This work proposes a new and efficient sparse representation-based spatial compression of video signals integrated with a hyper-chaotic DNA coding-based encryption module that provides higher security. The betterment of compression efficiency is achieved by introducing sparse coding on the video frames, and higher security is obtained by applying 5D hyper-chaotic DNA coding on the sparse coded frames

Empirical wavelet transform (EWT) can successfully decompose a smooth or noise-free simulated signal into several components. This method has encountered difficulties in processing simulated signals containing noise and in applications. In order to improve the shortcomings of EWT and expand its application, this paper proposes a variable spectral segmentation EWT (VEWT) associated with the trend of

In this paper, a novel approach is presented to estimate the metric scale (MSC) and roll and pitch angles of a platform by using distance sensors in a monocular visual odometry setup. A state-of-the-art visual odometry algorithm Semi-Direct Visual Odometry (SVO) [1] is used to obtain sparse three dimensional (3D) point cloud which is then matched with the measurements obtained from the distance sensors

Smart wearable devices have become more and more popular in our daily life due to their unique power of “wearing-while-using.” However, requirements of light weight and compact size lead to limited on-device resources in most wearable products, which hamper the development of wearable technology. Edge computing provides an opportunity for wearable devices to access more resources without violating

Light Field Image (LFI) can simultaneously record the intensity and direction information of light rays, and provide users with strong immersion experience. However, heterogeneous artifacts may be introduced during LFI processing, which results in degradation of the perceptual quality of LFI. To evaluate the LFI quality effectively, a novel blind light field image quality assessment method by analyzing

Flexible organic photodetectors that are lightweight, conformable, cost-effective, and compatible with large-scale processing techniques will meet the demands of next-generation wearable devices in the era of Internet of Things (IoT). Recent advances in various flexible organic photodetectors and their accompanied signal processing circuits have greatly enriched the application scenarios of wearable

Chemical reactions and diffusion are two basic mechanisms governing the dynamics of molecules in a fluid. As such, they play a critical role in molecular communication for channel modeling, design of detection rules, implementation of molecular circuits for computation, and modeling interactions with external biochemical systems. For finite numbers of information-carrying molecules, stochastic models

A bilinear interpolation technique is proposed for flexible representations of quantum images (FRQIs). In this process, several quantum modules were developed, including assignment, increment, and quarter modules, for use in an interpolation circuit. The network structure of these circuits, capable of both up-sampling and down-sampling FRQIs, was investigated as part of the study. Additionally, the

This paper considers the waveform design for colocated MIMO radar to improve target detectability embedded in signal-dependent interferences, and maximize the SINR (signal-to-interference-plus-noise ratio). Due to constant modulus constraint, the problem is indeed non-convex and NP-hard. To address the challenge of this intractable problem, we propose a novel optimization framework for solving the

The design of filter banks, which is a frequently used operator for many practical applications, has been widely reported in the literature. The requirements of using extensive mathematical manipulations for finding computationally efficient structures of filter banks have imposed severe difficulties for those who lack sufficient mathematical background. This paper is to report procedures of deriving

This paper studies the problem of deciding on the absence (i.e., null hypothesis, H0) or presence (i.e., alternative hypothesis, H1) of an unknown signal embedded in the received signal in a multiple-input, multiple-output (MIMO) receiver, employing 1-bit quantization. The originality of our solution lies in quantizing the received signal by an adapted 1-bit window comparator, rather than a traditional

Facial expression recognition has become an increasingly important research topic in pattern recognition and affective computing. Most of facial expression recognition methods assume that the training and testing data come from the same corpus. However, in practical situations, this assumption does not hold, in which the data are often collected from different scenarios, e.g., different races, environments

Semi-continuous data comes from a distribution that is a mixture of the point mass at zero and a continuous distribution with support on the positive real line. Such data appear in several real-life situations like blind signal separation problems, modeling of loss data in finance and insurance, sales of consumer goods, daily precipitation data, to name a few. In this paper, we present a novel algorithm

As an indispensable part of array signal processing, direction-of-arrival (DOA) estimation has been well investigated over the past few decades, and many excellent DOA estimation methods have been proposed. In this paper, a receiving domain covariance vector (RxCV) based unitary SBL algorithm is proposed for the off-grid DOA estimation of monostatic multiple-input multiple-output (MIMO) radar in unknown

Cancellable biometrics is the art of generating distorted or encrypted templates of original biometric templates. The evolution of cancellable biometrics is attributed to the advanced hacking technologies that can capture the original stored biometrics from databases. One of the solutions for this problem is to store cancellable biometric templates in the database rather than the original ones. This

Interest is growing in the application of the spatial location information from weather scenario to the monitoring of meteorological signal for airborne weather radar system. Here, a new algorithm utilizes the differences between ground clutter and meteorological signal in terms of the spatial location in elevation dimension to achieve weather signal detection (WSD). Specifically, weather signal submerged

Our efforts towards developing an automatic speaker verification (ASV) system for child speakers are presented in this paper. For the majority of the languages, children's speech data for training the ASV system is either unavailable (zero-resource) or very limited (low-resource). Under low- and zero-resource conditions, developing an ASV system becomes a very challenging problem. To overcome this

Traditional phase retrieval methods for sparse signals are not able to achieve optimal performance when dealing with special-structured signals such as block-sparse signals. In this paper, we focus on the phase retrieval for block k-sparse signal x∈CN, with uniform block size d. Taking advantage of the block-sparse property, an algorithm, called block compressed phase retrieval via lifting (BCPRL)

Advances in machine learning technology have enabled real-time extraction of semantic information in signals which can revolutionize signal processing techniques and improve their performance significantly for the next generation of applications. With the objective of a concrete representation and efficient processing of the semantic information, we propose and demonstrate a formal graph-based semantic

In this paper, we discuss the state of radio frequency (RF) coexistence and how it benefits from dynamic spectrum sensing. We categorize coexistence of radar and communications in terms of their cooperation and non-cooperation in using the spectrum as well as their roles as primary, secondary, and dual users of the frequency bands. We describe the ways the radar can alter its parameters in response

Sequences with low autocorrelation sidelobes has applications in various fields like wireless communications, radar, sonar, cryptography to name a few. In this paper, we propose an approach to construct sequences, in particular unimodular sequences, by directly minimizing the peak sidelobe level (PSL) metric. The underlying optimization problem involved is a minimax problem which is in general difficult

The Hilbert transform of mono- and multicomponent periodically non-stationary random signals (PNRSs), the carrier harmonics of which are narrow-band low-frequency modulated, are considered here. The relations for the auto- and cross-covariance and cross-spectral functions of a PNRS and its Hilbert transform are analyzed and the conditions for stationarity of the analytical signal are obtained. The

The distribution of aircraft dynamic radar cross section (RCS) varies at different course short-cuts and heights and can behave as a bimodal shape. Classical models, such as the Weibull distribution, the chi-square distribution, and lognormal distribution, fit poorly with bimodal RCS distributions and are unable to accurately describe the statistical characteristics of aircraft dynamic RCS. In order

Since functional Magnetic Resonance Imaging (fMRI) signals are a group of sparse signals, and its autocorrelation matrix contains limited information, it is difficult to accurately locate the brain activation area directly using traditional blind separation algorithms. For the issue, this paper proposes a method with inverse-sparse transform and second order blind identification (SOBI) for the separation

The robust watermarking method embeds the information in the image to protect the copyright. The embedding process will destroy the original image and cannot be effectively applied to the images used in the medicine and military with high integrity demand. A robust zero-watermarking algorithm for lossless copyright protection of color images without changing the original image was proposed in this

A two-stage technique for estimating hyperbolic frequency modulated (HFM) signal parameters is proposed. In the first stage, coarse parameter estimations are performed through a regression of the group delay estimate obtained from an unwrapped spectrum phase due to its robustness to noise. In the second stage, the coarse estimates are refined by a combination of the Newton-Raphson strategy and O'Shea

The time-frequency analysis of nonstationary multicomponent linear frequency modulation (LFM) signals is still a challenging task due to their cross-term interferences. In this paper, a novel signal-dependent adaptive compact kernel based time-frequency distribution (CKD) named regional adaptive CKD (RACKD) is proposed. The proposed RACKD firstly represents the signal in the Doppler-lag (ν, τ) ambiguity

In recent years, wearable technology is interwoven with our everyday lives because of its commoditization and comfort. Security and privacy become a big concern as many user-sensitive data have been stored in such devices, such as personal emails and bank accounts. Traditional user authentication techniques like PIN entry are unfriendly and vulnerable to shoulder surfing attacks. To address these problems

Recently it has been mathematically proved that any digital signal can be expressed as a string 13 3-point motifs and information in the signal is encoded in terms of those motifs. This result has several important applications in statistical signal processing, such as entropy estimation, transfer entropy, data mining, etc. In the current work we have shown an application in estimation of mutual information

Deep learning has been actively utilized for speech enhancement. However, deep learning-based speech enhancement usually produces over-smoothed speech, resulting in speech distortion and degraded intelligibility. In this paper, we propose the exaggeration of the training target so that the dynamic range of the enhanced speech becomes more similar to that of the clean speech. Target exaggeration can

Vehicular safety communications (VSCs) provide relevant solutions to avoid congestion and road accidents, more particularly at road intersections since these areas are more prone to accidents. In this context, one of the main impairments that can affect the performance of VSCs is interference. In this paper, we study the performance of cooperative transmissions at intersections in presence of interference

Vector quantized variational autoencoder (VQ-VAE) has recently become an increasingly popular method in non-parallel zero-shot voice conversion (VC). The reason behind is that VQ-VAE is capable of disentangling the content and the speaker representations from the speech by using a content encoder and a speaker encoder, which is suitable for the VC task that makes the speech of a source speaker sound

Single image deraining is one of challenges in image processing. An efficient algorithm for single image deraining can significantly improve the image quality in severe weather conditions. Existing deraining algorithms only pay attention to spatial characteristics or channel information, which leads to poor performance of the network. In this paper, we propose a novel dual attention residual group

In signal processing, ARMA processes are widely used to model short-memory processes. In various applications, comparing or classifying ARMA processes is required. In this paper, our purpose is to provide analytical expressions of the divergence rates of the Kullback-Leibler divergence, the Rényi divergence (RD) of order α and their symmetric versions for two Gaussian ARMA processes, by taking advantage

The detrended fluctuation analysis (DFA) and its higher-order variant make it possible to estimate the Hurst exponent and therefore to quantify the long-range dependence of a random process. These methods are popular and used in a wide range of applications where they have been proven to be discriminative to characterize or classify processes. Nevertheless, in practice, the signal may be short-memory

Wearable technologies provide a non-invasive way to monitor user's activity, identity, and health in real-time, which have attracted tremendous interests from both academia and industry. Due to constraints in form factor and power consumption, the sensing capabilities and functionalities of the wearables are usually limited by the available sensors. In the past decade, researchers have committed to

Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been widely used as powerful tools for numerical inference and optimization. They require the choice of a suitable proposal density that is crucial for their performance. For this reason, several adaptive importance sampling (AIS)

Long-time coherent integration (LTCI) is one of the most important techniques to improve radar detection performance of weak targets. In the existing LTCI methods, polynomial models with finite terms are used to formulate signal model. However, for the targets moving with constant Cartesian velocities (CCV), infinite polynomial terms are required to precisely describe target range evolution. The model

Electric wheelchairs, as mobile auxiliary equipment, improve the quality of life and self-independence of the disabled. The brain-computer interface (BCI) is used to establish a direct connection between the brain and the wheelchair, which adopts the electroencephalogram (EEG) signal to control the wheelchair. Compared with other types of BCI, the steady-state visual evoked potentials (SSVEP)-BCI is

Wearable devices have dramatically developed over the past decade as their functions extended from the simple posture analysis to non-invasive condition monitoring for early warning and proactive healthcare, which are especially significant for the dangerous disease such as cardiac arrhythmia. However, it is difficult for the wearable devices to collect plentiful and high-quality training samples so

In this paper, we propose a novel differentiable activation function for convolutional neural networks. This function is proposed using an orthogonal Legendre polynomial based linear-in-parameter model. First, we present mathematical modeling for max pooling operation in convolutional neural networks and derive the relationship between max pooling and average pooling. The representation of max pooling

Independent component analysis (ICA) is increasingly utilized to modern digital signal processing. Complex-valued FastICA, a fast fixed-point algorithm for ICA, is one of the most non-trivial algorithms for solving the ICA problems in the complex domain. Hitherto, there have been several attempts to give performance analysis for complex-valued FastICA. Rigorous theoretical analysis, however, still

This paper investigates a countermeasure model between a multistatic multiple-input multiple-output (MIMO) radar network and multiple targets in the presence of continuous surface clutter. As a member of the multistatic MIMO radar network, the main purpose of each radar is to minimize its transmit power under a certain target detection criterion. Based on the selfishness of each radar, a strategic

The complex least mean square (CLMS) adaptive algorithm based on the minimum mean square error (MSE) criterion has been widely used for linear applications with Gaussian noises in complex domain. However, the MSE criterion suffers from performance degeneration in the presence of non-Gaussian noise. To address this issue, a novel complex kernel risk-sensitive p-power loss (CKRSP-L) criterion is first

The Stockwell transform (ST), which is an invertible time-frequency spectral localization technique, uniquely combines elements of wavelet transform and short-time Fourier transform. However, its signal analysis ability is restricted in the time-frequency plane. In this paper, the novel fractional Stockwell transform (FRST) is proposed to address this problem, based on the ST and fractional Fourier

In modern radar detection systems, constant false alarm rate (CFAR) control is a key technique for automatic target detection in clutter level unknown environments. The maximum likelihood criterion is often used to design the CFAR detector for which it has the best detection performance with the expected probability of false alarm. However, the heterogeneous environments, viz., multiple-target scenario

This study proposes a novel blind watermarking algorithm based on radial harmonic Fourier moments (RHFMs) as well as discrete wavelet transform and discrete cosine transform (DWT-DCT) to achieve copyright protection. A non-adjacent coupled map lattice chaotic system was used to improve the safety in the watermark embedding process. First, a zero-watermark was obtained by performing DCT on the original

Blind identification of channel codes is becoming increasingly important in signal interception and intelligent communication systems. However, most existing channel codes recognition algorithms extract features manually, which makes them highly demanding in real-world application. Thus, efficiently identifying channel codes is difficult using present technologies. This paper presents a deep residual

Despite the advantage of power efficiency and spectral efficiency, spatial modulation (SM) technology makes communication systems sensitive to the transmission impairment. This paper analytically investigates the performance of SM multiple-input multiple-output (MIMO) systems where power amplifier (PA) shows clear nonlinearity. Firstly, the system model is presented with nonlinear distortion included

The compressive sensing theory enables reconstruction of sparse or compressible signals at reduced sampling rate. Recent studies have shown that stable signal reconstruction is possible even if each measurement is quantized to one bit. In conventional compressive sensing framework, the signal can be sparsely represented by some discrete atoms. In many applications however, signals are sparse in a continuous

Face image recognition technology plays an important role in biometric recognition field. Among the present face recognition methods, the method based on subspace learning have aroused wide concern due to their favorable properties, such as convenience for computation and effectiveness for identification. However, existing methods based on subspace learning are out of work when the sample-specific

In this paper, a new transmitter-receiver structure for high speed train (HST) data transmission and detection in fifth generation (5G) communication systems is proposed. This structure employs a new Doppler shift frequency (DSF) estimation approach, projected in this paper, to calculate the channel taps' DSFs at the base station (BS) receiver. The estimated DSFs are used to calculate the channel taps'

Orthogonal time frequency space (OTFS) is a recently introduced modulation technique that multiplexes information symbols in the delay-Doppler (DD) domain rather than the time frequency (TF) domain as in traditional multi-carrier modulation such as orthogonal frequency division multiplexing (OFDM). It has been shown that OTFS outperforms OFDM in a high Doppler environment. OTFS can be utilized in the

The particle filter (PF) has excellent estimation performance for nonlinear non-Gaussian systems. However, this method misleads the results due to sample impoverishment and the sensitivity on the initial values of state and process noise variance. To overcome this, an intelligent PF method with a resampling of multi-population cooperation is presented in this paper. Firstly, an intelligent resampling

Radar detection in environments where the power and variability of interference hinders the location of targets is of great importance. Many efforts are directed towards the development of adaptive techniques that process the eco-signals in order to increase the detection quality and to maintain a constant false alarm rate (CFAR). In this context was conceived the radar detection in the moments space

Binary pattern family is considered as a powerful tool for visual texture classification. Most popular methods improve the classification performance by multi-feature fusion. However, many sub-features are redundant and low-discriminative and the classification system has high computational complexity and unsatisfactory results. To handle above problems, this paper proposes a compact multi-pattern

In this paper, we propose a novel method for dual-function radar-communication(DFRC) system. Our focus is on radar being used for communications in parallel to conventional target tracking and/or surveillance assignment. In the proposed setup, the main beam is directed toward the targets as primary radar function, whereas, communication is performed through the sidelobes as secondary function. In addition