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Probability estimation measures the likelihood of different outcomes in a statistical context. It commonly involves estimating either the parameters or the entire distribution of a random variable. Parametric approaches, where a specific functional form is assumed for data distribution, have been used in various fields, particularly in computational statistics for modeling and simulating physical phenomena. However, non-parametric methods have gained prominence, especially in machine learning and signal processing. These methods are focused on estimating or modeling probability density functions without relying on predefined parametric forms. It becomes crucial when faced with unknown or complex distributions, especially if parametric assumptions do not hold. This paper deals with the non-parametric method based on the Parzen window for probability density function estimation, a versatile approach applicable to univariate and multivariate data. Having a sufficient amount of data, this method provides reliable estimates, while at the same time, it is quite suitable for implementation. Considering the advantages of hardware implementations compared to software solutions, this paper introduces analog-digital hardware for the Parzen approach. The proposed solution avoids the need for sorting operations, which are typically challenging to implement in hardware. The simulation is performed using PSpice software (OrCad version 22.1) showing that the required processing time is under 420 ns.

In modern industries, pipelines play a crucial role, both as an essential element in energy transportation (water, gas and electricity) and also in the distribution of these resources. The large size of piping infrastructures, their age and unpredictable external factors are the main difficulties in monitoring the piping system. In this context, the detection and the localization of leaks are challenging but essential, as leaks lead to substantial economic losses. Current methods have many limitations, involving invasive procedures, working only with short pipes or requiring a system shutdown. This paper presents a non-intrusive method based on acoustic signal processing. Leak detection is performed using matched filters, while localization is performed based on the phase diagram representation method and diagram-based entropy computation. Our continuous monitoring system was used for two months and a full comparison with the video inspection-based technique was conducted. The results indicate that this method has a high accuracy, regardless of the length of the pipe.

Signal modulation identification is of high interest for applications in military communications, but is not limited only to this specific field. Some possible applications are related to spectrum surveillance, electronic warfare, quality services, and cognitive radio. Distinguishing between multi-carrier signals, such as orthogonal frequency division multiplexing (OFDM) signals, and single-carrier signals is very important in several applications. Conventional methods face a stalemate in which the classification accuracy process is limited, and, therefore, new descriptors are needed to complement the existing methods. Another drawback is that some features cannot be extracted using conventional feature extraction techniques in practical OFDM systems. This paper introduces a new signal detection algorithm based on the phase diagram characterization. First, the proposed algorithm is described and implemented for simulated signals in MATLAB. Second, the algorithm performance is verified in an experimental scenario by using long-term evolution OFDM signals over a software-defined radio (SDR) frequency testbed. Our findings suggest that the algorithm provides good detection performance in realistic noisy environments.

Wireless communication technologies are undergoing intensive study and are experiencing accelerated progress which leads to a large increase in the number of end-users. Because of this, the radio spectrum has become more crowded than ever. These previously mentioned aspects lead to the urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency. Specifically, modulation scheme recognition occupies a crucial position in the civil and military application, especially with the emergence of Software Defined Radio (SDR). The modulation recognition is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Spread spectrum (SS) techniques represent the foundation for the design of Cognitive Radio systems. In this work, we propose a new method of characterization of Spread spectrum modulations capable of providing relevant information for the process of recognition of this type of modulations. Using the proposed approach, results higher than 90% are obtained in the modulation classification process, thus bringing an advantage over the classical methods, whose performance is below 75%.

The influence of finite-length registers and the corresponding quantization effects on the reconstruction of sparse and approximately sparse signals from a reduced set of measurements is analyzed in this paper. For the nonquantized measurements, the compressive sensing (CS) framework provides highly accurate reconstruction algorithms that produce negligible errors when the reconstruction conditions are met. However, hardware implementations of signal processing algorithms inevitably involve finite-length registers and quantization of the measurements. A detailed analysis of the effects related to the measurement quantization, with an arbitrary number of bits, is provided in this paper. A unified novel mathematical model to characterize the influence of the quantization noise and the signal nonsparsity on the CS reconstruction is introduced. Using this model, an exact formula for the expected error energy in the CS-based reconstructed signal is derived, while in the literature its bounds have been reported only. The theory is validated through various numerical examples with quantized measurements, involving scenarios with approximately sparse signals, noise folding effect, and floating-point arithmetics.

We present a signal decomposition procedure, which separates modes into individual components while preserving their integrity, in effort to tackle the challenges related to the characterization of modes in an acoustic dispersive environment. With this approach, each mode can be analyzed and processed individually, which carries opportunities for new insights into their characterization possibilities. The proposed methodology is based on the eigenanalysis of the autocorrelation matrix of the analyzed signal. When eigenvectors of this matrix are properly linearly combined, each signal component can be separately reconstructed. A proper linear combination is determined based on the minimization of concentration measures calculated exploiting time-frequency representations. In this paper, we engage a steepest-descent-like algorithm for the minimization process. Numerical results support the theory and indicate the applicability of the proposed methodology in the decomposition of acoustic signals in dispersive channels.

The difficulties of predictive maintenance of power grids are related to the large spread of electrical infrastructures and the definition of early warning indicators. Such indicator is the partial discharge activities—which can be very informative about the rising insulation problems of electrical materials. However, the detection and the localization of the partial discharges are very complicate tasks and are currently subject to intensive studies in both theoretical and practical domains. The traditional way to approach the global surveillance of partial discharge sources is to first detect it and the second is to attempt to localize their positions. Despite the numerous proposed approaches, based on advanced transient signal processing tools, there is no any operational technique to efficiently asses the partial discharge sources in a real power network. In this context, our paper proposes a new approach based on the global evaluation of entropy of transient phenomena detected in a power network, without needing any localization of the sources of these phenomena. We will show that this approach provides an effective evaluation of partial discharges sources. Moreover, since the technique requires a reduced number of sensors, it is very advantageous to use in real contexts.

In this study, we present the implementation of a neural network model capable of classifying radio frequency identification (RFID) tags based on their electromagnetic (EM) signature for authentication applications. One important application of the chipless RFID addresses the counterfeiting threat for manufacturers. The goal is to design and implement chipless RFID tags that possess a unique and unclonable fingerprint to authenticate objects. As EM characteristics are employed, these fingerprints cannot be easily spoofed. A set of 18 tags operating in V band (65–72 GHz) was designed and measured. V band is more sensitive to dimensional variations compared to other applications at lower frequencies, thus it is suitable to highlight the differences between the EM signatures. Machine learning (ML) approaches are used to characterize and classify the 18 EM responses in order to validate the authentication method. The proposed supervised method reached a maximum recognition rate of 100%, surpassing in terms of accuracy most of RFID fingerprinting related work. To determine the best network configuration, we used a random search algorithm. Further tuning was conducted by comparing the results of different learning algorithms in terms of accuracy and loss.

In the last years, the commercial drone/unmanned aerial vehicles market has grown due to their technological performances (provided by the multiple onboard available sensors), low price, and ease of use. Being very attractive for an increasing number of applications, their presence represents a major issue for public or classified areas with a special status, because of the rising number of incidents. Our paper proposes a new approach for the drone movement detection and characterization based on the ultra-wide band (UWB) sensing system and advanced signal processing methods. This approach characterizes the movement of the drone using classical methods such as correlation, envelope detection, time-scale analysis, but also a new method, the recurrence plot analysis. The obtained results are compared in terms of movement map accuracy and required computation time in order to offer a future starting point for the drone intrusion detection.

Monitoring highly dynamic environments is a difficult task when the changes within the systems require high speed monitoring systems. An active sensing system has to solve the problem of overlapped responses coming from different parts of the surveyed environment. Thus, the need of a new representation space which separates the overlapped responses, is mandatory. This paper describes two new concepts for high speed active sensing systems. On the emitter side, we propose a phase-space-based waveform design that presents a unique shape in the phase space, which can be easily converted into a real signal. We call it phase space lobe. The instantaneous frequency (IF) law of the emitted signal is found inside the time series. The main advantage of this new concept is its capability to generate several distinct signals, non-orthogonal in the time/frequency domain but orthogonal within the representation space, namely the phase diagram. On the receiver side, the IF law information is estimated in the phase diagram representation domain by quantifying the recurrent states of the system. This waveform design technique gives the possibility to develop the high speed sensing methods, adapted for monitoring complex dynamic phenomena In our paper, as an applicative context, we consider the problem of estimating the time of flight in an dynamic acoustic environment. In this context, we show through experimental trials that our approach provides three times more accurate estimation of time of flight than spectrogram based technique. This very good accuracy comes from the capability of our approach to generate separable IF law components as well as from the quantification in phase diagram, both of them being the key element of our approach for high speed sensing.

Chirp-rate, as a second derivative of signal phase, is an important feature of nonstationary signals in numerous applications such as radar, sonar, and communications. In this paper, an adaptive algorithm for the chirp-rate estimation is proposed. It is based on the confidence intervals rule and the cubic-phase function. The window width is adaptively selected to achieve good tradeoff between bias and variance of the chirp-rate estimate. The proposed algorithm is verified by simulations and the results show that it outperforms the standard algorithm with fixed window width.

The detection and localization of transient signals is nowadays a typical point of interest when we consider the multitude of existing transient sources, such as electrical and mechanical systems, underwater environments, audio domain, seismic data, and so forth. In such fields, transients carry out a lot of information. They can correspond to a large amount of phenomena issued from the studied problem and important to analyze (anomalies and perturbations, natural sources, environmental singularities, …). They usually occur randomly as brief and sudden signals, such as partial discharges in electrical cables and transformers tanks. Therefore, motivated by advanced and accurate analysis, efficient tools of transients detection and localization are of great utility. Higher order statistics, wavelets and spectrogram distributions are well known methods which proved their efficiency to detect and localize transients independently to one another. However, in the case of a signal composed by several transients physically related and with important energy gap between them, the tools previously mentioned could not detect efficiently all the transients of the whole signal. Recently, the generalized complex time distribution concept has been introduced. This distribution offers access to highly concentrated representation of any phase derivative order of a signal. In this paper, we use this improved phase analysis tool to define a new concept to detect and localize dependant transients taking regard to the phase break they cause and not their amplitude. ROC curves are calculated to analyze and compare the performances of the proposed methods.