamard basis is used to compress the signal. The probability of reconstruction is proved mathematically, and the reconstruction matrix is developed in the frequency domain.

Simulation results indicate that, with an ultra-low sampling rate, the proposed system can capture and reconstruct sparse multiband UWB signals with high probability. For sparse multiband UWB signals, the proposed system has potential to break through the Shannon theorem.

Different from the traditional sub-Nyquist techniques, the proposed sampling system not only breaks through the limitation of Shannon theorem but also avoids the barrier of input bandwidth of analog-to-digital converters (ADCs).

This paper aims to explore a new way to extract the fault feature of a rolling bearing signal on the basis of a combinatorial method.

By combining local mean decomposition (LMD) with Teager energy operator, a new feature-extraction method of a rolling bearing fault signal was proposed, called the LMD–Teager transform method. The principles and steps of method are presented, and the physical meaning of the time–frequency power spectrum and marginal spectrum is discussed. On the basis of comparison with the fast Fourier transform method, a simulated non-stationary signal was processed to verify the effect of the new method. Meanwhile, an analysis was conducted by using the recorded vibration signals which include inner race, out race and bearing ball fault signal.

The results show that the proposed method is more suitable for the non-stationary fault signal because the LMD–Teager transform method breaks through the difficulty of the Fourier transform method that can process only the stationary signal. The new method can extract more useful information and can provide better analysis accuracy and resolution compared with the traditional Fourier method.

Combining the advantage of the local mean decomposition and the Teager energy operator, the LMD–Teager method suits the nature of the fault signal. A marginal spectrum obtained from the LMD–Teager method minimizes the estimation bias brought about by the non-stationarity of the fault signal. So, the LMD–Teager transform has better analysis accuracy and resolution than the traditional Fourier method, which provides a good alternative for fault diagnosis of the rolling bearing.

In real working condition, signal is highly disturbed and even drowned by noise, which extremely interferes in detecting results. Therefore, this paper aims to provide an effective de-noising method for the debris particle in lubricant so that the ultrasonic technique can be applied to the online debris particle detection.

For completing the online ultrasonic monitoring of oil wear debris, the research is made on some selected wear debris signals. It applies morphology component analysis (MCA) theory to de-noise signals. To overcome the potential weakness of MCA threshold process, it proposes fuzzy morphology component analysis (FMCA) by fuzzy threshold function.

According to simulated and experimental results, it eliminates most of the wear debris signal noises by using FMCA through the signal comparison. According to the comparison of simulation evaluation index, it has highest signal noise ratio, smallest root mean square error and largest similarity factor.

The rapid movement of the debris particles, as well as the lubricant temperature, may influence the measuring signals. Researchers are encouraged to solve these problems further.

This paper includes implications for the improvement in the online debris detection and the development of the ultrasonic technique applied in online debris detection.

This paper provides a promising way of applying the MCA theory to de-noise signals. To avoid the potential weakness of the MCA threshold process, it proposes FMCA through fuzzy threshold function. The FMCA method has great obvious advantage in de-noising wear debris signals. It lays the foundation for online ultrasonic monitoring of lubrication wear debris.