Signal Energy Research Articles

Short-Term Entropy of Signal Energy Used for Effective Detecting of Weak Gunshots in Noisy Environments.

Conventional gunshot detection systems can quickly and reliably detect gunshots in the area where the acoustic sensors are placed. This paper presents the detection of weak hunting gunshots using the short-term entropy of signal energy computed from acoustic signals in an open natural environment. Our research in this field was primarily aimed at detecting gunshots fired at close range with the usual acoustic intensity to protect wild elephants from poachers. The detection of weak gunshots can extend existing detection systems to detect more distant gunshots. The developed algorithm was optimized for the detection of gunshots in two categories of the surrounding sounds, short impulsive events and continuous noise, and tested in acoustic scenes where the power ratios between the weak gunshots and louder surroundings range from 0 dB to -14 dB. The overall accuracy was evaluated in terms of recall and precision. Depending on impulsive or noise sounds, binary detection was successful down to -8 dB or -6 dB; then, the efficiency decreases, but some very weak gunshots can still be detected at -13 dB. Experiments show that the proposed method has the potential to improve the efficiency and reliability of gunshot detection systems.

Machine Learning Techniques for Blind Beam Alignment in mmWave Massive MIMO.

This paper proposes methods for Machine Learning (ML)-based Beam Alignment (BA), using low-complexity ML models, and achieves a small pilot overhead. We assume a single-user massive mmWave MIMO, Uplink, using a fully analog architecture. Assuming large-dimension codebooks of possible beam patterns at UE and BS, this data-driven and model-based approach aims to partially and blindly sound a small subset of beams from these codebooks. The proposed BA is blind (no CSI), based on Received Signal Energies (RSEs), and circumvents the need for exhaustively sounding all possible beams. A sub-sampled subset of beams is then used to train several ML models such as low-rank Matrix Factorization (MF), non-negative MF (NMF), and shallow Multi-Layer Perceptron (MLP). We provide an extensive mathematical description of these models and the algorithms for each of them. Our extensive numerical results show that, by sounding only 10% of the beams from the UE and BS codebooks, the proposed ML tools are able to accurately predict the non-sounded beams through multiple transmitted power regimes. This observation holds as the codebook sizes at UE and BS vary from 128×128 to 1024×1024.

A Multidimensional Health Indicator Based on Autoregressive Power Spectral Density for Machine Condition Monitoring.

Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment's state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time-frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time-frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura-Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark.

A Wavelet Shrinkage Mixed with a Single-level 2D Discrete Wavelet Transform for Image Denoising

The single-level 2D discrete wavelet transform method is a powerful technique for effectively removing Gaussian noise from natural images. Its effectiveness is attributed to its ability to capture a signal's energy at low energy conversion values, allowing for efficient noise reduction while preserving essential image details. The wavelet noise reduction method mitigates the noise present in the waveform coefficients produced by the discrete wavelet transform. In this study, three different wavelet families—Daubechies (db7), Coiflets (coif5), and Fejér-Korovkin (fk4)—were evaluated for their noise removal capabilities using the Bayes shrink method. This approach was applied to a set of images, and the performance was analyzed using the Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. Our results demonstrated that among the wavelet families tested, the Fejér-Korovkin (fk4) wavelet consistently outperformed the others. The fk4 wavelet family yielded the lowest MSE values, indicating minimal reconstruction error, and the highest PSNR values, reflecting superior noise suppression and better image quality across all tested images. These findings suggest that the fk4 wavelet family, when combined with the Bayes shrink method, provides a robust framework for Gaussian noise reduction in natural images. The comparative analysis highlights the importance of selecting appropriate wavelet families to optimize noise reduction performance, paving the way for further research and potential improvements in image denoising techniques.

Utility-based context-aware multi-agent recommendation system for energy efficiency in residential buildings

A significant part of CO2 emissions is due to high electricity consumption in residential buildings. Using load shifting can help to improve the households’ energy efficiency. To nudge changes in energy consumption behavior, simple but powerful architectures are vital. This paper presents a novel algorithm of a recommendation system generating device usage recommendations and suggests a framework for evaluating its performance by analyzing potential energy cost savings. As a utility-based recommender system, it models user preferences depending on habitual device usage patterns, user availability, and device usage costs. As a context-aware system, it requires an external hourly electricity price signal and appliance-level energy consumption data. Due to a multi-agent architecture, it allows for easy integration of new agents, enabling seamless functionality expansion, or the disabling of existing agents to tailor the system to specific needs. Empirical results show that the system can provide energy cost savings of 18% and more for most studied households.

Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features

Predictive maintenance to avoid fatigue and failure enhances the reliability of mechanics, herewith, this paper explores vibrational time-domain data in advancing fault diagnosis of predictive maintenance. This study leveraged a belt-drive system with the properties: operating rotational speeds of 500–2000 RPM, belt pretensions at 70 and 150 N, and three operational cases of healthy, faulty and unbalanced, which leads to 12 studied cases. In this analysis, two one-axis piezoelectric accelerometers were utilized to capture vibration signals near the driver and pulley. Five advanced statistics were calculated during signal processing, namely Variance, Mean Absolute Deviation (MAD), Zero Crossing Rate (ZCR), Autocorrelation Coefficient, and the signal's Energy. The Taguchi method was used to test the five selected features on the basis of Signal-to-Noise (S/N) ratio. For classifications, an expert system was used based on artificial intelligence where a Random Forest (RF) model was trained on untraditional parameters for optimizing the accuracy. The resulted 0.990 and 0.999, accuracy and AUC, demonstrate the RF model's high dependability. Evidently, the methodology highlights the features potential when progressed into expert systems, which advances predictive maintenance strategies for belt-drive systems.

Pulse duration, frequency band, and sweep direction effects on crosstalk in wideband backscattering measurements.

Multiple broadband transducers are typically used to cover both a wide frequency range and fill in gaps resulting from sampling with multiple narrowband echosounders. Synchronized operation of these echosounders is preferred in many cases. Simultaneous operation of multiple broadband echosounders, even when using non-overlapping primary bands, can result in cross-channel interferences caused by nonlinear generation of sound and can contaminate backscattered signal. Decreasing the transmit power of channels with lower frequencies has been demonstrated as an effective technique for reducing the level of crosstalk. Reducing the transmit power inherently decreases the signal energy. Hence, the reduction in crosstalk also reduces signal-to-noise ratio and consequently observation range. Increasing the broadband pulse duration is an alternative to compensate for the reduced signal energy from lower transmit power. This paper examines the effects of increasing pulse duration on crosstalk through numerical modeling and field experiments. Raising the transmit power amplifies the higher-harmonic level more than the main band, while extending the pulse duration increases the levels of both main-band and higher harmonics the same amount. Additionally, the study explores the influence of frequency band and sweep direction on crosstalk.

Detecting wire breaks in post-tensioned tendons of wind turbines: A signal energy spectrum analysis approach

In the wind industry, recent advancements have led to the research and development of wind turbines featuring hybrid towers, a novel combination of steel tube and precast concrete segments. These concrete modules, which absorb the high stresses caused at the intersection between the tower and its foundation, are commonly joined with post-tensioned tendons. This study focuses on the identification of wire breaks in post-tensioned tendons through the application of acoustic emission testing. Measurements involve the evaluation of wire breaks under controlled laboratory conditions and the analysis of the dynamic operational environment of a functioning wind turbine. The investigation encompasses the development and assessment of a methodology based on principles of discrete Fourier transformation and Parseval’s theorem for analyzing the frequency-dependent energy distribution of acoustic signals. This analytical approach extends to the examination of wire breaks generated under varying stress levels as well as the operational noise within the actual wind turbine. The findings demonstrate a notable concentration of energy within the frequency range of 5-20 kHz in the wire break signals. In contrast, the operational noise signals show a distinct pattern, presenting an extreme value distribution within the 0-2 kHz range. The developed methodology exhibits robustness, even in the presence of high background noise levels, establishing itself as a valuable tool for wire break detection. Additionally, its potential for integration with machine learning algorithms offers the prospect of automated wire break detection, enabling further advancements in structural health monitoring for wind turbines.

Reconfigurable Intelligent Surfaces Between the Reality and Imagination

One interesting idea that could improve wireless communication systems is reconfigurable intelligent surfaces or RIS. To control the spread of wireless signals, RIS uses a plethora of tiny, inexpensive, and customizable reflecting devices. Signal strength, energy consumption, spectrum efficiency, and adaptability to changes in the wireless environment can all be improved by altering the phase shift of the reflecting elements. This is how RIS works. 5G and beyond, the IoT, and smart cities are just a few of the many potential uses for RIS. More study is required before this technology can reach its full potential since it is still in its infancy. Still, RIS is going to be big in the next generation of wireless networks. A great deal of excitement has enveloped the whole 6G development industry. There has been no shortage of grandiose and frequently unfounded assertions that, in reality, the creation of a full-duplex 5G technology that can introduce new services is just around the corner, even though self-interference cancellation schemes for full-duplex communications and the difficulties of building full-duplex base stations have long been known.

Development of a method for recognizing emotions from a speech signal

The purpose of research is automatic recognition of the speaker's emotions, based on the processing of sound recordings intended for use in alarm systems when working with operators of locomotive crews and dispatch services.Methods. Human emotion recognition has been a rapidly developing area of research in recent years. Features of the vocal tract, such as sound power, formant frequencies, are used to detect certain emotions with good accuracy. A method was used to determine the signal energy by highlighting the dominant frequency. The work has developed a program code, on the basis of which an analysis of four emotions is given - anger, joy, fear and calm. The most important and difficult step is to determine the features most suitable for distinguishing emotions and the availability of databases. Collecting databases is a complex task requiring the manifestation of sincerity of emotions. Often, the collection of a database takes place in an artificial environment and the speech may sound staged; to eliminate such problems, it is necessary to use call center recordings.Results. Recordings of basic emotional states, such as anger, joy, sadness, fear and surprise, which are the most common case of the study, were obtained and processed. The developed software code allows us to get closer to automatically determining emotions from a speech signal. To analyze speech recordings in samples, indicators of signal energy and identification of the dominant frequency were used.Conclusion. The implemented method of monitoring the emotional state of a human operator using a speech signal is widely used in the prevention and improvement of indicators of the psychophysiological professional suitability of locomotive crew workers and the preservation of their professional health. Distinct differences are observed in the characteristics of all types of emotions.

Регистрация и локализация гидроакустического источника береговым измерительным комплексом

The actual problem of the presented research is to study the peculiarities of acoustic signal transformation at the geosphere boundary in the “water-land” transition zone. The signal energy of a moving hydroacoustic radiation source is transformed in the coastal zone into seismoacoustic oscillations recorded by a coastal two-coordinate laser deformograph. The spatial position of the measuring components of the laser deformograph makes it possible to localize the displacement of an object in the nearby water area based on the study of variations in the amplitude of the recorded signal. The methodology of the experiment based on the movement of the transmitter with constant speed and distance from the shore measurement system is described. As a result of the experiment, a stable registration of the seismoacoustic signal was obtained along the entire trajectory of the transmitter with a decrease in the amplitude of the received signal when it is at an angle close to 45 degrees. According to the results of the analysis of variations in the amplitude of the registered signal on differently directed components of the laser deformograph, the results of direction finding of the source of low-frequency hydroacoustic radiation were obtained, with the error in determining the direction to the hydroacoustic source ranging from 0.2 % to 10.5 %.

Bolt looseness monitoring using dynamic mode decomposition with piezoelectric active sensing

Bolt joints are widely used in multiple industries due to its ease of operation and low-cost. Under the influence of multiple external factors, bolt joint will loosen, which poses risks to structural safety. In this paper, a bolt loosening monitoring approach is proposed based on piezoelectric active sensing and dynamic mode decomposition (DMD). To the best of the author’s knowledge, this research represents the first attempt to detect bolt looseness using DMD method. A high-frequency dynamic response signal of the bolt joint is obtained based on piezoelectric active sensing. DMD is used to decompose the high-frequency dynamic response signal into a series of dynamic mode components. The bolt looseness mainly affects the localized high-frequency and high-order dynamic characteristics of the bolted structure. This paper innovatively proposes that the high frequency component signal constructed by the higher order dynamic component more directly reflect the localized dynamic characteristics of the bolted joint. Then, the signal energy of the constructed high frequency component signal is developed into a health index (HI) to characterize the bolt loosening degree. The experiments were conducted to verify the effectiveness of the proposed method. The experiment results shows that the proposed HI monotonically increases as the tightening of the bolt over the whole range of bolt-preload. Furthermore, the research incorporates different sensor mounting positions and loosening of bolts with various diameters, demonstrating the applicability of the proposed method. The proposed method is significant in detecting loosening of bolt joints, especially in bolt early loosening, and enhances the performance of the piezoelectric active sensing method.

A Wear Debris Signal Processing Method Based on Inductive Monitoring for Aero-Engine

In view of the high false alarm rate in the oil debris monitoring results of the triple-coil inductive sensor in the transmission lubrication system of the aero-engine, a new debris signal processing method based on inductive monitoring is proposed. A time domain analysis is carried out first, and the signal energy is the most effective index to distinguish the debris signature from the noise signature. On this basis, signal energy values within a fixed-length sliding window is processed through the histogram. Finally, a threshold is set for the detection of the debris signature, which is based on the distribution of data within the histogram. This method is applied to the experimental data from a test run of an aero-engine, and the results show that all the debris is detected even if part of it appears during a change in the working condition of the aero-engine. Therefore, this method shows satisfactory results in debris detection accuracy and especially the inhibition of false alarms. It is also applicable for real-time monitoring due to the similarity between the movement of the sliding window and real-time data acquisition. In addition, it is applicable for various sensing principles, including but not limited to the inductive sensor signal, since the detection performance is only related to the signal itself.

A multimodal dataset for investigating working memory in presence of music: a pilot study.

Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli. Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session. A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session. Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.

Experimental Study on the Effect of Environmental Water on the Mechanical Properties and Deterioration Process of Underground Engineering Masonry Mortar

Urban underground engineering is generally buried at a shallow depth and suffers long-term environmental water effects such as rainfall, rivers, underground pipeline leakage, and groundwater. The mechanical properties of the structures are affected by constant deterioration, which seriously hinders the safe, healthy, and sustainable development of the city. On the basis of on-site investigation of civil defense engineering, this article simulates the water environment conditions of mortar in underground engineering in the laboratory and conducts manual sample preparation in the laboratory. Then, water, H2CO3, NaCl, and Na2CO3 solution or wet–dry cycles are used to corrode the sample, respectively. A uniaxial compression test, Brazilian splitting test, analyses of the acoustic emission signals and electromagnetic signals, and magnetic imaging testing are performed, respectively. The results show that an increase in the action time of environmental water leads to a gradual increase in the uniaxial compressive strength, tensile strength, and elastic modulus of cement mortar, but it will decrease over a long period of time. Different environmental water components can also lead to a different performance of soaked mortar. The uniaxial compressive strength R, tensile strength σt, and elastic modulus E of mortar samples exhibit values in different solutions in the order of H2CO3 solution < NaCl solution < Na2CO3 solution < water. A moderate solution soak time can enhance the mechanical properties of the mortar, but this effect decreases at long time scales. The effect of wet–dry cycles on the mechanical properties and degradation process of mortar is significant. With the increase in wet–dry cycles, the porosity of mortar continuously increases. The cumulative ringing count, energy, amplitude, and impact number of acoustic emission signals always increase when the samples are loaded to failure. The uniaxial compressive strength, tensile strength, and elastic modulus first increase and then decrease. The experimental results lay the foundation for further investigating the performance changes in mortar under complex water environments in underground engineering.

Cooperative control of variable damping error port Hamiltonian and backstepping nonsingular terminal sliding mode control for manipulators driven by PMSMs

AbstractTo improve the disadvantages of signal control and energy control, a cooperative control strategy combining signal control and energy control is proposed for manipulators driven by permanent magnet synchronous motors (PMSMs) in this article. The cooperative control strategy is achieved by way of the convex combination of signal controller and energy controller, and Gaussian function is selected as cooperative function. The energy controller applies variable damping error port Hamiltonian and the signal controller uses backstepping nonsingular terminal sliding mode control. The PMSM with loss model is applied to obtain higher efficiency. To deal with modeling errors and joint friction, a nonlinear disturbance observer is used for manipulators system. Simulation and experiment results display that the designed strategy not only increases efficiency, but also enhances dynamic and steady‐state performance of manipulators.

Influence of atmospheric wind on the propagation of radio waves

Background. It is necessary to study the influence of the physical characteristics of the atmosphere, in particular, wind on atmospheric turbulence and, consequently, on the characteristics of the radio signal it is shown. Aim. The dependence of the time-spectral function of the radio signal energy flux on the wind speed in the troposphere in the antenna plane is found. Methods. A method of transition from the Cartesian coordinate system in the antenna plane to the polar coordinate system of wave numbers has been developed. Based on this method the relationship between the Fourier spectral function of the correlation moment and the representation of the Bessel function is found. For the Fourier spectral function of the correlation moment, the previously obtained solution of the differential equation for the fluctuations of the eikonal amplitude of electromagnetic wave in a turbulent atmosphere at the front of the electromagnetic wave at the coordinate of the receiving antenna is used. Using the inverse Fourier transform the relationship between the time-spectral function of the radio signal energy flux and the time-correlation function of this flux is found. Results. Based on the study of the time-correlation function of the radio signal energy flow its relationship with the two-point correlation moment characterizing the fluctuations of the eikonal amplitude of the radio signal is found. To analyze the effect of wind, a turbulence model was used, reflecting the inertial turbulence interval, in which the energy flow from larger turbulent vortices to smaller vortices is determined by the viscous dissipation of the smallest vortices. Conclusion. Numerical calculations have shown that such a wind in the antenna plane blows away turbulent vortices in this plane, bettering the quality of the received radio signal.

Diagnostic imaging of debonding in FRP-strengthened reinforced concrete structures using combinational harmonics generated by Rayleigh waves

A debonding location imaging technique (DLIT) utilising mixed-frequency Rayleigh waves is reported in this paper to predict the location of debonding in fibre-reinforced polymer (FRP) composite strengthened concrete structures. In the proposed methodology, a piezoceramic transducer network is employed to sequentially scan the FRP-concrete interface. One of the transducers serves as an actuator while the remainder serve as sensors. The incident waves which are sent by the actuator consist of two different fundamental frequencies. After the incident waves interact with debonding, additional signals are generated that coincide with the sum of the fundamental frequencies (i.e., sum frequency). The received wave component at the sum frequency is in accordance with theoretical predictions. In order to apply the DLIT, a time-frequency analysis is then applied to process the data collected by each actuator-sensor transducer pair in order to determine the signal energy density amplitudes (i.e., signal envelopes) at the fundamental frequencies and the sum frequency. Based on cross-correlation analysis between the signal envelopes of the fundamental frequencies and the sum frequency, an image can be constructed that reveals the centroid of the debonding region. Following the presentation of the proposed DLIT, numerical and experimental studies are then reported that compare the predicted centroid of debonding with the actual centroid of debonding as incorporated into the numerical model and the experimental specimens, respectively. The results demonstrate that the proposed DLIT based on the Rayleigh wave features at sum frequency can satisfactorily predict the location of debonding.

Impact time domain decomposition: An adaptive decomposition method for multi-source impact signals based on envelope energy gradient characteristics

Reciprocating mechanical vibration signals are characterised by multi-source impact coupling and varied time-domain intervals. Different sub-impacts contain rich information closely related to the operational states during different strokes of the working cycle. Therefore, accurate and efficient decomposition of different sub-impacts in the time domain is an important aspect of state monitoring and fault diagnosis in reciprocating machinery. Existing time-domain decomposition methods that primarily focus on the concentration of signal energy lack robustness against noise. Furthermore, these methods suffer from issues, such as excessive reliance on prior parameters, low computational efficiency, and signal distortion. In this study, impact time domain decomposition (ITDD), a novel adaptive decomposition method is introduced. ITDD uses the envelope energy gradient (EEG) characteristics of a signal to extract sub-impacts. An impact-adaptive localisation strategy is designed based on the EEG characteristics of the noisy signals. A dimensionless evaluation metric called the impact distortion index is then constructed to optimise the smoothed EEG sequence. Finally, a residual amplitude estimation method for adaptively setting the impact identification thresholds is proposed. The effectiveness and superiority of the ITDD over existing time-domain decomposition algorithms are demonstrated using simulations and actual analyses of reciprocating machinery signals. These results indicate that the ITDD can precisely and efficiently decompose multi-source impact signals, and it significantly enhances decomposition precision and speed compared to existing methods, with a decomposition time < 1 s, thus demonstrating substantial potential for engineering applications.

Ultrasound Biomarkers: Contrast-enhanced Ultrasound and Nakagami Imaging to Differentiate Benign and Malignant Choroidal Tumor

Purpose We hypothesized that contrast-enhanced ultrasound (CEUS) using a microbubble technique to quantify microvascular changes and Nakagami imaging for tissue characterization would provide a new approach for diagnosing and differentiating benign and malignant choroidal lesions. Methods Five patients with choroidal melanoma (CM) and five patients with choroidal hemangioma (CH) were selected. Definity®, which contains perflutren microbubbles, was administered as a slow IV bolus (1 ml). CEUS was performed for 1 min postinjection of the contrast agent with ultrasound radiofrequency data acquired from 10 s to 60 s. The contrast value was calculated for the whole tumor region. A gradient magnitude method was used for each postcontrast frames with 1-second interval, and the time-averaged value in pixel intensity gradient of postinjection frames was estimated and reported. Based on the Nakagami statistical distribution model, two Nakagami parameters, m and Ω, where m (shape parameter), representing tissue heterogeneity, and Ω (scale parameter), representing the average energy of backscattered signals, were studied. Results CEUS analysis showed that the time-averaged estimated contrast was significantly higher (p = 0.008) for CH compared to CM. Furthermore, the time-averaged contrast within the normal choroidal region was significantly higher than the choroidal tumor region for both CH and CM (p = 0.001 for CH cases and p < 0.0001 for CM cases). Nakagami analysis showed that the m estimates were significantly higher (p = 0.032) for CH (m = 0.61) than for CM (m = 0.28), indicating that CH is a more heterogeneous tumor than CM. The Ω estimates were significantly higher (p = 0.0019) for CH (Ω = 0.15) compared to CM (Ω = 0.03). These results may be due to the more vascular structures in CH compared to CM. Conclusions Quantitative intensity-based perfusion analysis using CEUS and backscattering tissue analysis using Nakagami imaging can provide valuable insights to differentiate benign and malignant choroidal lesions.