Complex Echo Research Articles

Decomposition of full-waveform LiDAR data utilizing an adaptive B-spline-based model and particle swarm optimization

High-precision waveform decomposition is crucial for LiDAR applications. Existing methods encounter challenges including poor target detection and low accuracy in extracting parameters of irregular components, especially in complex echoes. We introduce an adaptive B-spline-based decomposition (AdaptB-spline) method, which uses B-spline curves to adaptively adjust the shape and position of component through the particle swarm optimization (PSO); and proposes an initial parameter estimation method based on the B-spline and Richardson-Lucy (RL) deconvolution, which improves the noise immunity and component detection. Experiments were conducted on synthetic waveforms and satellite LiDAR waveforms by AdaptB-spline and other four methods (Gaussian (Gauss), B-spline-based (B-spline), skew-normal (SkewN), and multi-Gaussian (MultiGauss) decomposition). We concluded that AdaptB-spline exhibits superior performance in terms of component RMSE, CC, R2, component parameter error and range error metrics compared to the four methods. So AdaptB-spline can enhance component detection and accurately fit Gaussian or non-Gaussian waveforms, demonstrating outstanding target detection and ranging precision.

Sonographic and clinicopathologic features of metaplastic breast carcinoma and infiltrating ductal carcinoma: a comparative single-center cohort study.

The rarity of metaplastic breast carcinoma (MBC) has resulted in limited sonographic data. Given the inferior prognosis of MBC compared to invasive ductal carcinoma (IDC), accurate preoperative differentiation between the two is imperative for effective treatment planning and prognostic prediction. The objective of this study was to assess the diagnostic accuracy of MBC and differentiate it from IDC by analyzing sonographic and clinicopathologic features. In this retrospective cohort study, 197 women comprising 200 IDC lesions were enrolled between January 2012 and December 2021 and 20 women comprising 20 pure MBC lesions were enrolled between January 2019 and December 2019. A comparison was made between the sonographic and clinicopathologic characteristics of MBC and IDC. The results indicated that patients with MBC had a higher proportion of tumor grade 3 (95.0% vs. 32.5%; P<0.001), high Ki-67 expression (100.0% vs. 75.0%; P<0.001), and the triple-negative subtype (90.0% vs. 13.0%; P<0.001) as compared to those with IDC. On ultrasound (US) findings, MBC lesions tended to have a larger size (≥5 cm: 45.0% vs. 1.5%; P<0.001), regular shape (45.0% vs. 1.5%, P<0.001), circumscribed margin (40.0% vs. 0.5%, P<0.001), a complex cystic and solid echo pattern (50.0% vs. 3.5%; P<0.001), and posterior acoustic enhancement (95.0% vs. 14.5%; P<0.001). Additionally, MBC was more likely to be misinterpreted as a benign lesion by sonographers than was IDC (30.0% vs. 4.5%; P<0.001). Multilayer perceptron analysis revealed posterior acoustic enhancement, circumscribed margins, and size as distinguishing factors between these two tumor types. The estimated rates of local recurrence, distant metastasis, and 5-year overall survival in 19 cases with MBC were found to be 10.5%, 31.6%, and 65.0%, respectively. MBC typically presents as a large breast mass with more benign US features in older women, findings which may facilitate its accurate diagnosis and differentiation from other breast masses.

Spectral and temporal cues used by echolocating bottlenose dolphins to discriminate changes in inter-highlight intervals

Echolocating dolphins use spectral cues to discriminate complex echoes from targets with multiple reflective surfaces; however, the specific cues involved are not clear. This study investigated the role of spectral interference patterns on the dolphin’s ability to discriminate two-highlight echoes. In task one of the study, two dolphins were trained to echolocate, listen to returning electronic (“phantom”) echoes with two identical highlights, and produce a conditioned acoustic response if the inter-highlight interval (IHI) increased. The amount that the IHI increased varied across trials. Task two replicated task one but applied a random phase shift to all highlights. This varied the specific locations of notches along the frequency axis in the complex echo spectrum but preserved the notch spacing (equal to the inverse of the IHI). For IHIs less than 250 μs, discrimination thresholds increased with IHI and were larger with random phase shifts. For IHIs greater than 250 μs, thresholds plateaued and were similar for both tasks. Results agree with those from a previous study with passively listening dolphins. Namely, echolocating dolphins can use the spacing of notches in spectral interference patterns to detect IHI changes within the ∼250-μs temporal window, but temporal cues are used at longer IHIs. [Work supported by ONR]

Ultrasound Findings of Suspected Breast Cancer Lesions in Females Accessing Breast Ultrasound Services in Selected Hospitals in Lusaka, Zambia

Background: Breast cancer is the most common malignancy among women in Zambia. Ultrasound can be a useful tool for early diagnosis of breast cancer based on the breast lesions’ features. However, an overlap in the ultrasound features of malignant and benign breast tumours has been noticed together with a paucity of information on the ultrasound features that are more suggestive of breast cancer. Objective: The purpose of this study was to identify ultrasound features of suspected breast cancer lesions which may assist in ascertaining a high breast cancer or benign breast disease suspicion index, that may be used to prioritise patients requiring further and urgent management for breast cancer. Methods: Systematic random sampling was used for the selection of participants in this study. The selected patients first underwent an ultrasound scan in the Ultrasound department to obtain the breast lesion ultrasound features. The histology results for the same patients were then obtained from the University Teaching Hospital (UTH) histology laboratory or the histology report filed in the patient’s medical file. Results: Irregular shape of breast lesions, vertical orientation of breast lesions, hypoechogenicity of breast lesions, complex echo pattern of breast lesions, irregular or spiculated margin contours of breast lesions, compression of breast lesion surrounding tissue, absence of hyperechogenic spots in breast lesions and a breast lesion boundary with neither a hyperechoic halo nor thin capsule (None) were found to be associated with breast cancer. No breast lesion showed less than 3 ultrasound features suspicious for breast cancer. Hyperechoic spots in breast lesions were found to be associated with non-cancerous breast lesions. Conclusion: Low-resource countries, such as Zambia, where access to imaging diagnostic equipment such as Mammography and Magnetic Resonance Imaging machines (MRI), and histology examinations are scarce, the preceding ultrasound findings .......

A Polarimetric Scattering Characteristics-Guided Adversarial Learning Approach for Unsupervised PolSAR Image Classification

Highly accurate supervised deep learning-based classifiers for polarimetric synthetic aperture radar (PolSAR) images require large amounts of data with manual annotations. Unfortunately, the complex echo imaging mechanism results in a high labeling cost for PolSAR images. Extracting and transferring knowledge to utilize the existing labeled data to the fullest extent is a viable approach in such circumstances. To this end, we are introducing unsupervised deep adversarial domain adaptation (ADA) into PolSAR image classification for the first time. In contrast to the standard learning paradigm, in this study, the deep learning model is trained on labeled data from a source domain and unlabeled data from a related but distinct target domain. The purpose of this is to extract domain-invariant features and generalize them to the target domain. Although the feature transferability of ADA methods can be ensured through adversarial training to align the feature distributions of source and target domains, improving feature discriminability remains a crucial issue. In this paper, we propose a novel polarimetric scattering characteristics-guided adversarial network (PSCAN) for unsupervised PolSAR image classification. Compared with classical ADA methods, we designed an auxiliary task for PSCAN based on the polarimetric scattering characteristics-guided pseudo-label construction. This approach utilizes the rich information contained in the PolSAR data itself, without the need for expensive manual annotations or complex automatic labeling mechanisms. During the training of PSCAN, the auxiliary task receives category semantic information from pseudo-labels and helps promote the discriminability of the learned domain-invariant features, thereby enabling the model to have a better target prediction function. The effectiveness of the proposed method was demonstrated using data captured with different PolSAR systems in the San Francisco and Qingdao areas. Experimental results show that the proposed method can obtain satisfactory unsupervised classification results.

Discrimination of simulated two-highlight echoes including phase manipulations by bottlenose dolphins (Tursiops truncatus)

Dolphins potentially use spectral cues to discriminate inter-highlight interval (IHI) for passively presented, simulated two-highlight echo stimuli. To investigate this potential, dolphins were trained to listen to repetitive two-highlight “background” echoes and respond upon a change to “target” echoes with increased IHI. In the first experimental task, all highlights had the same phase, and the target differed from the background only in terms of IHI. In the second task, highlights within both background and target echoes had random phase angles. This resulted in a spectral interference pattern for the two-highlight echo with the same notch spacing as the first condition, but different absolute positions of the notches along the frequency axis. Discrimination thresholds for the constant-phase task were lower than those for the random-phase task at IHIs of 250 μs and lower. Thresholds for both tasks increased with increasing background IHI and were comparable at 375 and 500 μs. Within the dolphin auditory temporal window of ∼250 μs, IHI discrimination is therefore likely related to—but not strictly based on—the spacing of notches in the complex echo spectrum. Above 250 μs, IHI is likely determined through direct encoding of highlight timing in the auditory periphery. [Work funded by ONR.]

Radar Signal Classification System Based on BP Neural Network

This paper introduces a method of recognition and classification of radar echo signals. We introduce the method of radar signal preprocessing, including the construction of complex echo signals of radar and the normalization of spectrums. Besides, we calculate the power spectral density (PSD) and extract the features. Then we construct the back propagation (BP) neural network as the classifier. Finally, we conduct the simulation experiment and analyze the experimental results. From the experimental results, it can be seen that this method is better than the traditional single-layer neural network recognition method. In addition, our method can not only maintain a better classification accuracy of radar signals despite the low signal-to-noise ratio, but also reduce the classification gap between different radar signals, with good stability and robustness.

Chaos and multifold complexity for an inverted harmonic oscillator

We examine the multifold complexity and Loschmidt echo for an inverted harmonic oscillator. We give analytic expressions for any number of precursors, implementing multiple backward and forward time evolutions of the quantum state, at the leading order in the perturbation. We prove that complexity is dominated by the longest permutation of the given time combination in an alternating “zig-zag” order, the exact same result obtained with holography. We conjecture that the general structure for multifold complexity should hold true universally for generic quantum systems, in the limit of a large number of precursors.

움직이는 사람에 대한 비접촉식 생체신호추정 연구

The non-contact vital sign estimation of an individual using radar sensors has been widely researched. However, in the case of an individual with undesirable rigid-body motion of the torso, non-contact vital sign estimation may fail owing to extreme phase fluctuations caused by the larger rigid-body motion of the torso than the micro motion of the vital sign. To address this problem, we propose a rigid-body motion compensation method using the complex echo signals of the IR-UWB radar for the vital sign estimation of an individual with rigid-body motion. The proposed method extracts a complex signal vector using principal component analysis from complex echo signals in a radial range and pulse time domain. Next, compensation for the rigid-body motion is implemented by minimizing the entropy of the spectrum for the complex signal vector. In simulations using a point-scatterer model and experiments using IR-UWB commercial radar, we verified that our proposed method is capable of performing successful vital sign estimation of an individual with undesirable rigid-body motion.

Optimal Echocardiographic Parameters to Improve the Diagnostic Yield of Tc-99m-Bone Avid Tracer Cardiac Scintigraphy for Transthyretin Cardiac Amyloidosis.

Echocardiographic deformation-based ratios and novel multi-parametric scores have been suggested to discriminate transthyretin cardiac amyloidosis (ATTR-CM) from other causes of increased left ventricular wall thickness among patients referred for ATTR-CM evaluation. Their relative predictive accuracy has not been well studied. We sought to (1) identify echocardiographic parameters predictive of ATTR-CM and (2) compare the diagnostic accuracy of these parameters in patients with suspected ATTR-CM referred for technetium-99m-pyrophosphate scintigraphy. Echocardiograms from 598 patients referred to 3 major amyloidosis centers for technetium-99m-pyrophosphate to detect ATTR-CM were analyzed, including longitudinal strain (LS) analysis. Deformation ratios (septal apex to base ratio, relative apical sparing, ejection fraction to global LS), a multi-center European increased wall thickness score, and Mayo Clinic derived ATTR score (transthyretin cardiac amyloidosis score) were calculated. A logistic regression model was used to identify the parameters that best associated with a diagnosis of ATTR-CM. Comparison of the diagnostic capacity of the parameters was performed by receiver operating characteristic curves and the area under the curve (AUC). Over half of the subjects (54.2%) were diagnosed with ATTR-CM (78% were men, median age of 76 years). Age, inferolateral wall thickness, and basal LS were the strongest predictors of ATTR-CM, AUC of 0.87 (95% CI: 0.83, 0.90), superior to the increased wall thickness score AUC of 0.78 (95% CI: 0.73, 0.83; P=0.004). An inferolateral wall thickness of ≥14 mm (AUC: 0.73) was as accurate as the published cut-offs for transthyretin cardiac amyloidosis score and septal apex to base (AUC: 0.72 and 0.69, P=0.8 and P=0.1, respectively), and was superior to ejection fraction to global LS and relative apical sparing (AUC: 0.64 and 0.53, P<0.001, respectively). A cut-off of ≥-8% for average basal LS (AUC: 0.76, CI: 0.72-0.79) had a similar area under the curve to transthyretin cardiac amyloidosis score (TCAS) (P=0.2); outperforming the other indices (P<0.01). Inferolateral wall thickness and average basal LS performed as well as or better than more complex echo ratios and multiparametric scores to predict ATTR-CM.

Bioelectric Fields at the Beginnings of Life.

The consensus on the origins of life is that it involved organization of prebiotic chemicals according to the underlying principles of thermodynamics to dissipate energy derived from photochemical and/or geochemical sources. Leading theories tend to be chemistry-centric, revolving around either metabolism or information-containing polymers first. However, experimental data also suggest that bioelectricity and quantum effects play an important role in biology, which might suggest that a further factor is required to explain how life began. Intriguingly, in the early part of 20th century, the concept of the "morphogenetic field" was proposed by Gurwitsch to explain how the shape of an organism was determined, while a role for quantum mechanics in biology was suggested by Bohr and Schrödinger, among others. This raises the question as to the potential of these phenomena, especially bioelectric fields, to have been involved in the origin of life. It points to the possibility that as bioelectricity is universally prevalent in biological systems today, it represents a more complex echo of an electromagnetic skeleton which helped shape life into being. It could be argued that as a flow of ions creates an electric field, this could have been pivotal in the formation of an energy dissipating structure, for instance, in deep sea thermal vents. Moreover, a field theory might also hint at the potential involvement of nontrivial quantum effects in life. Not only might this perspective help indicate the origins of morphogenetic fields, but also perhaps suggest where life may have started, and whether metabolism or information came first. It might also help to provide an insight into aging, cancer, consciousness, and, perhaps, how we might identify life beyond our planet. In short, when thinking about life, not only do we have to consider the accepted chemistry, but also the fields that must also shape it. In effect, to fully understand life, as well as the yin of accepted particle-based chemistry, there is a yang of field-based interaction and an ethereal skeleton.

442 Myocardial Work Estimation With Ct Aids Evaluation Of Regional Right Ventricular Function

Introduction: Myocardial work (MW) - the area of the myocardial stress-strain loop - can serve as a better metric of myocardial performance than volumetric or strain measures, as MW is independent of loading conditions and has been shown to reflect regional oxygen perfusion and glucose metabolism. In practice, MW has been estimated as the ventricular pressure-myocardial strain area (PSA) using echocardiography. However, the RV’s complex geometry and echo’s limited acoustic windows can limit this approach.

Chaos and complexity for inverted harmonic oscillators

We investigate the circuit complexity and Loschmidt echo for the (inverted) harmonic oscillators. Using a perturbative approach, we analytically derive the Lyapunov exponent and scrambling time of the inverted harmonic oscillators. We show that the circuit complexity and Loschmidt echo exhibit qualitatively similar behaviors, particularly the consistent Lyapunov exponent.

Three-dimensional echo decorrelation monitoring of radiofrequency ablation in ex vivo bovine liver.

Three-dimensional (3D) echo decorrelation imaging was investigated for monitoring radiofrequency ablation (RFA) in ex vivo bovine liver. RFA experiments (N = 14) were imaged by 3D ultrasound using a matrix array, with in-phase and quadrature complex echo volumes acquired about every 11 s. Tissue specimens were then frozen at -80 °C, sectioned, and semi-automatically segmented. Receiver operating characteristic (ROC) curves were constructed for assessing ablation prediction performance of 3D echo decorrelation with three potential normalization approaches, as well as 3D integrated backscatter (IBS). ROC analysis indicated that 3D echo decorrelation imaging is potentially a good predictor of local RFA, with the best prediction performance observed for globally normalized decorrelation. Tissue temperatures, recorded by four thermocouples integrated into the RFA probe, showed good correspondence with spatially averaged decorrelation and statistically significant but weak correlation with measured echo decorrelation at the same spatial locations. In tests predicting ablation zones using a weighted K-means clustering approach, echo decorrelation performed better than IBS, with smaller root mean square volume errors and higher Dice coefficients relative to measured ablation zones. These results suggest that 3D echo decorrelation and IBS imaging are capable of real-time monitoring of thermal ablation, with potential application to clinical treatment of liver tumors.

ESTIMATING TREE CANOPY HEIGHT IN DENSELY FOREST-COVERED MOUNTAINOUS AREAS USING GEDI SPACEBORNE FULL-WAVEFORM DATA

Abstract. Tree canopy height is an important parameter for estimating forest carbon stock, and mountainous areas with dense vegetation cover are the main distribution areas of trees, so it is important to accurately measure the forest canopy height in mountainous areas with high vegetation cover. This paper focuses on the problem of poor inversion accuracy of canopy height estimation in large scale densely forest-covered mountainous areas, uses the complex echoes of GEDI full-waveform spaceborne laser in mountainous forests as the data source, improves the accuracy of forest canopy height estimation from multiple perspectives by improving the detection capability of weak and overlapping waves and constructing a canopy height model considering slope correction and environmental features. The results show that the modified RGD algorithm proposed in this paper can effectively detect the weak and overlapping waves in the echoes and improve the DTM/DSM inversion accuracy significantly (FVC&gt;90%, R2=0.8663/R2=0.8073). In addition, the forest canopy height model is constructed on the basis of the physical geometric model of mountain slope and spatial environment characteristics, and finally the canopy height inversion accuracy of this paper is higher (FVC&gt;90%, R2=0.6729). The experiment proves that the model constructed in this paper is not only applicable to densely forest-covered mountainous areas, but also improves the accuracy of forest canopy height inversion in other environments. This study can provide technical and decision support for forest resource survey and global carbon balance.

Gaussian Inflection Point Selection for LiDAR Hidden Echo Signal Decomposition

High-quality waveform decomposition, as one of the most critical cores of light detection and ranging (LiDAR) data processing, has become increasingly interesting. However, the current Gaussian decomposition method cannot handle the superimposed waveform with only one peak. Thus, this letter proposes a Gaussian inflection point selection method (GIPS). The method uses the number of inflection points (IPs) near the peak of the echo signal to judge the position of waveform half-width and selects an appropriate waveform half-width to iteratively decompose the echo signal to obtain Gaussian components, which are combined into a Gaussian model. Finally, a global Levenberg–Marquardt least-square algorithm (LM algorithm) is used to optimize the Gaussian model for fitting the echo signal. To verify the accuracy and effectiveness of GIPS, the experiments were conducted using land, vegetation and ice sensor (LVIS) data. The results show that the GIPS method can decompose complex LiDAR echo signals more correctly and efficiently than other methods do with an average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.9799.

MDLI-Net: Model-Driven Learning Imaging Network for High-Resolution Microwave Imaging With Large Rotating Angle and Sparse Sampling

Microwave imaging with large rotating angle and sparse sampling is an attractive approach to obtain the high-resolution target image with reduced radar resource. However, the popular imaging methods, e.g., Range-Doppler (RD), back projection (BP), and sparse recovery (SR), are difficult to deal with large rotating angle and sparse sampling simultaneously. In recent years, deep learning (DL) has been widely studied and been successfully used to handle the problems in computer vision. However, since most existing DL networks are put forward for the real visual image and a large amount of data is essential for network training, DL cannot be directly used to process the complex and sparse target echo for microwave imaging. In this article, a new learning imaging framework is proposed and a model-driven learning imaging network (MDLI-Net) is built for high-resolution microwave imaging with large rotating angle and sparse sampling. In the proposed framework, the electromagnetic scattering model is used to generate the training data efficiently, and the sparse microwave imaging theory is applied to guide the design of the deep imaging network. By inputting the 2-D sparse complex-valued target echo, the trained MDLI-Net can output the high-resolution and focused target image efficiently. The effectiveness of the proposed learning imaging method is validated by experiment results with both simulated and real data.

Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network

Sign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a feature engineering approach on the skeleton position alone to provide a better representation of the features and avoid the use of all of the image information. In addition, the paper proposed model makes use of recurrent neural networks (RNNs)-based models. Training RNNs is inherently difficult, and consequently, motivates to investigate alternatives. Besides the trainable long short-term memory (LSTM), this study has proposed the untrained low complexity echo system network (ESN) classifier. The accuracy of both LSTM and ESN indicates they can outperform those in state-of-the-art studies. In addition, ESN which has not been proposed thus far for ASLT exhibits comparable accuracy to the LSTM with a significantly lower training time.

Contrast-Enhanced Ultrasound Imaging Features of Focal Splenic Tuberculosis.

BackgroundThe aim of this study was to characterize the contrast-enhanced ultrasound imaging features of focal splenic tuberculosis.Material/MethodsWe retrospectively analyzed the conventional ultrasound (US) and contrast-enhanced ultrasound (CEUS) imaging features of 22 patients with splenic TB confirmed by surgical histopathology or biopsy.ResultsConventional US demonstrated that 15 of the 22 patients had a single lesion, while 7 had multiple lesions. The maximum diameter of the lesions ranged from 1.0 to 3.7 cm. Of the 22, 17 were detected with hypoecho and 5 were detected with complex echo by conventional US. Seven (7/22) were detected with blood flow signals by color Doppler flow image (CDFI). CUES demonstrated that 18 cases (81.8%, 18/22) began to enhance in the arterial phase, mostly followed by slow wash-out in the intermediate or late parenchymal phase, and 4 (18.2%, 4/22) presented with non-enhancement during all phases. The enhancement patterns were categorized into 4 types: Type I, homogeneous enhancement (2/22); Type II, rim-like enhancement (12/22); Type III, septation-like enhancement (4/22); and Type IV, non-enhancement (4/22).ConclusionsCEUS showed that splenic TB lesions were enhanced in the arterial phase, followed by slow washed out or persistent enhancement in the intermediate and late parenchymal phases. The rim- or septation-like enhancement may be helpful for diagnosing splenic TB. The splenic lesions presenting round hypoecho by conventional US and complete non-enhancement by CEUS are highly suspicious of splenic TB.

Echolocation click parameters and biosonar behaviour of the dwarf sperm whale (Kogia sima).

Dwarf sperm whales (Kogia sima) are small toothed whales that produce narrow-band high-frequency (NBHF) echolocation clicks. Such NBHF clicks, subject to high levels of acoustic absorption, are usually produced by small, shallow-diving odontocetes, such as porpoises, in keeping with their short-range echolocation and fast click rates. Here, we sought to address the problem of how the little-studied and deep-diving Kogia can hunt with NBHF clicks in the deep sea. Specifically, we tested the hypotheses that Kogia produce NBHF clicks with longer inter-click intervals (ICIs), higher directionality and higher source levels (SLs) compared with other NBHF species. We did this by deploying an autonomous deep-water vertical hydrophone array in the Bahamas, where no other NBHF species are present, and by taking opportunistic recordings of a close-range Kogia sima in a South African harbour. Parameters from on-axis clicks (n=46) in the deep revealed very narrow-band clicks (root mean squared bandwidth, BWRMS, of 3±1 kHz), with SLs of up to 197 dB re. 1 µPa peak-to-peak (μPapp) at 1 m, and a half-power beamwidth of 8.8deg. Their ICIs (mode of 245 ms) were much longer than those of porpoises (<100 ms), suggesting an inspection range that is longer than detection ranges of single prey, perhaps to facilitate auditory streaming of a complex echo scene. On-axis clicks in the shallow harbour (n=870) had ICIs and SLs in keeping with source parameters of other NBHF cetaceans. Thus, in the deep, dwarf sperm whales use a directional, but short-range echolocation system with moderate SLs, suggesting a reliable mesopelagic prey habitat.