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2022 IEEE International Conference on Signal Processing and Communications (SPCOM)最新文献

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Biquad filter based equalization for PMMA SI-POF links 基于双滤波器的PMMA SI-POF链路均衡
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840829
Raju D. Kamble, K. Appaiah
This paper proposes the use of an analog biquad filter as an equalizer for PMMA based step index plastic optical fiber (POF) links. We show both theoretically and experimentally that this approach achieves 100 Mbit/s over for 100 m of fiber. The material properties of the POF channel causes significant intersymbol interference SNR degradation for long link lengths. The use of DSP based equalization, while effective, imposes significant additional complexity. We propose the design and implementation of an analog biquad filter that is tuned using fiber modeling to effectively compensate the dispersive limitations. We show experimentally that the designed filter is able to successfully overcome the dispersion limits over a large range of fiber lengths and data rates.
本文提出了一种模拟双路滤波器作为基于PMMA的阶跃折射率塑料光纤(POF)链路的均衡器。我们在理论上和实验上都证明了这种方法可以在100米的光纤中实现100 Mbit/s的传输速度。POF信道的材料特性导致长链路长度下的码间干扰信噪比显著下降。使用基于DSP的均衡,虽然有效,施加显著额外的复杂性。我们提出了一种模拟双路滤波器的设计和实现,该滤波器使用光纤建模进行调谐,以有效地补偿色散限制。实验表明,所设计的滤波器能够在很大的光纤长度和数据速率范围内成功克服色散限制。
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引用次数: 0
Randomized Simultaneous Hard Thresholding Pursuit Algorithm for Multiple Measurement Vectors 多测量向量随机同步硬阈值追踪算法
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840810
Ketan Atul Bapat, M. Chakraborty
In this paper, we propose a new algorithm named Randomized Simultaneous Hard Thresholding Pursuit(RSHTP) for the multiple measurements vector (MMV) problem in compressed sensing. In the proposed algorithm, the gradient is calculated only with respect to few of the signals at each iteration that are chosen randomly. This reduces the computational cost which is significant when the problem size is large. A deterministic convergence analysis is carried out where we present theoretical guarantees using the restricted isometric property (RIP). Simulation studies show that the proposed algorithm enjoys at par performance even at a moderate rate of column selection in each iteration.
针对压缩感知中的多测量向量(MMV)问题,提出了随机同步硬阈值追踪(RSHTP)算法。在该算法中,每次迭代只对随机选择的少数信号计算梯度。这减少了计算成本,这在问题规模较大时非常重要。一个确定性的收敛分析进行了,我们提出了使用限制等距性质(RIP)的理论保证。仿真研究表明,即使在每次迭代中选择列的速度适中时,所提出的算法也具有相当好的性能。
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引用次数: 1
On the Error Exponents for Common Message Broadcasting over DMCs with Feedback 带反馈的dmc上公共消息广播的错误指数
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840799
A. Sarkar, B. Dey, S. R. Pillai
We investigate the achievable probability of error in a communication setup where a single transmitter broadcasts an common message sequence to a set of receivers. The communication is aided by the availability of perfect causal feedback of all the received symbols to the encoder. Two types of schemes are considered: in the first model, the encoder and the decoders have to agree in advance on the sequence number of the intended message, whereas the decoders should also Figure out the sequence number from the received symbols in the second model. The former is called coordinated message transmission, and latter is named streaming block transmission. The challenge faced in both models is to appropriately synchronize the independent receivers, while leveraging the boost in error probability due to feedback. We propose error exponents, which are optimal for a class of broadcast channels under coordinated message transmission. We propose an achievable exponent under streaming block transmission. These results extend the best known single receiver results.
我们研究了在单个发射器向一组接收器广播公共消息序列的通信设置中可实现的错误概率。所有接收到的符号对编码器的完美因果反馈的可用性有助于通信。考虑了两种方案:在第一种模型中,编码器和解码器必须事先就预期消息的序列号达成一致,而在第二种模型中,解码器还需要从接收到的符号中计算出序列号。前者称为协调消息传输,后者称为流块传输。在这两种模型中面临的挑战是适当地同步独立接收器,同时利用反馈导致的错误概率的提高。我们提出的误差指数对于一类广播信道在协调消息传输下是最优的。我们提出了一个在流块传输下可实现的指数。这些结果扩展了最著名的单接收器结果。
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引用次数: 0
Deep Learning for THz Channel Estimation and Beamforming Prediction via Sub-6GHz Channel 基于深度学习的太赫兹信道估计和Sub-6GHz波束形成预测
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840844
Sagnik Bhattacharya, Abhishek K. Gupta
An efficient channel estimation is of vital importance to help THz communication systems achieve their full potential. Conventional uplink channel estimation methods, such as least square estimation, are practically inefficient for THz systems because of their large computation overhead. In this paper, we propose an efficient convolutional neural network (CNN) based THz channel estimator that estimates the THz channel factors using uplink sub-6GHz channel. Further, we use the estimated THz channel factors to predict the optimal beamformer from a pre-given codebook, using a dense neural network. We not only get rid of the overhead associated with the conventional methods, but also achieve near-optimal spectral efficiency rates using the proposed beamformer predictor. The proposed method also outperforms deep learning based beamformer predictors accepting THz channel matrices as input, thus proving the validity and efficiency of our sub-6GHz based approach.
有效的信道估计对于帮助太赫兹通信系统充分发挥其潜力至关重要。传统的上行信道估计方法,如最小二乘估计,由于其巨大的计算开销,对于太赫兹系统实际上是低效的。在本文中,我们提出了一种基于卷积神经网络(CNN)的高效太赫兹信道估计器,用于估计上行链路6ghz以下信道的太赫兹信道因子。此外,我们利用估计的太赫兹信道因子,利用密集神经网络从预先给定的码本中预测最佳波束形成器。我们不仅摆脱了与传统方法相关的开销,而且使用所提出的波束形成器预测器获得了接近最佳的频谱效率。提出的方法也优于基于深度学习的波束形成器预测器,接受太赫兹信道矩阵作为输入,从而证明了我们基于sub-6GHz方法的有效性和效率。
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引用次数: 2
Intelligent Reflecting Surface Assisted Terahertz Communications 智能反射面辅助太赫兹通信
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840765
M. Kumar, S. Sharma, K. Deka, V. Bhatia
Terahertz (THz) band communication is a promising technology for the 6G wireless networks. However, due to very high spread attenuation and molecular absorption, THz frequencies provide a limited coverage area and need novel solutions to overcome these constraints. Further, intelligent reflecting surfaces (IRS) is introduced to enhance the coverage area by reconfiguring the wireless propagation environment. Therefore, in this paper, an IRS-assisted THz system is designed and analyzed and referred to as IRS-TH system. The proposed IRS-TH significantly improves bit error rate (BER) performance by passive beamforming at the IRS panel. Impact of IRS-TH parameters such as different phase shifting methods, the position of IRS panel, and line-of-sight path is studied. Exhaustive simulation results show that the proposed IRS-TH can significantly enhance BER and sum-rate performance as compared to conventional THz system without IRS.
太赫兹(THz)频段通信是6G无线网络中很有前途的技术。然而,由于非常高的扩散衰减和分子吸收,太赫兹频率提供了有限的覆盖区域,需要新的解决方案来克服这些限制。此外,引入智能反射面(IRS),通过重新配置无线传播环境来增强覆盖面积。因此,本文设计并分析了irs辅助太赫兹系统,简称IRS-TH系统。提出的IRS- th通过在IRS面板上进行无源波束形成,显著提高了误码率(BER)性能。研究了不同移相方式、IRS面板位置、视距路径等IRS- th参数的影响。详尽仿真结果表明,与不含IRS的传统太赫兹系统相比,本文提出的IRS- th系统能显著提高误码率和和率性能。
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引用次数: 3
Analysis of EEG for Parkinson’s Disease Detection 脑电图对帕金森病的检测分析
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840776
Darshil Shah, K. G. Gopan, N. Sinha
Parkinson’s Disease (PD) is a disorder of the central nervous system which affects movement, often including tremors. Nerve cell damage in the brain causes dopamine levels to drop which gradually degrades the functionality of the brain. Since PD is a neurodegenerative ailment, Electroencephalography (EEG) signal are used for early detection of Parkinson’s Disease. EEG being non-linear and non-stationary manual analysis is not only time consuming but prone to error. To detect PD, two methods are discussed in this paper: (1) CNN for EEG images and (2) k-nearest neighbors for manually extracted features from EEG signals. The proposed methodology is applied to publicly available datasets (1) University of New Mexico (UNM) (27 PD patients and 27 controls) and (2) Iowa (14 PD patients and 14 controls). Data from New Mexico is used to evaluate the performance of the model using k-fold cross-validation method and data from Iowa is used for out-of-sample evaluation. Mean test accuracy on the mentioned datasets reaches to 88.51% and 87.6% respectively making an improvement of 3.11% and 1.9% for UNM and Iowa dataset, as compared to the current state-of-the-art accuracy.
帕金森氏症(PD)是一种影响运动的中枢神经系统紊乱,通常包括震颤。大脑中的神经细胞损伤会导致多巴胺水平下降,从而逐渐降低大脑的功能。由于帕金森病是一种神经退行性疾病,脑电图(EEG)信号可用于帕金森病的早期检测。脑电图是非线性和非平稳的,人工分析不仅费时而且容易出错。为了检测PD,本文讨论了两种方法:(1)对脑电信号进行CNN检测,(2)对脑电信号进行k近邻检测。提出的方法应用于公开可用的数据集:(1)新墨西哥大学(UNM)(27名PD患者和27名对照)和(2)爱荷华州(14名PD患者和14名对照)。来自新墨西哥州的数据使用k-fold交叉验证方法评估模型的性能,来自爱荷华州的数据用于样本外评估。在上述数据集上的平均测试准确率分别达到了88.51%和87.6%,与目前最先进的准确率相比,UNM和Iowa数据集的平均测试准确率分别提高了3.11%和1.9%。
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引用次数: 3
Robustness of DAS Beamformer Over MVDR for Replay Attack Detection On Voice Assistants 基于MVDR的DAS波束形成器对语音助手重放攻击检测的鲁棒性
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840757
Piyushkumar K. Chodingala, Shreya S. Chaturvedi, Ankur T. Patil, H. Patil
Due to the increased use of Virtual Assistants (VAs) for various personal usage, the safety of VAs from various spoofing attacks is utmost important. To that effect, we investigate the significance of Delay-and-Sum (DAS) beamformer over state-of-the-art Minimum Variance Distortionless Response (MVDR) along with Teager Energy Operator (TEO)-based features for replay Spoof Speech Detection (SSD) on VAs. Conventional DAS method is known to suppress the additive noise component and retains the reverberation effect (i.e., an important acoustic cue for replay SSD). On the contrary, MVDR used for Distant Speech Recognition (DSR) suppresses the reverberation effect and additive noise. Hence, MVDR is not suitable choice for replay SSD, whereas DAS can be exploited for replay SSD in VAs. Furthermore, suppression of reverberation due to the DAS vs. MVDR beamformer is analyzed via TEO profile. The experimental validation is done on recently released Realistic Replay Attack Microphone-Array Speech Corpus (ReMASC) and its DAS vs. MVDR beamformed versions. Furthermore, Teager Energy Cepstral Coefficients (TECC) feature set is employed as it is recently shown to capture the characteristics of reverberation for replay SSD task. For performance comparison, Constant-Q Cepstral Coefficients (CQCC), Linear Frequency Cepstral Coefficients (LFCC), and Mel Frequency Cepstral Coefficients (MFCC) feature sets along with Gaussian Mixture Model (GMM) classifier are used. In particular, TECC-GMM SSD system on DAS gave relative reduction in %EER by 13.12% and 43.16% for Eval set as compared to the original ReMASC and its MVDR beamformed version, respectively. Finally, relative significance of TECC w.r.t. practical deployment is shown through latency analysis of various SSD systems for VAs.
由于各种个人用途越来越多地使用虚拟助理(VAs),因此虚拟助理免受各种欺骗攻击的安全性至关重要。为此,我们研究了延迟和和(DAS)波束形成器相对于最先进的最小方差无失真响应(MVDR)的重要性,以及基于Teager能量算子(TEO)的特征,用于VAs上的重放欺骗语音检测(SSD)。已知传统的DAS方法可以抑制加性噪声成分并保留混响效应(即,重放SSD的重要声学线索)。相反,用于远距离语音识别(DSR)的MVDR可以抑制混响效应和加性噪声。因此,MVDR不适合用于重放SSD,而DAS可以用于VAs中的重放SSD。此外,通过TEO分析了DAS与MVDR波束形成器对混响的抑制。在最近发布的现实重播攻击麦克风阵列语音语料库(ReMASC)及其DAS与MVDR波束形成版本上进行了实验验证。此外,Teager能量倒谱系数(TECC)特征集被采用,因为它最近被证明可以捕捉重放SSD任务的混响特征。为了进行性能比较,使用了恒定q倒谱系数(CQCC),线性频率倒谱系数(LFCC)和Mel频率倒谱系数(MFCC)特征集以及高斯混合模型(GMM)分类器。特别是,DAS上的TECC-GMM SSD系统与原始ReMASC及其MVDR波束形成版本相比,Eval集的%EER分别相对降低了13.12%和43.16%。最后,通过对各种SSD系统的延迟分析,说明了TECC w.r.t.实际部署的相对意义。
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引用次数: 1
DOA Estimation using Multiclass-SVM in Spherical Harmonics Domain 球面谐波域多类支持向量机的DOA估计
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840848
Priyadarshini Dwivedi, Gyanajyoti Routray, R. Hegde
Direction of arrival (DOA) estimation is still a challenging and fundamental problem in acoustic signal processing. This paper proposes a new method for DOA estimation that utilizes the support vector machine (SVM) based classification. The source signal is recorded by the spherical microphone array (SMA) and decomposed into the spherical harmonics domain. The phase and the magnitude features are calculated from the spherical harmonics (SH) decomposed signals. A multiclass support vector machine (M-SVM) algorithm is implemented to classify these phase and magnitude features to the DOA classes. Since the SVM is a non-probabilistic and deterministic model, it is computationally faster and highly reduced complexity than the neural network-based learning models. Extensive simulations are conducted for the performance evaluation of the proposed method. It is observed that the proposed model provides robust DOA estimates at various signal-to-noise ratios (SNR) and reverberation time. Performance evaluated in terms of the root mean square error (RMSE) provides interesting results motivating the use of the proposed model in practical applications.
到达方向估计仍然是声信号处理中一个具有挑战性和基础性的问题。本文提出了一种基于支持向量机(SVM)分类的DOA估计方法。源信号由球形传声器阵列(SMA)记录并分解成球谐波域。从球谐波分解后的信号中计算出相位和幅值特征。采用多类支持向量机(M-SVM)算法对这些相位和幅度特征进行DOA分类。由于支持向量机是一种非概率和确定性模型,因此与基于神经网络的学习模型相比,它的计算速度更快,复杂度也大大降低。为了对所提出的方法进行性能评估,进行了大量的仿真。观察到,所提出的模型在各种信噪比(SNR)和混响时间下提供了鲁棒的DOA估计。根据均方根误差(RMSE)评估的性能提供了有趣的结果,激励在实际应用中使用所提出的模型。
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引用次数: 1
A two-stage classification strategy to reduce the effect of wrist orientation in surface myoelectric pattern recognition 一种两阶段分类策略以减少腕关节方向对表面肌电模式识别的影响
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840809
Pratap Kumar Koppolu, K. Chemmangat
The myoelectric Pattern Recognition (PR) collects surface Electromyographic (sEMG) signals using the electrodes placed on the upper limb of the amputee. Then it recognizes patterns in those signals based on the intended limb movement using signal processing and machine learning techniques. The performance of the PR system should be robust against multiple factors, like wrist orientation, muscle force level changes, limb position changes, and electrode shifts. This paper demonstrates how performance is affected by wrist orientation and proposes a method to overcome those effects. A two-stage classification technique with Dynamic Time Warping (DTW) as the classifier, along with features extracted from a three-axis accelerometer and six-channel sEMG sensors, is proposed here. Accelerometer features are used to identify the wrist orientation, and sEMG features are used to classify the various limb movements performed by ten subjects. The performance of the proposed method was measured by classification error and classification accuracy of limb movements. The corresponding results were compared with the state-of-the-art techniques.
肌电模式识别(PR)通过放置在截肢者上肢的电极收集表面肌电图(sEMG)信号。然后利用信号处理和机器学习技术,根据预期的肢体运动来识别这些信号中的模式。PR系统的性能应该对多种因素具有鲁棒性,如手腕方向、肌肉力量水平变化、肢体位置变化和电极移动。本文演示了手腕方向如何影响性能,并提出了一种克服这些影响的方法。本文提出了一种以动态时间扭曲(DTW)作为分类器的两阶段分类技术,以及从三轴加速度计和六通道肌电信号传感器中提取的特征。加速度计特征用于识别手腕方向,肌电图特征用于对10个受试者进行的各种肢体运动进行分类。用肢体运动的分类误差和分类精度来衡量该方法的性能。相应的结果与最先进的技术进行了比较。
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引用次数: 1
Energy-Efficient UAV Trajectory Planning in Rechargeable IoT Networks 可充电物联网网络中节能无人机轨迹规划
Pub Date : 2022-07-11 DOI: 10.1109/SPCOM55316.2022.9840770
Aditya Singh, Surender Redhu, R. Hegde
Maintaining adequate energy in low-powered Internet of Things (IoT) nodes is crucial for developing several applications like smart homes, autonomous industries, etc. In this context, energy harvesting plays an essential role in improving the operational lifetime of the IoT nodes. Unmanned Aerial Vehicles (UAVs) have become a feasible option for reaching out to the low-powered IoT nodes in remote areas and recharging them by acting as efficient energy transmitter units. However, ensuring a sustainable and regular supply of power to these IoT nodes mainly depends on the trajectories of UAVs. In this context, the UAV trajectory optimization problem is first formulated. Subsequently, an energy-efficient UAV route planning algorithm (UAV-RPA) is proposed to generate the UAV trajectory to recharge the IoT nodes. The proposed algorithm minimizes the UAV-travel time by selecting an optimal sequence of IoT nodes such that the UAV trajectory length is minimized. Moreover, extensive simulations are also conducted under various network scenarios to evaluate the performance of the route planning algorithm. It is observed that the proposed UAV-RPA generates a minimal length UAV trajectory over an IoT network when compared to other UAV trajectory generation algorithms. Also, the average residual energy per IoT node in the network is also improved. This, in turn, improves the operational lifetime of self-sustaining UAV-powered IoT networks.
在低功耗物联网(IoT)节点中保持足够的能量对于开发智能家居、自主工业等多种应用至关重要。在这种情况下,能量收集在提高物联网节点的运行寿命方面起着至关重要的作用。无人驾驶飞行器(uav)已经成为一种可行的选择,可以接触到偏远地区的低功耗物联网节点,并通过充当高效的能量传输单元为其充电。然而,确保这些物联网节点的可持续和定期供电主要取决于无人机的轨迹。在此背景下,首先提出了无人机轨迹优化问题。在此基础上,提出了一种高效节能的无人机航路规划算法(UAV- rpa),用于生成无人机航路给物联网节点充电。该算法通过选择最优的物联网节点序列,使无人机的飞行轨迹长度最小化,从而使无人机的飞行时间最小化。此外,还在各种网络场景下进行了大量的仿真,以评估路由规划算法的性能。观察到,与其他无人机轨迹生成算法相比,所提出的无人机- rpa在物联网网络上生成最小长度的无人机轨迹。此外,网络中每个物联网节点的平均剩余能量也得到了提高。这反过来又提高了自持无人机驱动的物联网网络的运行寿命。
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引用次数: 2
期刊
2022 IEEE International Conference on Signal Processing and Communications (SPCOM)
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