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As an important part of oceanographic surveying, underwater sonar image classification has attracted much attention. Most of the existing classification methods cannot be widely used in underwater sonar image classification. However, deep learning models can automatically extract underwater sonar image features to improve the classification accuracy through an internal network structure. In the present study, a novel deep learning model with adaptive weights convolutional neural network (AW-CNN) was proposed to classify underwater sonar images. To solve the random initialization of filter weights in a convolutional neural network (CNN), the generated weights of the deep belief network (DBN) were applied to adaptively replace the randomly trained filter weights of the CNN in the implementation process of the AW-CNN for underwater sonar image classification. Specifically, first, dimension conversion was accomplished by using the increment-dimension function to unify the inputs of the CNN and the DBN. Then, according to the dimension conversion, the internal fusion of the two models is realized, and replacement of the randomly trained filter weights of the CNN was completed. Finally, in order to further improve the classification accuracy, the local response normalization (LRN) function is proposed to normalize the adaptive weights in the network initialization. Compared with other models, the classification results demonstrate that the proposed AW-CNN approach has the capability to effectively and successfully divide sonar images into their relevant seabed classes, which is beneficial to finding mines and shoals and detecting the integrated degree of the dam bottom.

In this study, we examine whether education, as an important component of the human capital of auditors, is related to the occurrence of financial misstatement, and investigate the moderating effect of professional experience. Using a sample of 16,651 firm-year observations from the Chinese stock market from 2003 to 2014, we find that the education level of signing auditors is significantly negatively associated with the likelihood of financial misstatement, which suggests that higher education can enhance the ethics and independence of auditors and mitigate the risk of financial misstatement. In addition, professional experience attenuates the negative relation between the education level of signing auditors and financial misstatement. Our findings are also robust to a variety of sensitivity tests, and our conclusions still hold after using a two-stage OLS-logistic regression to address the endogeneity problem. Lastly, the negative effect of education level on financial misstatement holds only for 985 Project universities, low individual-level (audit-firm-level) client importance, and state-owned enterprises. Keywords: Auditor human capital, Financial misstatement, Education level, Professional experience, Signing auditors, JEL classification: M42, J41, P36

The outbreak of COVID-19 has prompted a wide range of policy responses from governments around the world. In this study, we investigate the effect of governmental policies on the spread of the COVID-19 in a cross-country setting using the Oxford COVID-19 Government Response Stringency Index. We find that stringent government policies overall, and the following policies in particular, are associated with a lower spread rate of COVID-19 cases: workplace closing, restrictions on gatherings, close of public transport, stay-at-home order, restrictions on internal movement, and international travel controls; while school closing and public events cancellation are not associated with a lower COVID-19 spread. After including all policies into one single regression and examining their associations simultaneously with the virus spread, we find that the two policies stand out and remain to have a negative association with the COVID-19 spread: close of public transport and restrictions on international travel. Finally, we show that when countries are more oriented toward a tight culture, their governmental strict policies effect on the spread of COVID-19 becomes 1.5 – 3 times stronger than countries more toward a loose culture. Our findings suggest that the governments need to carefully implement policies to cope with the COVID-19 spread in their own social and cultural context.

Time-frequency analysis-based signal feature extraction has been well studied in the active sonar area in recent years. When the non-stationary signal is analyzed, traditional approach, such as short-time Fourier transform, cannot provide an excellent time or frequency resolution simultaneously on account of the uncertainty principle. Furthermore, acoustic echo is strongly influenced by multi-path channel and complex environment of shallow water. In this paper, an enhanced time-frequency analysis method named second-order synchrosqueezing transform is investigated here to extract underwater acoustic echo characteristics and reconstruct them. Second-order synchrosqueezing transform is able to extract and clearly depict components with time-varying spectrum, and furthermore it allows individual reconstruction of these components. Synchrosqueezing is performed in three steps. First, the continuous wavelet transform of the received signal is calculated. Second, an initial estimate of the demodulated frequency is calculated based on the first step. Finally, continuous wavelet transform is squeezed via reassignment. In this way, synchrosqueezing representation is obtained, which process is invertible. Furthermore, second-order operation aims to adapt synchrosqueezing transform to the superposition of perturbed linear chirps.

Favorable propagation (FP) and channel hardening (CH) are desired properties in massive multiple-input multiple-output (MIMO) systems. To date, these properties have primarily been analyzed for classical \textit{statistical} channel models, or \textit{ray-based} models with very specific angular parameters and distributions. This paper presents a thorough mathematical analysis of the asymptotic system behavior for ray-based channels with \textit{arbitrary} ray distributions, and considers \textit{two} types of antenna array structures at the cellular base station: a uniform linear array (ULA) and a uniform planar array (UPA). In addition to FP and channel hardening, we analyze the \textit{large system potential} (LSP) which measures the asymptotic ratio of the expected power in the desired channel to the expected total interference power when both the antenna and user numbers grow. LSP is said to hold when this ratio converges to a positive constant. The results demonstrate that while FP is guaranteed in ray-based channels, CH may or may not occur depending on the nature of the model. Furthermore, we demonstrate that LSP will not normally hold as the expected interference power grows logarithmically for both ULAs and UPAs relative to the power in the desired channel as the system size increases. Nevertheless, we identify some fundamental and attractive properties of massive MIMO in this limiting regime.

Compared with traditional single-beam side-scan sonar device, current side-scan sonar have greatly improved the stability, resolution and definition of sonar image, which leads to a large increase in the amount of data, so it is impossible to completely identify target artificially. During the side-scanning sonar operation, sonar equipment is affected by ocean currents, wind waves, etc., resulting in posture instability and image distortion. The complex underwater environment and the interference of its own will also cause a lot of noise in the collected sonar image. The problems above have brought great difficulties for target recognition. This paper utilizes LeNet5 convolutional neural network (CNN) to perform automatic rough recognition of side-scan sonar images, and then uses scale-invariant feature transform (SIFT) feature matching for further recognition. The processing of experimental data shows that the accuracy and efficiency of target recognition are good using these two algorithms.

Hyperspectral images provide a precise representation of the earth’s surface, with abundant spectral and spatial features, but normal classification algorithms use only the information provided by the spectral features of each data point. In this paper, we propose a new approach to hyperspectral image classification based on Mahalanobis distance metric learning and kernel learning that considers both the features of the spectral bands and a spatial prior. This approach consists of two components. First, we obtain a primary labeled classification result and a posterior probability distribution for each pixel point using a Mahalanobis-kernel-based classifier. Second, instead of the original or extracted spectral features, we reconstruct the spatial relationship of the hyperspectral images using the posterior probability of every data point, smooth the boundaries, and revise suspicious points based on this piecewise information using a kernel-based multi-region segmentation method. In an experimental study, we adopt a support vector machine (SVM) classifier as the kernel classifier to obtain the posterior probabilities using dimensionally reduced data. The proposed method is compared with several other methods from various perspectives. Simulation experiments run on several real hyperspectral data sets are reported. The results show that the proposed method performs better than other comparable classification algorithms, especially in a condition-constrained environment.

In recent years, machine learning has been prospectively performed in underwater acoustic ranging task. The scale of acoustic observation has a considerable impact in the performance of networks. In this paper, we propose a joint color spectrum and conditional generative adversarial network processing for underwater acoustic source ranging. The joint method maps acoustic observations into color images to extract features. Original images are fed into conditional generative adversarial network and vast images generated from the generator network could be regarded as an expansion of the training set. The feasibility and effectiveness of the joint method are verified through some statistical indexes. In addition, variational auto-encoder and principal component analysis are used to measure the similarity of generated and original data. The method yields a meaningful improvement for underwater acoustic source ranging.

A narrowband Chan-Vese model with adaptive ladder initialization approach is proposed in this paper to segment underwater sonar image. Specifically, for the first time, the problem of more iterative times, human intervention necessity and lower segmentation accuracy, which are commonly exist in the SDF and BIF, was solved with the method utilizing the new adaptive ladder initialization of zero level set. Then, to further reduce the impact of the global search on traditional Chan-Vese model, the narrowband Chan-Vese model is introduced. It is shown that by applying the adaptive ladder initialization is ultimately local optimization and accurate segmentation results. On this basis, recurring to analysis of traditional Chan-Vese model law, combined with narrowband Chan-Vese model with adaptive ladder initialization approach, the objective and quantitative analysis method is developed. Finally, segmentation results demonstrate the effectiveness and adaptability of the proposed method.

An adaptive approach is proposed to denoise and detect the underwater sonar image in this paper. Firstly, to improve the denoising performance of non-local spatial information in the underwater sonar image, an adaptive non-local spatial information denoising method based on the golden ratio is proposed. Then, a new adaptive cultural algorithm (NACA) is proposed to accurately and quickly complete the underwater sonar image detection in this paper. Concretely, NACA has two improvements. In the first place, to obtain better initial clustering centres, an adaptive initialization algorithm based on data field (AIA-DF) is proposed in this paper. Secondly, in the belief space of NACA, a new update strategy is adopted to update cultural individuals in terms of the quantum-inspired shuffled frog leaping algorithm (QSFLA). The experimental results show that the proposed denoising method in this paper can effectively remove relatively large and small filtering degree parameters and improve the denoising performance to some extent. Compared with other comparison algorithms, the proposed NACA can converge to the global optimal solution within small epochs and accurately complete the object detection, having better effectiveness and adaptability.

Using hand-collected data from China, we examine whether cultural embeddedness on cigarette packages affects corporate financial reporting quality, and further investigate the moderating effect of foreign directors. The findings reveal that firms in provinces with more cultural embeddedness on cigarette packages exhibit significantly higher extent of discretionary accruals. This finding suggests that cultural embeddedness on cigarette packages as a tobacco-related sinful behavior window dressing shapes unethical social atmosphere, elicits unethical managerial behavior, brings out information distortion, and eventually reduces financial reporting quality. Moreover, the positive relation between cultural embeddedness on cigarette packages and discretionary accruals is less pronounced for firms with foreign directors than for their counterparts. Furthermore, our findings are robust to a variety of sensitivity tests using alternative proxies for cultural embeddedness on cigarette packages and financial reporting quality, and further our conclusions still stand after employing the difference-in-difference approach, two-stage least square regression procedure and two-stage Heckman method to address the endogeneity problem. Lastly, pictorial health warnings on cigarette packages are significantly negatively related with country-level smokers, but cultural embeddedness on cigarette packages is significantly positively associated with tobacco consumption at the province level and owner-manager agency costs in China, providing supportive evidence to our findings.

An underwater acoustic Kalman filter-based blind adaptive multi-user detection algorithm, suitable for underwater acoustic communication networks, is proposed in this paper. The algorithm can be employed to effectively improve the system capacity of multi-user communication in underwater acoustic sensor networks, reduce transmitting power and costs on power control, extend the multi-user communication distance, weaken or eliminate intersymbol interference, multiple access interference and near-far effect, thus effectively utilizing limited underwater frequency band resource. First, the dynamic model of underwater acoustic multi-user communication system and the optimal filter equation of the proposed algorithm are found. Second, computation complexity is analyzed, and convergence analysis is carried out in terms of excess mean output energy. Finally, pool, river, sea, and under-ice asynchronous communication experiments have been carried out for both the scalar and the vector hydrophones. Good experimental results verify the effectiveness of the proposed algorithm.

In order to solve the problem of depth classification of the underwater target in a very low frequency acoustic field, the active component of cross spectra of particle pressure and horizontal velocity (ACCSPPHV) is adopted to distinguish the surface vessel and the underwater target. According to the effective depth of a Pekeris waveguide, the placing depth forecasting equations of passive vertical double vector hydrophones are proposed. Numerical examples show that when the sum of depths of two hydrophones is the effective depth, the sign distribution of ACCSPPHV has nothing to do with horizontal distance; in addition, the sum of the first critical surface and the second critical surface is equal to the effective depth. By setting the first critical surface less than the difference between the effective water depth and the actual water depth, that is, the second critical surface is greater than the actual depth, the three positive and negative regions of the whole ocean volume are equivalent to two positive and negative regions and therefore the depth classification of the underwater target is obtained. Besides, when the 20 m water depth is taken as the first critical surface in the simulation of underwater targets (40 Hz, 50 Hz, and 60 Hz respectively), the effectiveness of the algorithm and the correctness of relevant conclusions are verified, and the analysis of the corresponding forecasting performance is conducted.

Vector sensors could simultaneously collect not only pressure but also velocity signals and there will be a valuable gain when combining these two kinds of signals properly. This paper studies single carrier underwater acoustic (UWA) communication using a vector sensor. In a recent field experiment, acoustic communication transmissions were carried out over a 1 km range in shallow water at Songhua River, Heilongjiang province, China. The same coded signal was transmitted when the vector sensor was deployed to depth of 1 m, 1.5 m, 2 m, and 2.5 m respectively. Decoding results, that nearly error-free performance at four depths, show the robustness of UWA communications using a single vector sensor in a highly refractive environment. The four received signals are also combined together to asynchronously realize a vertical array processing using passive time reversal based on the spatial and temporal diversity, achieving a maximum gain about 8.02 dB compared with a single sensor processing.

A new Orthogonal Frequency Division Multiplexing (OFDM) based underwater acoustic communication scheme utilizing acoustic vector sensor is presented. The acoustic channel assumed has the slow time-varying characteristic and the neighboring signals can be considered as transmitted through the same channel. The differential modulation encodes information through the relative phase changes between adjacent subcarriers. Therefore, instead of adding pilots to monitor the channel, the differential demodulation is used to reconstructs transmitted information without necessity of channel estimation. This increases the transmission rate and reduce the complexity of processor at the receiver. An acoustic vector sensor is used in the differential OFDM system to further improve the performance of the system with 4.8 dB gains that can be obtained by combined processing pressure and velocity signals from the acoustic vector sensor. To validate the capability of the proposed scheme, an experiment with picture and voice source information was conducted in a lake. The results obtained at different distances demonstrate the proposed system offers a significant advantage.

This paper applies dolphin whistles to covert underwater acoustic (UWA) communication and proposes a UWA communication scheme based on M-ary bionic signal coding. At the transmitter end, the scheme maps multiple information bits into a dolphin whistle through a signal selector. At the receiver end, passive time reversal mirror (PTRM) is used for channel equalization and source information is restored according to the decision of which whistle is transmitted. The scheme has high spread spectrum gain. The anti multi-path performance is greatly improved when using PTRM. Different from traditional covert UWA communication methods, this mimicked signal is unlikely to alert an adversary even in high SNRs because of its real existence in marine environment. A tank experiment is conducted for the scheme, at communication rate of 50 bit/s with SNR −5 dB user information is recovered at a very low bit error rate. The results of tank experiment demonstrate the feasibility of this covert UWA communication scheme.

Multiple-input multiple-output (MIMO) techniques have been the subject of increased attention for underwater acoustic communication for its ability to significantly improve the channel capabilities. Recently, an under-ice MIMO acoustic communication experiment was conducted in shallow water which differs from previous works in that the water column was covered by about 40 centimeters thick sea ice. In this experiment, high frequency MIMO signals centered at 10 kHz were transmitted from a two-element source array to a four-element vertical receive array at 1km range. The unique under-ice acoustic propagation environment in shallow water seems naturally separate data streams from different transducers, but there is still co-channel interference. Time reversal followed by a single channel decision feedback equalizer is used in this paper to compensate for the inter-symbol interference and co-channel interference. It is demonstrated that this simple receiver scheme is good enough to realize robust performance using fewer hydrophones (i.e. 2) without the explicit use of complex co-channel interference cancelation algorithms such as parallel interference cancelation or serial interference cancelation. Two channel estimation algorithms based on least square and least mean square are also studied for MIMO communications in this paper and their performance are compared using experimental data.

More and more scientific investigations and research activities have been carried out in arctic region, among which the related research on the Arctic Ocean is particularly important. It can be seen that arctic hydro acoustics is the significant support to guarantee military presence and normal scientific research activities. Under-ice acoustic communication experimental was done in Songhua River, Harbin, China in January 2015. Minus 20∼30 degrees work environment brings a great challenge to the under-ice experimental campaign. All of the under-ice acoustic communication tests achieve low bit error rate communication at 1km range with different received depth because of the relatively stable under-ice channel. It is found that the closer to the ice the simpler the under-ice acoustic (UIA) channel structure is. Time reversal mirror (TRM) can use the physical characteristics of the UIA channel to focus toward the desired user in multi-user UIA communication. Active average sound intensity (AASI) detector can estimate all azimuths of users with the same frequency band at the same time in order to achieve directional communication by vector combination. Space-division multiple-access (SDMA) based on TRM and AASI detector is used in code-division multiple-access (CDMA) UIA communication to increase the capacity of multiuser system. A method developed for direct sequence spread spectrum communications in an underwater channel are used to extract the transmitted symbols. Under-ice data shows that as many as 12 users can be supported simultaneously in CDMA system combined with SDMA technology.

Active sonar on small platform which uses vector array is selected in this paper as research object, the methods of signal and DOA estimation and the performance of these methods are discussed. Vector array comprehensive copy correlator and vector array comprehensive adaptive copy correlator which use acoustic pressure and vibration velocity at the same time are proposed in this paper. The performance of signal processing and the precision of DOA estimation can be improved by these methods. Simulation results present that the performance of the proposed methods are better than conventional copy correlator. In addition, vector array comprehensive adaptive copy correlator has higher gain than vector array comprehensive copy correlator in multi-path channel.

A direct sequence/binary differential phase-shift keying underwater acoustic communication technique based on differential coherent demodulation is proposed. Differential coherent demodulation is adopted at the receiving terminal to overcome the difficulty of estimating the carrier in underwater acoustic communication and overcoming the frequency and phase error resulting from carrier frequency drift. Anti-multipath, anti-Doppler, low-error-rate, effective and robust features for a complex underwater acoustic channel were verified in a lake experiment.

The detecting performance of active sonar have been affected by the weak targets scattering and serious noise background. If artificial reducing the threshold to get high detecting capability, the false alarm probability will be very high. Therefore, this paper propose a weak target detecting method based on multistatic information fusion. After getting more information about the target from different positions and extracting the target feature vectors to make a fusion, a better recogniting performance will be present. The target scattering centers are extracted by CLEAN technique, which could avoid extracting the false scattering centers, and adapt in low signal to noise ratio (SNR). The neural network classifier is used to make a fusion and recognize the target. Simulation results show that this method has better target identification performance and low false alarm probability, demonstrating the target features fusion has high value for active sonar detecting in the future.

Because of the complex marine environment, there are a large number of false arms when the active sonar is detecting targets. It is difficult to find the real targets, so the false negative rate has a significant increase. In order to suppress the clutter better and reduce the false alarm rate maximally, the problem of target detection via Multiple Hypothesis Tracking based Track Before Detect (MHT -TBD) is considered in this paper. MHT -TBD is a data association method that could remove wild spots in a clutter environment and save useful data. But for high maneuvering targets, the tracking success rate of MHT-TBD is significantly reduced. An improved MHT-TBD method named interactive multimode MHT-TBD (IMM-MHT-TBD), which combines interactive multimode with multi hypothesis tracking is introduced in this paper. It shows a superior performance in high maneuvering targets tracking by maintenance and data association in a clutter environment. Simulation results show that IMM-MHT-TBD can solve the problem of ...

Virtual time reversal mirror can effectively suppress intersymbol interference caused by multi-path effects caused by the Multi-path effect In underwater acoustic communication. Digital virtual time reversal mirror is optimal implementation of traditional virtual time reversal mirror. It only need to store Amplitude information and Delay information of the point which amplitude is not zero in the Estimated channel impulse response and it only do limited multiply-accumulate operations to received signal in the process of time reversal convolution. Thus, digital of virtual time reversal mirror saves DSP storage space, at the same time, it will aslo significantly reduce the computational. In this paper, we do simulation experiments of digital of virtual time reversal mirror on the Da Vinci platform, verifies that digital of virtual time reversal mirror can effectively suppress multi-path effects caused by ISI and it is feasible in engineering applications.

The underwater acoustic image measurement technology is a technology of passive acoustic localization in near field, which can get the track of moving target by measuring the main noise sources of the target through large scale array. Because of the complexity of underwater environment, it is difficult to lay a large high-accuracy half-wavelength array. Thus it is more convenient to use a sparse array in underwater acoustic image measuring. The sea trial research on the underwater acoustic image measurement based on the sparse array has been done to verify the feasibility of the method. The experiment result shows that using sparse array would lead to a series of interference fringes appearing in the image, but the interference fringes can be removed by frequency-division integrated technology. Three tracks measurement results of a catamaran are given. The results prove that the method is feasible.

Under ice acoustic communication experimental was done in Songhua River, Harbin, China, in January 2015. Minus 20–30 degrees work environment brings a great challenge to the under ice experimental. A series of underwater acoustic communication tests including spread spectrum, OFDM, pattern time delay shift coding (PDS) and CDMA have been achieved. Experimental result shows that the under ice channel is relatively stable and the closer to the ice the simpler the channel structure is. All of the under ice acoustic communication tests achieve low bit error rate communication at 1 km range with different received depth. Under ice CDMA multiuser acoustic communication shows that as many as 12 users can be supported simultaneously with as few as five receivers in under ice channels, using the time reversal mirror combined with the differential correlation detectors.