Range Compression Research Articles

Self-Attention conditional generative adversarial network optimised with crayfish optimization algorithm for improving cyber security in cloud computing

The decentralized and distributed architecture of cloud computing promotes adoption and growth in various societal domains, including education, government, information technology, business, entertainment. Cloud computing (CC) makes a broad range of information technologies available. Security and privacy are key challenges in storing big data in the cloud. To overcome this challenge, a Self-Attention Conditional Generative Adversarial Network Optimised with Crayfish Optimization Algorithm for Improving Cyber Security in Cloud Computing (CybS-CC-SACGANCOA) is proposed in this paper to enhance the safety of CC environment. The input data is amassed from NSL-KDD database. After that, the data is fed to pre-processing segment. The segment of pre-processing eliminates Cloud data termination and missing value replacement using Reformed Phase Conserving Vibrant Range Compression filter. The results of pre-processing serves for feature selection. The optimal features are selected by means of Manta Ray Foraging Optimization Algorithm (MRFOA). Cloud data is categorized as normal and abnormal data, like Denial-of-Service (DoS), Probe, Remote to local attack (R2L), User to root attack (U2R) by the help of SACGAN. Crayfish Optimization Algorithm (COA) is proposed to optimize the SACGAN classifier that classifies anomaly data precisely. The proposed CSCC-SACGANCOA technique is activated in Python under some metrics. The proposed CSCC-SACGANCOA approach has attained higher detection accuracy, lower computation time, higher AUC, higher scalability and lower detection error rate compared to the existing methods.

Optimized self-attention based cycle-consistent generative adversarial network adopted melanoma classification from dermoscopic images.

Skin is the exposed part of the human body that constantly protected from UV rays, heat, light, dust, and other hazardous radiation. One of the most dangerous illnesses that affect people is skin cancer. A type of skin cancer called melanoma starts in the melanocytes, which regulate the colour in human skin. Reducing the fatality rate from skin cancer requires early detection and diagnosis of conditions like melanoma. In this article, a Self-attention based cycle-consistent generative adversarial network optimized with Archerfish Hunting Optimization Algorithm adopted Melanoma Classification (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Primarily, the input Skin dermoscopic images are gathered via the dataset of ISIC 2019. Then, the input Skin dermoscopic images is pre-processed using adjusted quick shift phase preserving dynamic range compression (AQSP-DRC) for removing noise and increase the quality of Skin dermoscopic images. These pre-processed images are fed to the piecewise fuzzy C-means clustering (PF-CMC) for ROI region segmentation. The segmented ROI region is supplied to the Hexadecimal Local Adaptive Binary Pattern (HLABP) to extract the Radiomic features, like Grayscale statistic features (standard deviation, mean, kurtosis, and skewness) together with Haralick Texture features (contrast, energy, entropy, homogeneity, and inverse different moments). The extracted features are fed to self-attention based cycle-consistent generative adversarial network (SACCGAN) which classifies the skin cancers as Melanocytic nevus, Basal cell carcinoma, Actinic Keratosis, Benign keratosis, Dermatofibroma, Vascular lesion, Squamous cell carcinoma and melanoma. In general, SACCGAN not adapt any optimization modes to define the ideal parameters to assure accurate classification of skin cancer. Hence, Archerfish Hunting Optimization Algorithm (AHOA) is considered to maximize the SACCGAN classifier, which categorizes the skin cancer accurately. The proposed method attains 23.01%, 14.96%, and 45.31% higher accuracy and 32.16%, 11.32%, and 24.56% lesser computational time evaluated to the existing methods, like melanoma prediction method for unbalanced data utilizing optimized Squeeze Net through bald eagle search optimization (CNN-BES-MC-DI), hyper-parameter optimized CNN depending on Grey wolf optimization algorithm (CNN-GWOA-MC-DI), DEANN incited skin cancer finding depending on fuzzy c-means clustering (DEANN-MC-DI). RESEARCH HIGHLIGHTS: This manuscript, self-attention based cycle-consistent. SACCGAN-AHOA-MC-DI method is implemented in Python. (SACCGAN-AHOA-MC-DI) from dermoscopic images is proposed. Adjusted quick shift phase preserving dynamic range compression (AQSP-DRC). Removing noise and increase the quality of Skin dermoscopic images.

Ultraviolet pulse compression via cross-phase modulation in a hollow-core fiber

The generation of few-femtosecond pulses with high energy and tunable spectrum in the ultraviolet region is an ongoing challenge in ultrafast optics. Harnessing the cross-phase modulation between an intense near-infrared pulse and its third-harmonic, co-propagating in a gas-filled hollow-core fiber, we demonstrate spectral tuning, broadening, and temporal compression in the ultraviolet range. Ultraviolet pulses with negative chirp, leading to self-compression down to 6 fs during propagation in air, and energy >10µJ were characterized. This technique opens a way towards the realization of few-fs pulses with high energy, tunable over most of the ultraviolet range.

The study on the properties of a metamaterial composed of star-shaped auxetic and curved-beam multi-stable structure

This article presents the design of a new metamaterial with adjustable Poisson’s ratio and multi-stability, which combines two common metamaterial structures: star-shaped auxetic and multi-stability. The range of negative Poisson’s ratio variation for the unit structure is 0.2433. The negative Poisson’s ratio shows an increasing trend followed by a decrease with the increase in compression displacement. The peak compression displacement is 12 mm, and the peak negative Poisson’s ratio is −0.1499. Simultaneously, the structure exhibits good shape memory properties, with a shape memory recovery ratio of R rx = 94.18% in the y-axis direction and R ry = 91.20% in the x-axis direction. The shape memory recovery time is 35.10 s. The combination of experimental testing and finite element analysis was employed to verify the functionality of the adjustable Poisson’s ratio in the structure. Special attention was given to investigating the structural parameters such as the type of curved-beam, the angle of the star-shaped structure, the width of the star-shaped structure, and the connection method for their effects on the shape memory effect and the range, peak value, and peak compression displacement of the negative Poisson’s ratio in the unit structure. Based on the characteristics of the novel metamaterial, including a significant increase in stiffness upon compression and controllable deformation sequence, it can be used as a buffering device in protective engineering and civil engineering applications.

The Effects of Signal to Noise Ratio, T60 , Wide-Dynamic Range Compression Speed, and Digital Noise Reduction in a Virtual Restaurant Setting.

Hearing aid processing in realistic listening environments is difficult to study effectively. Often the environment is unpredictable or unknown, such as in wearable aid trials with subjective report by the wearer. Some laboratory experiments create listening environments to exert tight experimental control, but those environments are often limited by physical space, a small number of sound sources, or room absorptive properties. Simulation techniques bridge this gap by providing greater experimental control over listening environments, effectively bringing aspects of the real-world into the laboratory. This project used simulation to study the effects of wide-dynamic range compression (WDRC) and digital noise reduction (DNR) on speech intelligibility in a reverberant environment with six spatialized competing talkers. The primary objective of this study was to determine the efficacy of WDRC and DNR in a complex listening environment using virtual auditory space techniques. Participants of greatest interest were listeners with hearing impairment. A group of listeners with clinically normal hearing was included to assess the effects of the simulation absent the complex effects of hearing loss. Virtual auditory space techniques were used to simulate a small restaurant listening environment with two different reverberation times (0.8 and 1.8 sec) in a range of signal to noise ratios (SNRs) (-8.5 to 11.5 dB SNR). Six spatialized competing talkers were included to further enhance realism. A hearing aid simulation was used to examine the degree to which speech intelligibility was affected by slow and fast WDRC in conjunction with the presence or absence of DNR. The WDRC and DNR settings were chosen to be reasonable estimates of hearing aids currently available to consumers. A WDRC × DNR × Hearing Status interaction was observed, such that DNR was beneficial for speech intelligibility when combined with fast WDRC speeds, but DNR was detrimental to speech intelligibility when WDRC speeds were slow. The pattern of the WDRC × DNR interaction was observed for both listener groups. Significant main effects of reverberation time and SNR were observed, indicating better performance with lower reverberation times and more positive SNR. DNR reduced low-amplitude noise before WDRC-amplified the low-intensity portions of the signal, negating one potential downside of fast WDRC and leading to an improvement in speech intelligibility in this simulation. These data suggest that, in some real-world environments that include both reverberation and noise, older listeners with hearing impairment may find speech to be more intelligible if DNR is activated when the hearing aid has fast compression time constants. Additional research is needed to determine the appropriate DNR strength and to confirm results in wearable hearing aids and a wider range of listening environments.

Effect of hydrogen on thermal deformation behavior and microstructure evolution of MoNbHfZrTi refractory high-entropy alloy

The study examined the thermal-deformation behavior of an MoNbHfZrTi refractory high-entropy alloy (RHEA), which were hydrogenated using a liquid hydrogenation method and then subjected to high-temperature uniaxial compression in the deformation temperature range of 1100–1250 °C and strain rate range of 0.5–10−3 s−1. Test results showed that: (1) When the deformation temperature was between 1100 and 1250 °C, hydrogen addition led to increased high-temperature peak stress of the alloy at higher strain rates of 0.5 and 10−1 s−1. At a low strain rate of ∼10−2 s−1, the addition of hydrogen causes a decrease in the high-temperature peak stress. However, at a lower strain rate of ∼10−3 s−1, hydrogen addition has insignificant effects on the high-temperature peak stress. (2) At a higher strain rate of ∼0.5 s−1, the major reasons for hydrogen-induced hardening of the peak stress were solution strengthening and dislocation pinning. (3) At a low strain rate of 10−2 s−1, the reason for decrease of the peak stress was that the hydrogen addition promoted the formation of precipitated phases, thereby facilitating dynamic recrystallization (DRX), and making the microstructure more uniform at high temperatures.

Research on Optimization Strategies of Algorithmic Models for Digital Visual Communication in Cultural Heritage Protection

Abstract With the development of digital technology, digital visual communication has more diversified forms in cultural heritage protection. This paper proposes to design a series of digital commemorative collection tickets for the Dong painting based on the theme. In the visual aesthetic evaluation, the optimization strategy of the digital visual communication algorithms model for cultural heritage protection is evaluated. In the optimization of image enhancement, based on the Retinex algorithm, we propose a single-scale algorithm that achieves the edge enhancement and dynamic range compression of the image and a multi-scale algorithm that meets the requirements of enhancing the details of the image and preserving the color at the same time and introduces the color recovery factor C to avoid the phenomenon of color distortion of the picture. Based on the K-means algorithm and Gaussian mixture model, the optimization of the way of selecting the initial clustering center and determining the number of class clusters is done to improve the efficiency of color feature extraction. Based on the optimized algorithmic model, the evaluation score of the Dong painting product of intangible cultural heritage designed in this paper is 6.95, which is better than the other comparative products. And the total evaluation value of all design elements in the product is more than 80.

Infrared Image Dynamic Range Compression based on Adaptive Contrast Adjustment and Structure Preservation

Infrared Image Dynamic Range Compression based on Adaptive Contrast Adjustment and Structure Preservation

Visualization and analysis of multi-channel dynamic range compression in hearing aids

One main functionality of hearing aids is restoring audibility. This means that low sound pressure levels are amplified above the elevated hearing threshold, and higher sound pressure levels do not exceed the individual uncomfortable loudness level (UCL). To this end, hearing aids provide frequency-dependent dynamic range compression which is denoted as hearing aid channels (HACs) in the recently published standard IEC 60118-16. As an increasing number of HACs, among other features, is one main feature to differentiate between price or technology levels, IEC 60118-16 includes a measurement procedure to verify the number of HACs. In this work, we verify this test procedure with a research hearing aid (RHA), and evaluate six commercial hearing aids of three different manufacturers and two technology levels. These results demonstrate the possibilities and limitations of the new test procedure. Furthermore, we introduced an extension of this test procedure with a channel-specific compression setting to overcome limitations and to get a deeper insight into the functionality of HACs in hearing aids. These results show that many HACs of commercial devices are coupled to neighboring frequencies, and that different strategies are used across manufacturers to adapt the number of HACs for different technology levels.

Mathematical Model of Enhanced Aerial Image Quality and Security through Wavelet-Based Dynamic Range Compression and Watermarking Techniques

This research introduces a novel wavelet-based dynamic range compression system tailored for improving the visual quality of digital photographs taken in high dynamic range environments with non-uniform lighting. Primarily designed for aerial photography in defense and security applications, this method excels in preserving local contrast and tonal rendition while efficiently managing dynamic range, making it highly suitable for aviation industry video streaming. A key advancement of this research is the development of an algorithm capable of enhancing aerial images to a quality that surpasses direct human observation in terms of clarity and robustness. Additionally, the research presents an innovative watermarking technique utilizing Local Binary Patterns (LBP) and Discrete Wavelet Transform (DWT). This approach employs the LBP operator in conjunction with wavelet approximation coefficients to generate a resilient binary watermark. The focus is on establishing more reliable watermarking methods to ensure enhanced security and resilience of embedded information. The study explores the application of public watermarking to facilitate easy access and comprehension of copyright and patent information, highlighting the technology's efficacy in protecting sensitive image data. The research further investigates the enhancement of neural network performance in watermarking applications using Gray Level Co-occurrence Matrix (GLCM) and Principal Component Analysis (PCA) algorithms. PCA is utilized for image selection, while GLCM aids in feature extraction from the original image. The performance of the PCA algorithm is assessed based on its implementation's scaling factor.Through simulations, the proposed algorithm demonstrated impressive results, especially in terms of Peak Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and correlation coefficients, confirming its potential in improving image quality and security in aerial photography and related applications. This study represents a significant advancement in the fields of image processing and digital security, offering new tools and methodologies for enhancing the quality and protection of aerial imagery.

Evolutionary Gravitational Neocognitron Neural Network-Based Blood Flow Velocity Prediction Using Multi-Exposure Laser Speckle Contrast Imaging

Wide-field and noncontact imaging technique called laser speckle contrast imaging (LSCI) is utilized to map blood flow. To greatly improve prediction accuracy, the block-matching along 3-dimensional transform domain collaborative filtering-dependent denoising technique is developed. To implement real-time denoising, the processing time makes complexity. Due to the existence of considerable noise and artifacts, this is challenging to achieve the acceptable level with less raw speckle images. Notwithstanding, it acts poorly while learning from the original LSCI speckle contrast images because of the uneven noise distribution. To overcome this issue, an evolutionary gravitational neocognitron neural network-based blood flow velocity prediction using multiple exposure laser speckle contrast imaging (EGNNN-BFVP-MeLSCI) is proposed. Initially, the input MeLSCI datasets are collected from real-time dataset. The input picture is enhanced and noise is removed using pre-processing technique called altered phase preserving dynamic range compression (APPDRC). Gray level co-occurrence matrix (GLCM) window adaptive approach-dependent feature extraction approach is used for the preprocessed pictures. Using GLCM window adaptive method, the picture characteristics, such as intensity information, images derivative, geodesic information, contrasts, energy, correlations, homogeneity, and entropy, are retrieved. Then, the extracted features are transferred into the EGNNN classifier for accurately predicting the blood flow velocity. The performance of proposed method is executed at python and evaluated under certain performance metrics, such as accuracy, precision, sensitivity, specificity, [Formula: see text]-measure, ROC, computation time, mean squared error, root mean squared error, predicted velocity. The proposed EGNNN-BFVP-MeLSCI method attains higher accuracy of 96.31%, 97.06%, and 93.52%, lower computational time of 97.22%, 91.39%, 96.41% compared with existing approaches, like DnCNN-BFVP-MeLSCI, CFD-BFVP-MeLSCI, DLS-BFVP-MeLSCI, CNN-BFVP-MeLSCI, DBN-BFVP-MeLSCI, CGAN-BFVP-MeLSCI and MVN-BFVP-MeLSCI, respectively.

A novel cell phone localization solution for trapped victims based on compressed RSSI fluctuation range and PSO-BP neural network

With the frequent occurrence of global earthquake disasters in recent years, quickly and accurately locating trapped people is the key to reducing the mortality rate of earthquake disasters. This paper proposes a cell phone localization solution for trapped people that can be rapidly deployed after an earthquake. This solution locates the buried cell phone by collecting its received WiFi signal strength indication (RSSI) data. Reducing the fluctuation of RSSI value is crucial to accurately locating the buried cell phone. We filter RSSI data outliers by hybrid filtering and propose a fluctuation range compression factor Q to reduce the fluctuation range of RSSI data. In order to adapt to the compressed RSSI data fluctuation range, the processed RSSI data were grouped. The grouped data are then imported into the PSO-BP neural network training to build the ranging model. So that there is a mapping relationship between RSSI data and trapped distance. The established ranging model is denoted as the HFQBP ranging model. After establishing the ranging model, the cell phone is localized by an Optimized Multilateration (OM) algorithm. For simplicity, this algorithm is named HFQBP-OM. In the experimental phase, we combined the method proposed in this paper with other methods, resulting in three different localization algorithms: HFQBP-WSC, PL-OM, and PL-WSC. And compared with the algorithms in this paper. The results indicate that the algorithm HFQBP-OM proposed in this paper has improved the total average accuracy of the nine localization points by 34.7%, 54.8%, and 58.9% compared to HFQBP-WSC, PL-OM, and PL-WSC, respectively. The HFQBP-OM algorithm has higher accuracy and stability. Also, it should be noted that the localization solution proposed in this paper uses smartphones connected to WiFi probes to collect RSSI data of the WiFi signal of the buried cell phone. Use a laptop computer to solve the localization coordinates. Smartphones and laptops are so popular that WiFi probes are the only special equipment needed. WiFi probe is inexpensive. It reduces the difficulty of implementing the localization solution in this paper and increases the generalizability of the solution.

An On-Board Imaging Processing Algorithm for Stripmap Mode of Azimuth Multichannel Spaceborne SAR

In the traditional processing methods of azimuth multi-channel spaceborne synthetic aperture radar (SAR), the azimuth spectrum reconstruction and subsequent azimuth focusing are always via full-aperture processing. However, if the multi-channel full-aperture echo data are stored on the satellite, and then the full-aperture algorithms are used for the on-board imaging processing, the huge amount of echo data will require more on-board storage resources and computing resources, and the imaging processing time will become longer. To solve above problems, a novel on-board imaging processing algorithm via the idea that the data acquisition and the on-board imaging processing of the sub-aperture data are carried out simultaneously is proposed in this paper. In the algorithm, the azimuth spectrum ambiguity is eliminated by the sub-aperture azimuth spectrum reconstruction. Then, the range cell migration correction (RCMC) and the range compression for the unambiguous sub-aperture signals are accomplished by the chirp scaling algorithm (CSA). After that, the low-resolution subaperture images are got via the sub-aperture focusing. By coherently combining all sub-aperture images, the final result with high-resolution of all echo data can be obtained. At last, the simulation for the point targets is given to verify the effectiveness of the proposed algorithm.

Reconfigurable Multiband Dynamic Range Compression-based FRM Filter for Hearing Aid

In this research, we present an innovative method for enhancing the performance of hearing aids using a Multiband Dynamic Range Compression-based Reconfigurable Frequency Response Masking (FRM) Filterbank. First, a unform16-band reconfigurable filter bank, which is reconfigurable, is designed utilizing the FRM scheme. The strategic arrangement of each sub-band within the proposed filter bank is meticulously prepared to optimize the matching performance. Based on the hearing characteristics of patients, the sub-bands can be distributed in low, medium, and high-frequency regions. Also, the gain can be adjusted per the patient's hearing profile from their audiogram for better auditory compensation.
 Further, the Multiband Dynamic Range Compression (MBDRC) technique is applied to address the specific needs of individuals with different frequency-dependent hearing impairments. It involves using dynamic range compression independently to different frequency sub-bands within a filter bank. In MBDRC, the compression parameters, such as compression threshold and ratio, can be adjusted independently for every subband. It allows for a more tailored approach to address the specific hearing needs of different frequency regions. If an individual has more severe hearing loss in high-frequency regions, higher compression ratios and lower compression thresholds can be applied to those subbands to amplify and improve audibility for high-frequency sounds. Once dynamic range compression is applied to each sub-band, the resultant sub-bands are reassembled to yield the ultimate output signal, which can subsequently be transmitted to the speaker or receiver of the hearing aid. A GUI can be helpful for better visualization and parameter control, including gain adjustment and compression parameters of this entire process. With this aim in mind, a GUI has been developed on MATLAB. Different audio files can be imported, and their frequency response can be generated and observed. Based on a person's audiogram, the control parameters can be set to low, medium, or high. Their sub-band distribution in low, medium, and high-frequency regions can be visualized. Further, the filter bank makes automatic gain adjustments, as seen in the GUI. The gain points for each band can also be manually adjusted according to users' hearing characteristics to minimize the error. Also, the compression parameters can be set separately for each subband as per the hearing requirement of the patient. Further, the processed output can be visualized in the output frequency response tab, and the input and output audio signals can be analyzed.

A Low-Delay Dynamic Range Compression and Contrast Enhancement Algorithm Based on an Uncooled Infrared Sensor with Local Optimal Contrast

Real-time compression of images with a high dynamic range into those with a low dynamic range while preserving the maximum amount of detail is still a critical technology in infrared image processing. We propose a dynamic range compression and enhancement algorithm for infrared images with local optimal contrast (DRCE-LOC). The algorithm has four steps. The first involves blocking the original image to determine the optimal stretching coefficient by using the information of the local block. In the second, the algorithm combines the original image with a low-pass filter to create the background and detailed layers, compressing the background layer with a dynamic range of adaptive gain, and enhancing the detailed layer for the visual characteristics of the human eye. Third, the original image was used as input, the compressed background layer was used as a brightness-guided image, and the local optimal stretching coefficient was used for dynamic range compression. Fourth, an 8-bit image was created (from typical 14-bit input) by merging the enhanced details and the compressed background. Implemented on FPGA, it used 2.2554 Mb of Block RAM, five dividers, and a root calculator with a total image delay of 0.018 s. The study analyzed mainstream algorithms in various scenarios (rich scenes, small targets, and indoor scenes), confirming the proposed algorithm’s superiority in real-time processing, resource utilization, preservation of the image’s details, and visual effects.

A low delay highly dynamic range infrared imaging system for complex scenes based on FPGA

We proposed a hardware-oriented framework for infrared images compression and enhancement. The aim is to achieve high-quality imaging with minimal delay. Specifically, we introduce several key improvements: Firstly, we adopt the anisotropic guided filtering for layering, which effectively addresses the issue of detail halo. Secondly, we proposed a dynamic range compression algorithm (LIHS&HRC), which is based on local information histogram statistics and thus highlights the gray scales with abundant details. Thirdly, we propose a low-complexity adaptive two-dimensional non-local mean algorithm, which preserves important details while reducing noise. Through extensive experimentation, it is demonstrated the proposed algorithm framework significantly reduces latency and Field Programmable Gate Array (FPGA) resource requirements. It generates high-quality infrared image sequences in a minimal delay of only 0.31 ms at a frame rate of 50fps, with consuming a mere 0.0362 MB of shift register and 0.0312 MB of BRAM.

A fast disturbance cancellation scheme for orthogonal frequency division multiplexing‐based passive radar exploiting reciprocal filter

AbstractThis paper proposes a simple non‐adaptive approach for the suppression of direct signal and clutter contributions in a passive radar system based on orthogonal frequency division multiplexing transmissions, exploiting the properties of a reciprocal filter (RF). The use of a RF allows to circumvent the limits posed by the waveform ambiguity function, producing a data‐independent time‐invariant response to a stationary point‐like target echo at the output of the range compression stage. This feature enables simple clutter cancellation strategies, based on the subtraction of delayed portions of the surveillance signal, according to the conventional moving target indication methodologies. The proposed processing scheme consists of a range compression stage based on a RF, followed by a Single Canceller. Its performance is investigated against a simulated and an experimental data set, for the case of a digital video broadcasting‐terrestial‐based passive radar scenario, and it is compared to an alternative scheme based on a conventional matched filter and adaptive cancellation techniques. The proposed approach is shown to yield a perfect cancellation of the clutter returns from a stationary scene under ideal conditions. Moreover, it proves to be robust to real‐world conditions, despite its considerably limited computational load compared to adaptive clutter cancellation techniques.

A Multi-Domain Joint Novel Method for ISAR Imaging of Multi-Ship Targets

As a key object on the ocean, regulating civilian and military ship targets more effectively is a very important part of maintaining maritime security. One of the ways to obtain high-resolution images of ship targets is the inverse synthetic aperture radar (ISAR) imaging technique. However, in the actual ISAR imaging process, ship targets in a formation often lead to complicated motion conditions. Due to the close distance between the ship targets, the rough imaging results of the targets cannot be completely separated in the image domain, and the small differences in motion parameters lead to overlapping phenomena in the Doppler history. Therefore, for situations in which ship formation targets with little difference in motion parameters are included in the same radar beam, this paper proposes a multi-domain joint ISAR separation imaging method for multi-ship targets. First, the method performs echo separation using the Hough transform (HT) with the minimum entropy autofocus method in the image domain. Secondly, the time–frequency curve is extracted in the time–frequency domain using the short-time Fourier transform (STFT) for time–frequency analysis, which solves the problem of the ship formation targets being aliased on both echo and Doppler history after range compression and achieves the purpose of separating the echo signals of the sub-ship targets with high accuracy. Eventually, better-focused images of each target are obtained via further motion compensation and precise imaging. Finally, the effectiveness of the proposed method is verified using a simulation and measured data.

Diversity matters - extending sound intensity coding by inner hair cells via heterogeneous synapses.

Our sense of hearing enables the processing of stimuli that differ in sound pressure by more than six orders of magnitude. How to process a wide range of stimulus intensities with temporal precision is an enigmatic phenomenon of the auditory system. Downstream of dynamic range compression by active cochlear micromechanics, the inner hair cells (IHCs) cover the full intensity range of sound input. Yet, the firing rate in each of their postsynaptic spiral ganglion neurons (SGNs) encodes only a fraction of it. As a population, spiral ganglion neurons with their respective individual coding fractions cover the entire audible range. How such "dynamic range fractionation" arises is a topic of current research and the focus of this review. Here, we discuss mechanisms for generating the diverse functional properties of SGNs and formulate testable hypotheses. We postulate that an interplay of synaptic heterogeneity, molecularly distinct subtypes of SGNs, and efferent modulation serves the neural decomposition of sound information and thus contributes to a population code for sound intensity.

The impact of speech type on listening effort and intelligibility for native and non-native listeners.

Listeners are routinely exposed to many different types of speech, including artificially-enhanced and synthetic speech, styles which deviate to a greater or lesser extent from naturally-spoken exemplars. While the impact of differing speech types on intelligibility is well-studied, it is less clear how such types affect cognitive processing demands, and in particular whether those speech forms with the greatest intelligibility in noise have a commensurately lower listening effort. The current study measured intelligibility, self-reported listening effort, and a pupillometry-based measure of cognitive load for four distinct types of speech: (i) plain i.e. natural unmodified speech; (ii) Lombard speech, a naturally-enhanced form which occurs when speaking in the presence of noise; (iii) artificially-enhanced speech which involves spectral shaping and dynamic range compression; and (iv) speech synthesized from text. In the first experiment a cohort of 26 native listeners responded to the four speech types in three levels of speech-shaped noise. In a second experiment, 31 non-native listeners underwent the same procedure at more favorable signal-to-noise ratios, chosen since second language listening in noise has a more detrimental effect on intelligibility than listening in a first language. For both native and non-native listeners, artificially-enhanced speech was the most intelligible and led to the lowest subjective effort ratings, while the reverse was true for synthetic speech. However, pupil data suggested that Lombard speech elicited the lowest processing demands overall. These outcomes indicate that the relationship between intelligibility and cognitive processing demands is not a simple inverse, but is mediated by speech type. The findings of the current study motivate the search for speech modification algorithms that are optimized for both intelligibility and listening effort.