Modulation Division Research Articles

Vital Characteristics Cellular Neural Network (VCeNN) for Melanoma Lesion Segmentation: A Biologically Inspired Deep Learning Approach.

Cutaneous melanoma is a highly lethal form of cancer. Developing a medical image segmentation model capable of accurately delineating melanoma lesions with high robustness and generalization presents a formidable challenge. This study draws inspiration from cellular functional characteristics and natural selection, proposing a novel medical segmentation model named the vital characteristics cellular neural network. This model incorporates vital characteristics observed in multicellular organisms, including memory, adaptation, apoptosis, and division. Memory module enables the network to rapidly adapt to input data during the early stages of training, accelerating model convergence. Adaptation module allows neurons to select the appropriate activation function based on varying environmental conditions. Apoptosis module reduces the risk of overfitting by pruning neurons with low activation values. Division module enhances the network's learning capacity by duplicating neurons with high activation values. Experimental evaluations demonstrate the efficacy of this model in enhancing the performance of neural networks for medical image segmentation. The proposed method achieves outstanding results across numerous publicly available datasets, indicating its potential to contribute significantly to the field of medical image analysis and facilitating accurate and efficient segmentation of medical imagery. The proposed method achieves outstanding results across numerous publicly available datasets, with an F1 score of 0.901, Intersection over Union of 0.841, and Dice coefficient of 0.913, indicating its potential to contribute significantly to the field of medical image analysis and facilitating accurate and efficient segmentation of medical imagery.

Automatic reorientation to generate short-axis myocardial PET images

BackgroundAccurately redirecting reconstructed Positron emission tomography (PET) images into short-axis (SA) images shows great significance for subsequent clinical diagnosis. We developed a system for automatic redirection and quantitative analysis of myocardial PET images.MethodsA total of 128 patients were enrolled for 18 F-FDG PET/CT myocardial metabolic images (MMIs), including 3 image classifications: without defects, with defects, and excess uptake. The automatic reorientation system includes five modules: regional division, myocardial segmentation, ellipsoid fitting, image rotation and quantitative analysis. First, the left ventricular geometry-based canny edge detection (LVG-CED) was developed and compared with the other 5 common region segmentation algorithms, the optimized partitioning was determined based on partition success rate. Then, 9 myocardial segmentation methods and 4 ellipsoid fitting methods were combined to derive 36 cross combinations for diagnostic performance in terms of Pearson correlation coefficient (PCC), Kendall correlation coefficient (KCC), Spearman correlation coefficient (SCC), and determination coefficient. Finally, the deflection angles were computed by ellipsoid fitting and the SA images were derived by affine transformation. Furthermore, the polar maps were used for quantitative analysis of SA images, and the redirection effects of 3 different image classifications were analyzed using correlation coefficients.ResultsOn the dataset, LVG-CED outperformed other methods in the regional division module with a 100% success rate. In 36 cross combinations, PSO-FCM and LLS-SVD performed the best in terms of correlation coefficient. The linear results indicate that our algorithm (LVG-CED, PSO-FCM, and LLS-SVD) has good consistency with the reference manual method. In quantitative analysis, the similarities between our method and the reference manual method were higher than 96% at 17 segments. Moreover, our method demonstrated excellent performance in all 3 image classifications.ConclusionOur algorithm system could realize accurate automatic reorientation and quantitative analysis of PET MMIs, which is also effective for images suffering from interference.

Effect of Line-of-sight Channels on DC-biased Optical Filter Bank Multicarrier Visible Light Communication with Multiple LED Arrays

This paper investigates the impact of propagation delay and channel loss due to the use of multiple LED arrays in visible light communication (VLC) systems based on filter bank multicarrier (FBMC) modulation. FBMC offers greater spectral efficiency, and asynchronous transmission and is a promising alternative scheme to orthogonal frequency division modulation (OFDM). The proposed FBMC model is based on 4-quadrature amplitude modulation (QAM) and 16-QAM formats and uses 100 symbols and 600 input bits per symbol. In this paper, the VLC-FBMC system is designed based on the line-of-sight (LOS) model under the additive white Gaussian noise (AWGN) channel. Comparison analyses between different bit rates in terms of bit error rate (BER), best sampling point, and signal-to-noise ratio (SNR) requirement have been carried out to show the delay and loss effect on communication quality and system performance. The results demonstrate that the proposed FBMC model achieves a bit rate of up to 29.296 Mbit/s with a low BER of 10-3 and less SNR penalty in high QAM formats, demonstrating its potential as a viable alternative to OFDM for future VLC systems.

Digital Division Algorithms for Efficient Execution on Integrated Circuits

In this paper, we analyse division algorithms for use on chips and propose the implementation of an optimal divider for these chips. By “optimal”, we refer to an algorithm that meets the following criteria: space efficiency – which involves minimizing resource utilization on the IC’s die area; speed efficiency – the algorithm's processing time (measured in n clock cycles); power efficiency – power consumption of the divider; implementation time – time for implementation of the algorithm using HDL. The chosen algorithm should strike a balance between space efficiency and processing speed, ensuring the efficient use of hardware resources while delivering swift computational results. The ultimate goal is to create a division module that aligns seamlessly with the integrated circuit's architecture, catering to computational efficiency and resource constraints.

Instance-level Adversarial Source-free Domain Adaptive Person Re-identification

Domain adaption (DA) for person re-identification (ReID) has attained considerable progress by transferring knowledge from a source domain with labels to a target domain without labels. Nonetheless, most of the existing methods require access to source data, which raises privacy concerns. Source-free DA has recently emerged as a response to these privacy challenges, yet its direct application to open-set pedestrian re-identification tasks is hindered by the reliance on a shared category space in existing methods. Current source-free DA approaches for person ReID still encounter several obstacles, particularly the divergence-agnostic problem and the notable domain divergence due to the absent source data. In this article, we introduce an Instance-level Adversarial Mutual Teaching (IAMT) framework, which utilizes adversarial views to tackle the challenges mentioned above. Technically, we first elaborately develop a variance-based division (VBD) module to segregate the target data into instance-level subsets based on their similarity and dissimilarity to the source using the source-trained model, implicitly tackling the divergence-agnostic problem. To mitigate domain divergence, we additionally introduce a dynamic adversarial alignment (DAA) strategy, aiming to enhance the consistence of feature distribution across domains by employing adversarial instances from the target data to confuse the discriminators. Experiments reveal the superiority of the IAMT over state-of-the-art methods for DA person ReID tasks, while preserving the privacy of the source data.

A Study on the Algorithm Configuration for Sharing Career Experience in a Metaverse Platform

This study relates to a system for providing an experience sharing platform in a metaverse environment. In more detail, it is about an experience-sharing platform providing system that enables users to share and trade each other's experiences based on metaverse by allowing users to share information about their careers with each other in a metaverse environment. The algorithm proposed by this study is largely composed of a metaverse interface and a main server. The metaverse interface includes a motion generation unit that generates user motion information according to a signal received through a user terminal owned by a user, an avatar generation unit that generates an avatar that moves in conjunction with the user motion information, and a space generation unit that creates a virtual metaverse space for displaying at least one avatar. The main server is largely composed of input of requirements, matching, experience sharing, and cost payment modules and databases. In addition, the position control and area division module are also composed of the main server including a satisfaction reflecting movement, a virtual horizontal line generation, and an additional division processing parts, which are lower configuration algorithms of them.

DF-ParPINN: parallel PINN based on velocity potential field division and single time slice focus

Modified Benney-Luke equation (mBL equation) is a three-dimensional temporal-spatial equation with complex structures, that is a high-dimensional partial differential equation (PDE), it is also a new equation of the physical ocean field, and its solution is important for studying the internal wave-wave interaction of inclined seafloor. For conventional PDE solvers such as the pseudo-spectral method, it is difficult to solve mBL equation with both accuracy and speed. Physics-informed neural network (PINN) incorporates physical prior knowledge in deep neural networks, which can solve PDE with relative accuracy and speed. However, PINN is only suitable for solving low-dimensional PDE with simple structures, and not suitable for solving high-dimensional PDE with complex structures. This is mainly because high-dimensional PDEs usually have complex structures and high-order derivatives and are likely to be high-dimensional non-convex functions, and the high-dimensional non-convex optimization problem is an NP-hard problem, resulting in the PINN easily falling into inaccurate local optimal solutions when solving high-dimensional PDEs. Therefore, we improve the PINN for the characteristics of mBL equation and propose “DF-ParPINN: parallel PINN based on velocity potential field division and single time slice focus” to solve mBL equation with large amounts of data. DF-ParPINN consists of three modules: temporal-spatial division module of overall velocity potential field, data rational selection module of multiple time slices, and parallel computation module of high-velocity fields and low-velocity fields. The experimental results show that the solution time of DF-ParPINN is no more than 0.5s, and its accuracy is much higher than that of PINN, PIRNN, cPINN, and DeepONet. Moreover, the relative error of DF-ParPINN after deep training 1000000 epochs can be reduced to less than 0.1. The validity of DF-ParPINN proves that the improved PINN also can solve high dimensional PDE with complex structures and large amounts of data quickly and accurately, which is of great significance to the deep learning of the physical ocean field.

FDSR: An Interpretable Frequency Division Stepwise Process Based Single-Image Super-Resolution Network.

Deep learning has excelled in single-image super-resolution (SISR) applications, yet the lack of interpretability in most deep learning-based SR networks hinders their applicability, especially in fields like medical imaging that require transparent computation. To address these problems, we present an interpretable frequency division SR network that operates in the image frequency domain. It comprises a frequency division module and a step-wise reconstruction method, which divides the image into different frequencies and performs reconstruction accordingly. We develop a frequency division loss function to ensure that each reconstruction module (ReM) operates solely at one image frequency. These methods establish an interpretable framework for SR networks, visualizing the image reconstruction process and reducing the black box nature of SR networks. Additionally, we revisited the subpixel layer upsampling process by deriving its inverse process and designing a displacement generation module. This interpretable upsampling process incorporates subpixel information and is similar to pre-upsampling frameworks. Furthermore, we develop a new ReM based on interpretable Hessian attention to enhance network performance. Extensive experiments demonstrate that our network, without the frequency division loss, outperforms state-of-the-art methods qualitatively and quantitatively. The inclusion of the frequency division loss enhances the network's interpretability and robustness, and only slightly decreases the PSNR and SSIM metrics by an average of 0.48 dB and 0.0049, respectively.

Improving the performance of free space optical systems: a space-time orthogonal frequency division modulation approach

Free space optical (FSO) communication systems are known for high capacity and information security. The overall system performances of FSO systems are however significantly affected by atmospheric turbulence induced fading. This paper, therefore, proposes a technique to mitigate this effect through the introduction of an additional degree of error correction capacity by exploiting the spectral dimension in the coding space. A space-time trellis coded orthogonal frequency division modulation (OFDM) scheme was developed, simulated and evaluated for optical communication through a Gamma-Gamma channel. The evaluation of the coding gain obtained from the simulation results, the mathematical analysis and the truncation error analysis shows that the proposed technique is a promising and viable technique for improving the error correction performance of space-time codes for free space optical communication links.

Robust Doppler compensation for low frequency underwater communications

Impairment caused by Doppler frequency shift and harsh channel condition distinguish underwater acoustic communications from radio communications. Slow speed of acoustic waves produces relatively high Doppler shift and result in expansion or compression in time samples, which is known as Doppler scaling. In particular, the Doppler scaling causes significant carrier frequency offset (CFO) in orthogonal frequency division modulation (OFDM). To compensate the CFO, it is essential to resample signal samples according to the Doppler scale. In theory, the Doppler scale can be estimated by measuring the distance between peaks of the matched filter output of consecutive Doppler-invariant preambles. In practice, multi-path reflections and non-orthogonality between the HFM and OFDM payload produce large numbers of local false peaks. In this paper, we propose a robust Doppler estimation technique by employing constant false alarm rate (CFAR) filter to remove false peaks. We validate the performance of the proposed scheme through two sea-trials carried out in shallow water near Jervis Bay, Australia. In the sea-trials, a slow-moving towed hydrophone array was deployed as the receiver and the source was a stationary, low source level, vertical transmit array. Sea-trial results demonstrated up to 112bps information rate using bit-interleaved coded OFDM over 400 Hz bandwidth.

Orthogonal time-frequency space (OTFS) modulation for underwater mobile acoustic communications

This paper investigates the Orthogonal Time-Frequency Space (OTFS) Modulation for Underwater Mobile Acoustic Communications where the communication channel suffers from severe multipath and Doppler effects simultaneously. Practical OTFS modulation schemes with different parameters are designed for acoustic transmission at a center frequency of 115 kHz and a bandwidth of 11.5 kHz. The schemes are tested in lake experiments where the transmitter was anchored and the receiver was towed by a boat at a speed of approximately 1 m/s or 3.6 km/h. The receiver utilizes low-complexity channel estimation and equalization algorithms, such as NLMS (Normalized Least Mean Squares) and IPNLMS (improved proportionate NLMS) algorithms. The results show some insights of the OTFS scheme for acoustic communications. First, the mobile acoustic channel characteristics are different in the 2D delay-Doppler domain than those in the 1D time and frequency domains for the Single-Carrier Coherent Modulation (SCCM) and the Orthogonal frequency division modulation (OFDM), respectively. Second, in mobile scenarios, the OTFS scheme receiver effectively and significantly reduces the accuracy requirements of the Doppler compensation algorithm and provides better frequency-selective fading suppression and Doppler effect robustness compared to SCCM and OFDM schemes. Third, the OTFS scheme has better anti-multipath performance and reduces multipath interference by effectively differentiating signals on different paths in the 2D delay-Doppler domain.

Detecting tomato disease types and degrees using multi-branch and destruction learning

Tomato diseases have a crucial impact on tomato yield and quality, and their detection and control can improve agricultural production efficiency and promote the healthy development of agricultural production. In this study, diseased tomato leaves were used as the research object and fine-grained feature learning of diseases was performed using a deep-learning method to achieve tomato disease degree detection. The proposed model uses ResNet-50 as the backbone network, and the object positioning, destruction and reassembly, and attention area division modules are built on it. Without adding parameters or further boundary annotation, the object positioning module could reliably predict the location of lesions. By erasing the contextual semantic data, the proposed destruction and reassembly module learns more granular features. The attention area division module is capable of localizing the lesion positions, thereby enhancing the recognition accuracy. The results of the study indicate that the proposed model performed better than state-of-the-art methods in identifying the extent of tomato diseases, and achieved an accuracy of 95.03%. The accuracy in identifying the types of tomato diseases is 98.25%. Concludingly, the proposed model is competitive with other state-of-the-art approaches for identifying the severity of tomato diseases and provides a novel model for identifying the degrees of plant diseases.

Modulation and demodulation with quadrature square waves as carriers to improve spectrum utilization

Frequency division modulation enables fast and highly accurate detection of multiple signals, but its spectrum utilization is extremely low. Of course, existing methods improve the spectrum utilization by using quadrature signals as carriers, but it requires the carrier generation to be synchronized with the start of the acquisition. This is difficult to achieve when the carrier generation and the signal acquisition are not controlled by the same clock. Therefore, this paper proposes a demodulation method for the case where the carrier signal is a pair of quadrature square wave signals and the start of the carrier is not synchronized with the sampling. The method demodulates two signals modulating a pair of quadrature carriers by calculating the phase of one independent carrier signal. In this paper, three-wavelength multispectral images are acquired, and the demodulation results are compared with those acquired by the time-division method. The results show that there is a high similarity between the two, which proves the effectiveness of the method proposed in this paper, improves spectrum utilization, and is useful for the acquisition of multiplexed signals.

Chest tomosynthesis deblurring using CNN with deconvolution layer for vertebrae segmentation.

Limited scan angles cause severe distortions and artifacts in reconstructed tomosynthesis images when the Feldkamp-Davis-Kress (FDK) algorithm is used for the purpose, which degrades clinical diagnostic performance. These blurring artifacts are fatal in chest tomosynthesis images because precise vertebrae segmentation is crucial for various diagnostic analyses, such as early diagnosis, surgical planning, and injury detection. Moreover, because most spinal pathologies are related to vertebral conditions, the development of methods for accurate and objective vertebrae segmentation in medical images is an important and challenging researcharea. The existing point-spread-function-(PSF)-based deblurring methods use the same PSF in all sub-volumes without considering the spatially varying property of tomosynthesis images. This increases the PSF estimation error, thus further degrading the deblurring performance. However, the proposed method estimates the PSF more accurately by using sub-CNNs that contain a deconvolution layer for each sub-system, which improves the deblurringperformance. To minimize the effect of the spatially varying property, the proposed deblurring network architecture comprises four modules: (1) block division module, (2) partial PSF module, (3) deblurring block module, and (4) assembling block module. We compared the proposed DL-based method with the FDK algorithm, total-variation iterative reconstruction with GP-BB (TV-IR), 3D U-Net, FBPConvNet, and two-phase deblurring method. To investigate the deblurring performance of the proposed method, we evaluated its vertebrae segmentation performance by comparing the pixel accuracy (PA), intersection-over-union (IoU), and F-score values of reference images to those of the deblurred images. Also, pixel-based evaluations of the reference and deblurred images were performed by comparing their root mean squared error (RMSE) and visual information fidelity (VIF) values. In addition, 2D analysis of the deblurred images were performed by artifact spread function (ASF) and full width half maximum (FWHM) of the ASFcurve. The proposed method was able to recover the original structure significantly, thereby further improving the image quality. The proposed method yielded the best deblurring performance in terms of vertebrae segmentation and similarity. The IoU, F-score, and VIF values of the chest tomosynthesis images reconstructed using the proposed SV method were 53.5%, 28.7%, and 63.2% higher, respectively, than those of the images reconstructed using the FDK method, and the RMSE value was 80.3% lower. These quantitative results indicate that the proposed method can effectively restore both the vertebrae and the surrounding softtissue. We proposed a chest tomosynthesis deblurring technique for vertebrae segmentation by considering the spatially varying property of tomosynthesis systems. The results of quantitative evaluations indicated that the vertebrae segmentation performance of the proposed method was better than those of the existing deblurringmethods.

An Algorithm Design about Psychological Counseling Platform Using the Derivative Works

The psychological counseling platform using the derivative works in this study includes four main modules and two sub modules. The first main one is a content database that stores a plurality of derivative works in which keywords are set. The second is a message input module including a request message providing unit that displays a request message input from the client terminal on the counselor's terminal, and a counseling message input unit that receives a counseling message written based on the request message from the counselor's terminal. The third main module is a keyword extraction module that extracts keywords by analyzing the counseling message. Final one is an answer providing module that provides derivative works corresponding to the extracted keyword and the consultation message to the client terminal. As the sub modules, this system consists of a region division module and a chart generation module. The region division module allows the screen of the client terminal to correspond to the central region where the counseling message is placed and the vertices of an equilateral triangle having the central point of the central region as the outer center. The chart generation module consists of a chart display unit, an expected value calculation unit, and a variable value setting unit. Based on the proposed algorithm, this system has the effect of increasing the effectiveness of psychological counseling and improving the concentration of counseling by providing recreated secondary copyrighted content using copyright-protected videos, images, music, and photos together with psychological counseling at the same time.

A Review of Design of Digital Clock Based on Verilog HDL

With the development of electronic technology, digital clocks have added many functions that facilitate people's lives, Digital electronic clock is a device that uses digital circuits to realize the digital display of time, minutes, and seconds. This paper mainly discusses how to use Verilog HDL to design a simple digital clock and realize the basic functions such as timing and display in the clock, as well as the platform and tools used. The circuit of the digital clock is divided into three modules, namely the frequency division module, counting module, and decoding display module. And the timing process is through the LED display, and the digital tube displays "hours", "minutes", and "seconds" which are displayed in two digits. The frequency division module divides a 50 MHz input signal to obtain a 1 Hz clock signal, and the counting time module can count and adjust the time of the clock, minutes, and seconds, and then display it on the FPGA development board through the decoding display module. After clock simulation, the system realizes the function of the digital clock and meets the design requirements.

On the Effect of Channel Knowledge in Underwater Acoustic Communications: Estimation, Prediction and Protocol

Underwater acoustic communications are limited by the following channel impairments: time variability, narrow bandwidth, multipath, frequency selective fading and the Doppler effect. Orthogonal Frequency Division Modulation (OFDM) is recognized as an effective solution to such impairments, especially when optimally designed according to the propagation conditions. On the other hand, OFDM implementation requires accurate channel knowledge atboth transmitter and receiver sides. Long propagation delay may lead to outdated channel information. In this work, we present an adaptive OFDM scheme where channel state information is predicted through a Kalman-like filter so as to optimize communication parameters, including the cyclic prefix length. This mechanism aims to mitigate the variability of channel delay spread. This is cast in a protocol where channel estimation/prediction are jointly considered, so as to allow efficiency. The performance obtained through extensive simulations using real channels and interference show the effectiveness of the proposed scheme, both in terms of rate and reliability, at the expense of an increasing complexity. However, this solution is significantly preferable to the conventional mechanism, where channel estimation is performed only at the receiver, with channel coefficients sent back to the transmit node by means of frequent overhead signaling.

LSTM projected layer neural network-based signal estimation and channel state estimator for OFDM wireless communication systems

<abstract><p>Advanced wireless communication technologies, such as 5G, are faced with significant challenges in accurately estimating the transmitted signal and characterizing the channel. One of the major obstacles is the interference caused by the delay spread, which results from receiving multiple signal copies through different paths. To mitigate this issue, the orthogonal frequency division modulation (OFDM) technique is often employed. Efficient signal detection and optimal channel estimation are crucial for enhancing the performance of multi-carrier wireless communication systems. To this end, this paper proposes a Long Short Term Memory-Projected Layer (LSTM-PL) deep neural network(DNN) based channel estimator to detect received OFDM signal. The results show that the LSTM-PL algorithm outperforms traditional methods such as Least Squares(LS), Minimum Mean Square Error (MMSE) and other LSTM deep learning channel estimation methods like Long Short Term Memory(LSTM)-DNN and Bidirectional-LSTM(Bi-LSTM)-DNN, as evidenced by Symbol-Error Rate (SER) outcomes.</p></abstract>

Joint Subcarrier and Power Allocation for Integrated OFDM Waveform in RadCom Systems

In the integrated radar and communication (RadCom) systems, the integrated orthogonal frequency division modulation (OFDM) waveform with interleaved subcarriers (IS) can simultaneously perform radar and communication functions. However, the autocorrelation function (ACF) of the integrated waveform may have high sidelobe level with high probability due to the discontinuous spectrum for radar. To solve this problem, in this letter, a joint subcarrier and power allocation method is proposed to minimize the peak-to-sidelobe ratio (PSLR) while meeting the requirement of the communication data rate (CDR) and maintaining a certain range resolution. The formulated optimization problem is nonconvex, and we propose the cyclic minimization algorithm to obtain the subcarrier and power allocation. Finally, several numerical results are provided to verify the effectiveness of the proposed method.

Saturation of visual responses explains size tuning in rat collicular neurons.

The receptive field of many visual neurons is composed of a central responsive area, the classical receptive field, and a non-classical receptive field, also called the "suppressive surround." A visual stimulus placed in the suppressive surround does not induce any response but modulates visual responses to stimuli within the classical receptive field, usually by suppressing them. Therefore, visual responses become smaller when stimuli exceed the classical receptive field size. The stimulus size inducing the maximal response is called the preferred stimulus size. In cortex, there is good correspondence between the sizes of the classical receptive field and the preferred stimulus. In contrast, in the rodent superior colliculus, the preferred size is often several fold smaller than the classical receptive field size. Here, we show that in the rat superior colliculus, the preferred stimulus size changes as a square root of the contrast inverse and the classical receptive field size is independent of contrast. In addition, responses to annulus were largely independent of the inner hole size. To explain these data, three models were tested: the divisive modulation of the gain by the suppressive surround (the "normalization" model), the difference of the Gaussians, and a divisive model that incorporates saturation to light flux. Despite the same number of free parameters, the model incorporating saturation to light performed the best. Thus, our data indicate that in rats, the saturation to light can be a dominant phenomenon even at relatively low illumination levels defining visual responses in the collicular neurons.