Deblurring Ghost Imaging Reconstruction Based on Underwater Dataset Generated by Few-Shot Learning.

Underwater ghost imaging based on deep learning can effectively reduce the influence of forward scattering and back scattering of water. With the help of data-driven methods, high-quality results can be reconstructed. However, the training of the underwater ghost imaging requires enormous paired underwater datasets, which are difficult to obtain directly. Although the Cycle-GAN method solves the problem to some extent, the blurring degree of the fuzzy class of the paired underwater datasets generated by Cycle-GAN is relatively unitary. To solve this problem, a few-shot underwater image generative network method is proposed. Utilizing the proposed few-shot learning image generative method, the generated paired underwater datasets are better than those obtained by the Cycle-GAN method, especially under the condition of few real underwater datasets. In addition, to reconstruct high-quality results, an underwater deblurring ghost imaging method is proposed. The reconstruction method consists of two parts: reconstruction and deblurring. The experimental and simulation results show that the proposed reconstruction method has better performance in deblurring at a low sampling rate, compared with existing underwater ghost imaging methods based on deep learning. The proposed reconstruction method can effectively increase the clarity degree of the underwater reconstruction target at a low sampling rate and promotes the further applications of underwater ghost imaging.

Simulated and Experimental Verification for a Terahertz Specific Finite Rate of Innovation Signal Processing Method.

Recently, finite rate of innovation methods have been successfully applied to achieve low sampling rates in many areas, such as for ultrasound and radio signals. However, to the best of our knowledge, there are no journal publications applying this to real terahertz signals. In this work, we mathematically describe a finite rate of innovation method applied specifically to terahertz signals both experimentally and in simulation. To demonstrate our method, we applied it to randomized simulated signals with and without the presence of noise and to simple experimental measurements. We found excellent agreement between the simulated signals and those recreated based on results from our method, with this success also being replicated experimentally. These results were obtained at relatively low sampling rates, compared to standard methods, which is a key advantage to using a finite rate of innovation method as it allows for faster data acquisition and signal processing.

A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems.

The future 6G mobile communication network will support an unprecedented amount of Internet of Things (IoT) devices, which will boost the demand for low cost terminals under the principle of green communication. One of the critical issues for low cost terminals is the sampling rate of analog-to-digital converters (ADCs) at the receivers. A high sampling rate of the ADC gives rise to a high energy consumption and high hardware cost for the terminal. In the conventional multi-user OFDM systems, all users have to sample the received signal with a sampling rate that is larger than or equal to the Nyquist rate, despite only a small fraction of the bandwidth (number of subcarriers) is allocated to each user. This paper proposes a low sampling rate receiver design for multi-antenna multi-user OFDM systems. With the aid of zero-forcing precoding, the sampling rate of the receiver can be reduced to 1/K of the Nyquist rate, where K is the number of users. The simulation results show that with a significant reduction in sampling rate, performance loss is insignificant and acceptable in terms of bit error rate, mutual information and peak-to-average power ratio.

A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories.

GPS (Global Positioning System) trajectories with low sampling rates are prevalent in many applications. However, current map matching methods do not perform well for low-sampling-rate GPS trajectories due to the large uncertainty between consecutive GPS points. In this paper, a collaborative map matching method (CMM) is proposed for low-sampling-rate GPS trajectories. CMM processes GPS trajectories in batches. First, it groups similar GPS trajectories into clusters and then supplements the missing information by resampling. A collaborative GPS trajectory is then extracted for each cluster and matched to the road network, based on longest common subsequence (LCSS) distance. Experiments are conducted on a real GPS trajectory dataset and a simulated GPS trajectory dataset. The results show that the proposed CMM outperforms the baseline methods in both, effectiveness and efficiency.

A Novel Energy-Efficient Approach for Human Activity Recognition.

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

Reliability of the Parabola Approximation Method in Heart Rate Variability Analysis Using Low-Sampling-Rate Photoplethysmography.

Photoplethysmographic signals are useful for heart rate variability analysis in practical ambulatory applications. While reducing the sampling rate of signals is an important consideration for modern wearable devices that enable 24/7 continuous monitoring, there have not been many studies that have investigated how to compensate the low timing resolution of low-sampling-rate signals for accurate heart rate variability analysis. In this study, we utilized the parabola approximation method and measured it against the conventional cubic spline interpolation method for the time, frequency, and nonlinear domain variables of heart rate variability. For each parameter, the intra-class correlation, standard error of measurement, Bland-Altman 95% limits of agreement and root mean squared relative error were presented. Also, elapsed time taken to compute each interpolation algorithm was investigated. The results indicated that parabola approximation is a simple, fast, and accurate algorithm-based method for compensating the low timing resolution of pulse beat intervals. In addition, the method showed comparable performance with the conventional cubic spline interpolation method. Even though the absolute value of the heart rate variability variables calculated using a signal sampled at 20 Hz were not exactly matched with those calculated using a reference signal sampled at 250 Hz, the parabola approximation method remains a good interpolation method for assessing trends in HRV measurements for low-power wearable applications.

Sampling unknown large networks restricted by low sampling rates.

Graph sampling plays an important role in data mining for large networks. Specifically, larger networks often correspond to lower sampling rates. Under the situation, traditional traversal-based samplings for large networks usually have an excessive preference for densely-connected network core nodes. Aim at this issue, this paper proposes a sampling method for unknown networks at low sampling rates, called SLSR, which first adopts a random node sampling to evaluate a degree threshold, utilized to distinguish the core from periphery, and the average degree in unknown networks, and then runs a double-layer sampling strategy on the core and periphery. SLSR is simple that results in a high time efficiency, but experiments verify that the proposed method can accurately preserve many critical structures of unknown large scale-free networks with low sampling rates and low variances.

Sound localization in an anisotropic plate using electret microphones.

Acoustic source localization without knowing the velocity profile in anisotropic plates is still one of the most challenging areas in this field. The current time-of-flight based approaches for localization in anisotropic media, are based on using six high sampling sensors. The number of sensors and the corresponding large amount of data, would make those methods inefficient in practical applications. Although there are many different non-time-of-flight based approaches such as machine learning, or soft computing based methods that can be used for localization with a less number of sensors, they are not as accurate as time-of-flight based techniques. In this article, a new approach which requires only four low sampling rate sensors to localize acoustic source in an anisotropic plate is proposed. In this technique, four electret low sampling rate sensors in two clusters are installed on the plate surface. The presented method uses attenuation analysis in a suitable frequency band to decrease the number of sensors. The approach is experimentally tested and verified on an airplane composite nose by applying artificially generated acoustic emissions (Hsu-Nielsen source). The results reveal that the accuracy of proposed technique depends on distinction of dominant frequency band. A stethoscope as a physical filter is employed to reduce the sensitivity of the technique and delineation of frequency band. The suggested technique improves the accuracy of localization prediction.

Locating the acoustic source in thin glass plate using low sampling rate data.

Acoustic source localization is an important step for structural health monitoring (SHM). There are many research studies dealing with localization based on high sampling rate data. In this paper, for the first time, acoustic source is localized on an isotropic plate using low sampling rate data. Previous studies have mainly used a cluster of specific sensors to easily record high sampling rate signals containing qualitative time domain features. This paper proposes a novel technique to localize the acoustic source on isotropic plates by simply implementing a combination of two simple electret microphones and Loci of k-Tuple Distances (LkTD) from the two sensors with low sampling rate data. In fact the method proposes substitution of previous methods based on solving the system of equations and increasing the number of sensors by implementing the selection of LkTD. Unlike most previous studies, estimation of time difference of arrival (TDOA) is based on the frequency properties of the signal rather than it's time properties. An experimental set-up is prepared and experiments are conducted to validate the proposed technique by prediction of the acoustic source location. The experimental results show that TDOA estimations based on low sampling rate data can produce more accurate predictions in comparison with previous studies. It is also shown that the selection of LkTD on the plate has noticeable effects on the performance of this technique.