The concept of dual function radar-communication (DFRC) provides solution to the problem of spectrum scarcity. This paper examines a multiple-input multiple-output (MIMO) DFRC system with the assistance of a reconfigurable intelligent surface (RIS). The system is capable of sensing multiple spatial directions while serving multiple users via orthogonal frequency division multiplexing (OFDM). The objective of this study is to design the radiated waveforms and receive filters utilized by both the radar and users. The mutual information (MI) is used as an objective function, on average transmit power, for multiple targets while adhering to constraints on power leakage in specific directions and maintaining each user’s error rate. To address this problem, we propose an optimal solution based on a computational genetic algorithm (GA) using bisection method. The performance of the solution is demonstrated by numerical examples and it is shown that, our proposed algorithm can achieve optimum MI and the use of RIS with the MIMO DFRC system improving the system performance.

Providing students with useful feedback on faulty programs can effectively help students fix programs. Spectrum-Based Fault Location (SBFL), which is a widely studied and lightweight technique, can automatically generate a suspicious value of statement ranking to help users find potential faults in a program. However, the performance of SBFL on student programs is not satisfactory, to improve the accuracy of SBFL in student programs, we propose a novel Multi-Correct Programs based Fault Localization (MCPFL) approach. Specifically, We first collected the correct programs submitted by students on the OJ system according to the programming problem numbers and removed the highly similar correct programs based on code similarity, and then stored them together with the faulty program to be located to construct a set of programs. Afterward, we analyzed the suspiciousness of the term in the faulty program through the Term Frequency-Inverse Document Frequency (TF-IDF). Finally, we designed a formula to calculate the weight of suspiciousness for program statements based on the number of input variables in the statement and weighted it to the spectrum-based fault localization formula. To evaluate the effectiveness of MCPFL, we conducted empirical studies on six student program datasets collected in our OJ system, and the results showed that MCPFL can effectively improve the traditional SBFL methods. In particular, on the EXAM metric, our approach improves by an average of 27.51% on the Dstar formula.

With the gradually increase of the application of new energy in microgrids, Electric Spring (ES), as a new type of distributed compensation power electronic device has been widely studied. The Generalized Electric Spring (G-ES) is an improved topology, and the space limitation problem in the traditional topology is solved. Because of the mode of G-ES use in the power grid, a reasonable solution to the voltage loss of the critical section feeder is needed. In this paper, the voltage balance equation based on the feedforward compensation coefficient is established, and a two cascade control strategy based on the equation is studied. The first stage of the two cascade control strategy is to use communication means to realize the allocation of feedforward compensation coefficients, and the second stage is to use the coefficients to realize feedforward fixed angle control. Simulation analysis shows that the proposed control strategy does not affect the control accuracy of the critical load (CL), and effectively improves the operational range of the G-ES.

We evaluated the energy efficiency per 1-bit transmission of an optical light source on InP substrate to achieve optical interconnection. A semiconductor optical amplifier (SOA) assisted extended reach EADFB laser (AXEL) was utilized as the optical light source to enhance the energy efficiency compared to the conventional electro-absorption modulator integrated with a DFB laser (EML). The AXEL has frequency bandwidth extendibility for operation of over 100Gbit/s, which is difficult when using a vertical cavity surface emitting laser (VCSEL) without an equalizer. By designing the AXEL for low power consumption, we were able to achieve 64-Gbit/s, 1.0pJ/bit and 128-Gbit/s, 1.5pJ/bit operation at 50°C with the transmitter dispersion and eye closure quaternary of 1.1dB.

This paper proposes an accurate and efficient method to calculate probability distributions of pulse-shaped complex signals. We show that the distribution over the in-phase and quadrature-phase (I/Q) complex plane is obtained by a recursive probability mass function of the accumulator for a pulse-shaping filter. In contrast to existing analytical methods, the proposed method provides complex-plane distributions in addition to instantaneous power distributions. Since digital signal processing generally deals with complex amplitude rather than power, the complex-plane distributions are more useful when considering digital signal processing. In addition, our approach is free from the derivation of signal-dependent functions. This fact results in its easy application to arbitrary constellations and pulse-shaping filters like Monte Carlo simulations. Since the proposed method works without numerical integrals and calculations of transcendental functions, the accuracy degradation caused by floating-point arithmetic is inherently reduced. Even though our method is faster than Monte Carlo simulations, the obtained distributions are more accurate. These features of the proposed method realize a novel framework for evaluating the characteristics of pulse-shaped signals, leading to new modulation, predistortion and peak-to-average power ratio (PAPR) reduction schemes.

To achieve the extreme high data rate and extreme coverage extension requirements of 6G wireless communication, new spectrum in sub-THz (100-300GHz) and non-terrestrial network (NTN) are two of the macro trends of 6G candidate technologies, respectively. However, non-linearity of power amplifiers (PA) is a critical challenge for both sub-THz and NTN. Therefore, high power efficiency (PE) or low peak to average power ratio (PAPR) waveform design becomes one of the most significant 6G research topics. Meanwhile, high spectral efficiency (SE) and low out-of-band emission (OOBE) are still important key performance indicators (KPIs) for 6G waveform design. Single-carrier waveform discrete Fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) has achieved many research interests due to its high PE, and it has been supported in 5G New Radio (NR) when uplink coverage is limited. So DFT-s-OFDM can be regarded as a candidate waveform for 6G. Many enhancement schemes based on DFT-s-OFDM have been proposed, including null cyclic prefix (NCP)/unique word (UW), frequency-domain spectral shaping (FDSS), and time-domain compression and expansion (TD-CE), etc. However, there is no unified framework to be compatible with all the enhancement schemes. This paper firstly provides a general description of the 6G candidate waveforms based on DFT-s-OFDM enhancement. Secondly, the more flexible TD-CE supporting methods for unified non-orthogonal waveform (uNOW) are proposed and discussed. Thirdly, a unified waveform framework based on DFT-s-OFDM structure is proposed. By designing the pre-processing and post-processing modules before and after DFT in the unified waveform framework, the three technical methods (NCP/UW, FDSS, and TD-CE) can be integrated to improve three KPIs of DFT-s-OFDM simultaneously with high flexibility. Then the implementation complexity of the 6G candidate waveforms are analyzed and compared. Performance of different DFT-s-OFDM enhancement schemes is investigated by link level simulation, which reveals that uNOW can achieve the best PAPR performance among all the 6G candidate waveforms. When considering PA back-off, uNOW can achieve 124% throughput gain compared to traditional DFT-s-OFDM.

Object detection is one of the most important aspects of computer vision, and the use of CNNs for object detection has yielded substantial results in a variety of fields. However, due to the fixed sampling in standard convolution layers, it restricts receptive fields to fixed locations and limits CNNs in geometric transformations. This leads to poor performance of CNNs for slender object detection. In order to achieve better slender object detection accuracy and efficiency, this proposed detector DFAM-DETR not only can adjust the sampling points adaptively, but also enhance the ability to focus on slender object features and extract essential information from global to local on the image through an attention mechanism. This study uses slender objects images from MS-COCO dataset. The experimental results show that DFAM-DETR achieves excellent detection performance on slender objects compared to CNN and transformer-based detectors.

In this letter, we consider a global stabilization problem for a class of feedforward systems by an event-triggered control. This is an extended work of [10] in a way that there are uncertain feedforward nonlinearity and time-varying input delay in the system. First, we show that the considered system is globally asymptotically stabilized by a proposed event-triggered controller with a gain-scaling factor. Then, we also show that the interexecution times can be enlarged by adjusting a gain-scaling factor. A simulation example is given for illustration.

Although intervention practices like mindfulness meditation have proven effective in treating psychosis, there is no clarity on the mechanism of information propagation in the brain. In this study, we formulated a network optimization problem and searched for the optimal solution using Digital Annealer developed by Fujitsu Ltd. This is inspired by quantum computing and is effective in solving large-scale combinatorial optimization problems to find the information propagation pathway in the brain that contributes to the realization of mindfulness. Specifically, we defined the optimal network state as the state of the brain network that is considered to be associated with the mindfulness state. We formulated the problem into two network optimization problems — the minimum vertex-cover problem and the maximum-flow problem — to search for the information propagation pathway that is important for realizing the state. In the minimum vertex-cover problem, we aimed to identify brain regions that are important for the realization of the mindfulness state, and identified eight regions, including four that were suggested to be consistent with previous studies. We formulated the problem as a maximum-flow problem to identify the information propagation pathways in the brain that contribute to the activation of these four identified regions. As a result, approximately 30% of the connections in the brain network structure of this study were identified, and the pathway with the highest flow rate was considered to characterize the bottom-up emotion regulation during mindfulness. The findings of this study could be useful for more direct interventions in the context of mindfulness, which are being investigated by neurofeedback and other methods. This is because existing studies have not clarified the information propagation pathways that contribute to the realization of the brain network states that characterize mindfulness states. In addition, this approach may be useful as a methodology to identify information propagation pathways in the brain that contribute to the realization of higher-order human cognitive activities, such as mindfulness, within large-scale brain networks.

Nowadays there is no way to automatically obtain the function points when using function point analyze (FPA) method, especially for the requirement documents written in Chinese language. Considering the characteristics of Chinese grammar in words segmentation, it is necessary to divide words accurately Chinese words, so that the subsequent entity recognition and disambiguation can be carried out in a smaller range, which lays a solid foundation for the efficient automatic extraction of the function points. Therefore, this paper proposed a method of K-Means clustering based on TF-IDF, and conducts experiments with 24 software requirement documents written in Chinese language. The results show that the best clustering effect is achieved when the extracted information is retained by 55% to 75% and the number of clusters takes the middle value of the total number of clusters. Not only for Chinese, this method and conclusion of this paper, but provides an important reference for automatic extraction of function points from software requirements documents written in other Oriental languages, and also fills the gaps of data preprocessing in the early stage of automatic calculation function points.

Coprime (pair of) DFT filter banks (coprime DFTFB), which process signals like a spectral analyzer in time domain, divides the power spectrum equally into MN bands by employing two DFT filter banks (DFTFBs) of size only M and N respectively, where M and N are coprime integers. With coprime DFTFB, frequencies in wide sense stationary (WSS) signals can be effectively estimated with a much lower sampling rates than the Nyquist rates. However, the imperfection of practical FIR filter and the correlation based detection mode give rise to two kinds of spurious peaks in power spectrum estimation, that greatly limit the application of coprime DFTFB. Through detailed analysis of the spurious peaks, this paper proposes a modified spectral analyzer based on dual coprime DFTFBs and sub-decimation, which not only depresses the spurious peaks, but also improves the frequency estimation accuracy. The mathematical principle proof of the proposed spectral analyzer is also provided. In discussion of simultaneous signals detection, an O-extended MN-band coprime DFTFB (OExt M-N coprime DFTFB) structure is naturally deduced, where M, N, and O are coprime with each other. The original MN-band coprime DFTFB (M-N coprime DFTFB) can be seen a special case of the OExt M-N coprime DFTFB with extending factor O equals ‘1’. In the numerical simulation section, BPSK signals with random carrier frequencies are employed to test the proposed spectral analyzer. The results of detection probability versus SNR curves through 1000 Monte Carlo experiments verify the effectiveness of the proposed spectrum analyzer.

This paper presents the average block error rate (BLER) performance of circular 32QAM and 64QAM schemes employing a frequency domain equalizer (FDE) for discrete Fourier transform (DFT)-precoded orthogonal frequency division multiplexing (OFDM) in multipath Rayleigh fading channels. The circular QAM scheme has an advantageous feature in that the fluctuation in the amplitude component is smaller than that for the cross or rectangular QAM scheme. Hence, focusing on the actual received signal-to-noise power ratio (SNR) taking into account a realistic peak-to-average power ratio (PAPR) measure called the cubic metric (CM), we compare the average BLER of the circular 32QAM and 64QAM schemes with those of cross 32QAM and rectangular 64QAM schemes, respectively. We investigate the theoretical throughput of various circular 32QAM and 64QAM schemes based on mutual information from the viewpoint of the minimum Euclidean distance. Link-level simulation results show that the circular 32QAM and 64QAM schemes with independent bit mapping for the phase and amplitude modulations achieves a lower required average received SNR considering the CM than that with the minimum Euclidean distance but with composite mapping of the phase and amplitude modulations. Through extensive link-level simulations, we show the potential benefit of the circular 32QAM and 64QAM schemes in terms of reducing the required average received SNR considering the CM that satisfies the target average BLER compared to the cross 32QAM or rectangular 64QAM scheme.

Expectation propagation (EP) decoding is proposed for sparse superposition coding in orthogonal frequency division multiplexing (OFDM) systems. When a randomized discrete Fourier transform (DFT) dictionary matrix is used, the EP decoding has the same complexity as approximate message-passing (AMP) decoding, which is a low-complexity and powerful decoding algorithm for the additive white Gaussian noise (AWGN) channel. Numerical simulations show that the EP decoding achieves comparable performance to AMP decoding for the AWGN channel. For OFDM systems, on the other hand, the EP decoding is much superior to the AMP decoding while the AMP decoding has an error-floor in high signal-to-noise ratio regime.

The spreading technique can improve system performance since it mitigates the influence of deeply faded subcarrier channels. Proposals for implementing orthogonal frequency division multiplexing (OFDM) systems include frequency symbol spreading (FSS) based on the Walsh-Hadamard transform (WHT) and the discrete Fourier transform (DFT). In a single carrier frequency division multiplexing (SC-FDMA), good performance is obtained by the interleaved subcarrier allocation. Moreover, in a multiple-input multiple-output (MIMO), interleaving the operation of the different transmit antennas is also effective. By combining these techniques, in this paper, we propose the different antenna interleaved allocation with the full and divided WHT/DFT spreading for a high time resolution carrier interferometry (HTRCI) MIMO-OFDM.

We propose a deep learning-based model for classifying pathological voices using a convolutional neural network and a feedforward neural network. The model uses combinations of heterogeneous parameters, including mel-frequency cepstral coefficients, linear predictive cepstral coefficients and higher-order statistics. We validate the accuracy of this model using the Massachusetts Eye and Ear Infirmary (MEEI) voice disorder database and the Saarbruecken Voice Database (SVD). Our model achieved an accuracy of 99.3% for MEEI and 75.18% for SVD. This model achieved an accuracy that is 7.18% higher than that of competitive models in previous studies.

The conventional universal filtered-DFT-spread-OFDM (UF-DFTs-OFDM) can drastically improve the out-of-band emission (OOBE) caused by the discontinuity between symbols in the conventional cyclic prefix-based DFTs-OFDM (CP-DFTs-OFDM). However, the UF-DFTs-OFDM degrades the communication quality in a long-delay multipath fading environment due to the frequency-domain ripple derived from the long transition time of the low pass filter (LPF) corresponding to the guard interval (GI). In this paper, we propose an enhanced UF-DFTs-OFDM (eUF-DFTs-OFDM) that achieves significantly low OOBE and high communication quality even in a long-delay multipath fading environment. The eUF-DFTs-OFDM applies an LPF with quite short length in combination with the zero padding (ZP) or the CP process. Then, the characteristics of the OOBE, peak-to-average power ratio (PAPR), and block error rate (BLER) are evaluated by computer simulation with the LTE uplink parameters. The result confirms that the eUF-DFTs-OFDM can improve the OOBE by 22.5dB at the channel-edge compared to the CP-DFTs-OFDM, and also improve the ES/N0 to achieve BLER =10-3 by about 2.5dB for QPSK and 16QAM compared to the UF-DFTs-OFDM. For 64QAM, the proposed eUF-DFTs-ODFDM can eliminate the error floor of the UF-DFTs-OFDM. These results indicate that the proposed eUF-DFTs-OFDM can significantly reduce the OOBE while maintaining the same level of communication quality as the CP-DFTs-OFDM even in long-delay multipath environment.

This paper analyzes the effect of error propagation of decision feedback equalizer (DFE) for PAM4 based 400Gb/s Ethernet. First, an analytic model for the error propagation is proposed to estimate the probability of different burst error length due to error propagation for PAM4 link system with multi-tap TX FFE (Feed Forward Equalizer) + RX DFE architecture. After calculating the symbol error rate (SER) and bit error rate (BER) based on the probability model, the theoretical analysis about the impact of different equalizer configurations on BER is compared with the simulation results, and then BER performance with FEC (Forward Error Correction) is analyzed to evaluate the effect of DFE error propagation on PAM4 link. Finally, two FEC interleaving schemes, symbol and bit interleaving, are employed in order to reduce BER further and then the theoretical analysis and the simulation result of their performance improvement are also evaluated. Simulation results show that at most 0.52dB interleaving gain can be achieved compared with non-interleaving scheme just at a little cost in storing memory and latency. And between the two interleaving methods, symbol interleaving performs better compared with the other one from the view of tradeoff between the interleaving gain and the cost and can be applied for 400Gb/s Ethernet.

The windowed interpolation DFT methods have been utilized to estimate the parameters of a single frequency and multi-frequency signal. Nevertheless, they do not work well for the real-valued sinusoids with closely spaced positive- and negative- frequency. In this paper, we describe a novel three-point windowed interpolation DFT method for frequency measurement of real-valued sinusoid signal. The exact representation of the windowed DFT with maximum sidelobe decay window (MSDW) is constructed. The spectral superposition of positive- and negative-frequency is considered and calculated to improve the estimation performance. The simulation results match with the theoretical values well. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.

This paper proposes a novel Depth From Defocus (DFD) technique based on the property that two images having different focus settings coincide if they are reblurred with the opposite focus setting, which is referred to as the “cross reblurring” property in this paper. Based on the property, the proposed technique estimates the block-wise depth profile for a target object by minimizing the mean squared error between the cross-reblurred images. Unlike existing DFD techniques, the proposed technique is free of lens parameters and independent of point spread function models. A compensation technique for a possible pixel disalignment between images is also proposed to improve the depth estimation accuracy. The experimental results and comparisons with the other DFD techniques show the advantages of our technique.

We realize a multicore erbium-doped fiber amplifier (MC-EDFA) with 2dB optical gain improvement (average) by recycling the residual 0.98μm pump light from the MC-EDF output. Eight-channel per core wavelength division multiplexed (WDM) Nyquist PM-16QAM optical signal amplification is demonstrated over a 40-minute period. Furthermore, we demonstrate the proposed MC-EDFA's stability by using it to amplify a Nyquist PM-16QAM signal and evaluating the resulting Q-factor variation. We found that our scheme contributes to reducing the total power consumption of MC-EDFAs in spatial division multiplexing (SDM)/WDM networks by up to 33.5%.