摘要 :
Purpose -To provide detailed information about the novel universal frequency-to-digital converter UFDC-1, which can help engineers and researchers to design new digital sensors and transducers, as well as smart sensors and sensor ...
展开
Purpose -To provide detailed information about the novel universal frequency-to-digital converter UFDC-1, which can help engineers and researchers to design new digital sensors and transducers, as well as smart sensors and sensor systems. Design/methodology/approach -The high performance of the UFDC-1 is achieved by using four novel measuring methods for frequency-time parameters. All existing integrated frequency-to-digital converters and frequency (time) output sensors have been reviewed and current design requirements for the UFDC-1 have been formulated. Findings -The UFDC-1 enables the transition from traditional analog (voltage and current) sensors output to frequency-time output. This yields a lot of benefits due to the properties of frequency as informative parameter. No output standardization is necessary, as opposed to the case of analog output sensors. Users can now work with the UFDC-1, the same as with traditional ADCs. Sensor manufacturers can simply integrate the UFDC-1 in microsystems and digital output sensors in order to produce serial output or bus capability. Practical implications -The UFDC-1 has many applications: obtaining a digital output from any frequency, period, duty-cycle, time interval, phase-shift, pulse number output sensors, up to one chip digital sensors design and smart (self-adaptive) sensors, thanks to its programmable relative error and non-redundant conversion time. The UFDC-1 can work with any existing frequency-time domain sensor to produce a digital output or create multiparametric smart sensors and systems. Originality/value -This paper fulfils an identified information need and offers practical help to engineers and researchers in designing new digital sensors and transducers, as well as smart sensors and systems using a minimum of hardware.
收起
摘要 :
We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular resolution in the presence of position errors. To ...
展开
We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular resolution in the presence of position errors. To achieve this goal, existing algorithms, such as subspace separation and sparse recovery, have to rely on multiple snapshots, which increases the burden of data transmission and the processing delay. Therefore, we aim to enhance angular resolution using only a single snapshot. To this end, we exploit the orthogonality of the signals of partly calibrated arrays. Particularly, we transform the signal model into a multiple-measurement model, and show that there is approximate orthogonality between the source signals in this model. We then use blind source separation to exploit the orthogonality. Simulation and experiment results both verify that our proposed algorithm achieves high angular resolution as distributed arrays without position errors, inversely proportional to the whole array aperture.
收起
摘要 :
Spectrum sensing (SS) is one of the major advanced features of the cognitive radio (CR) technology. Although multiple SS approaches have been introduced in the literature, the spectrum sensing is still a challenging operation from...
展开
Spectrum sensing (SS) is one of the major advanced features of the cognitive radio (CR) technology. Although multiple SS approaches have been introduced in the literature, the spectrum sensing is still a challenging operation from the practice perspective. Therefore, in this letter, we go one step further toward proving the efficiency of this CR functionality within a testbed that aims to emulate a near real-world high-mobile wireless communication system, using the national instrument (NI) 2954R universal software radio peripheral as a software-defined radio platform. Indeed, SS existing experimental works consider only the time-invariant channel assumption. Yet, from a practical standpoint, this assumption is no longer viable for high-mobile wireless environments for military or high-speed railway applications, where the channel nature is rather time-selective. Thus, this letter provides experiments of the SS function under time-selective multiple-input-multiple-output channels. The suggested prototype was designed with respect to saving time and development efforts. Furthermore, we show that the proper selection of the SS algorithm can enhance the communication system performances in terms of increasing receiver velocity (v(r)) and reducing sensing time while improving detection performances (P-d). For instance, we have found that the receiver can move with a five times higher v(r) value while ameliorating its detection capacity by 20%. In addition, at a fixed v(r) value, the sensing time can be 100 times shorter while achieving up to 21% of P-d increase.
收起
摘要 :
Errors in [1] are corrected below. 1. In Eq. (17), $\mathrm{vecs}(\boldsymbol{\Sigma}_{0})$ should be $\mathrm{vec}(\boldsymbol{\Sigma}_{0})$. Specifically, the correct version of Eq. (17) is: \begin{align*} \mathbf{s}_{\boldsymbo...
展开
Errors in [1] are corrected below. 1. In Eq. (17), $\mathrm{vecs}(\boldsymbol{\Sigma}_{0})$ should be $\mathrm{vec}(\boldsymbol{\Sigma}_{0})$. Specifically, the correct version of Eq. (17) is: \begin{align*} \mathbf{s}_{\boldsymbol{\phi}_{0}}\triangleq\nabla_{\boldsymbol{\phi}}\ln p_{Z}(\mathbf{z};\boldsymbol{\phi}_{0},h_{0})=[\mathbf{s}^{T}_{\boldsymbol{\mu}_{0}},\mathbf{s}^{T}_{\boldsymbol{\mu}^{*}_ {0}},\mathbf{s}^{T}_{\mathrm{vec}(\boldsymbol{\Sigma}_{0})}]^{T}.\tag{17} \end{align*} 2. In the first line after Eq. (18), $\mathbf{s}^{T}_{\mathrm{vecs}(\boldsymbol{\Sigma}_{0})}$ should be $\mathbf{s}^{T}_{\mathrm{vec}(\boldsymbol{\Sigma}_{0})}$. 3. A minus “-” is missing in front of the right-hand side of Eq. (25). The correct equation is: \begin{align*} \boldsymbol{\theta},h_{0} \right)}{\partial\theta_{i}}\right|_{\boldsymbol{\theta}=\boldsymbol{\theta}_{0}}\\ i=1,\ldots,d. \tag{40} \end{align*} \begin{align*} [\mathbf{s}_{\boldsymbol{\theta}_{0}}]_{i} i=1,\ldots,d. \tag{41} \end{align*} \begin{align*} i=1,\ldots,d. \tag{42} \end{align*}
收起
摘要 :
This paper provides a signal processing perspective on large-scale sensor networks. It focuses on two characteristics of sensor networks: application specificity and energy constraint. The former requires that network protocols be...
展开
This paper provides a signal processing perspective on large-scale sensor networks. It focuses on two characteristics of sensor networks: application specificity and energy constraint. The former requires that network protocols be designed for the application at hand, which is often signal processing in nature, and measured by application-specific metrics. The latter calls for novel distributed signal processing techniques to provide accurate and timely network state information that can be exploited by network protocols to ensure energy efficiency. The underlying theme is about how a principled integration of signal processing and networking can lead to an efficient and fair use of limited resources. The paper aims to demonstrate that capturing and exploiting dependencies between signal processing and networking offer design choices resulting in improved network performance.
收起
摘要 :
Sensor technology's impact on health care is growing rapidly. New applications are appearing almost daily. Wireless sensors are now used in an ever-growing number ways, such as monitoring glucose levels in diabetics, recording and...
展开
Sensor technology's impact on health care is growing rapidly. New applications are appearing almost daily. Wireless sensors are now used in an ever-growing number ways, such as monitoring glucose levels in diabetics, recording and tracking heart irregularities, and diagnosing infectious diseases. Linking sensors to mobile phones has made wearable sensors a reality, allowing individuals to monitor not only chronic diseases but also their lifestyle activities.
收起
摘要 :
Purpose - The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier,to improve the performance of E-nose in the detection of wound infection. When an e...
展开
Purpose - The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier,to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection,sensor array's optimization and parameters' setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach - An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (l-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings - The classification accuracy of E-nose is the highest when the weighting coefficients of the l-F method and classifier's parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications - To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications- In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value - The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.
收起
摘要 :
Reviews the underlying principles of digital signal processing (DSP) with little recourse to mathematics. Aims to encourage experimentation to obtain a feel for the processes involved. DSP provides a powerful numeric means of extr...
展开
Reviews the underlying principles of digital signal processing (DSP) with little recourse to mathematics. Aims to encourage experimentation to obtain a feel for the processes involved. DSP provides a powerful numeric means of extracting useful information from sensor data applied to a system. Discusses the basic concepts of such processing techniques and introduces some useful algorithms.
收起
摘要 :
Purpose - The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose. Design/methodology/approach - Mice are used as experimental subjects. Orthogonal signal correctio...
展开
Purpose - The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose. Design/methodology/approach - Mice are used as experimental subjects. Orthogonal signal correction (OSC) is applied to preprocess the response of Enose. Radical basis function (RBF) network is used for discrimination, and the parameters in RBF are optimized by particle swarm optimization. Findings - OSC is very suitable for eliminating interference and improving the performance of Enose in wound infection detection. Research limitations/implications - Further research is required to sample wound infection dataset of human beings and to demonstrate that the Enose with proper algorithms can be used to detect wound infection. Practical implications - In this paper, Enose is used to detect wound infection, and OSC is used to improve the performance of the Enose. This widens the application area of Enose and OSC. Originality/value - The innovative concept paves the way for the application of Enose.
收起
摘要 :
A large-scale array (LSA) inevitably encounters scenarios with a small number of samples, and its beamformer suffers from high computational complexity. High computational complexity prevents the system from being used in practica...
展开
A large-scale array (LSA) inevitably encounters scenarios with a small number of samples, and its beamformer suffers from high computational complexity. High computational complexity prevents the system from being used in practical online engineering applications. The complex vector of the beamformer weights can be expressed as the product of training snapshots and the signal steering vector (SSV), and a coefficient vector, since the optimal weight vector is a linear combination of basis vectors of the signal-plus-interference subspace. In this study, a new adaptive beamformer is developed based on the minimum variance distortionless response (MVDR) criterion and kernel techniques. The new beamformer only needs to compute the inversion of a low-dimensional Gram matrix instead of the high-dimensional sample covariance matrix (SCM), which significantly reduces the calculation cost. Moreover, an efficient loading parameter calculation method (only related to the received matrix and not required user-defined parameters) is derived, which can adaptively suppress the mismatches of the ill-conditioned Gram matrix. Furthermore, a fast version of the new beamformer is formulated for the LSA under the scanning mode. Simulation results demonstrate that the new beamformer achieves better performance and a lower computation load than existing algorithms for a small number of samples. In particular, insufficient samples and high computational complexity problems are more frequently aroused in space-time broadband array signal processing. Interestingly, the new techniques can be successfully extended to wideband array signal processing and yield satisfactory beam pattern shapes.
收起
原文获取