Component-Dependent Independent Component Analysis for Time-Sensitive Applications

Wu, Huanzhuo; Shen, Yunbin; Zhang, Jiajing; Tsokalo, Ievgenii; Salah, Hani; Fitzek, H. P. Frank

doi:10.1109/icc40277.2020.9149432

Cited by 9 publications

(10 citation statements)

References 12 publications

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“…Modern wireless communication systems operate at a data rate of hundreds of megabits per second [16]- [18] with complex-valued signals. Furthermore, IoT applications usually require strict real-time processing [7], [8]. Therefore, it is crucial to design a high-throughput ICA preprocessor for these systems.…”

Section: B Related Workmentioning

confidence: 99%

“…Furthermore, the ICA algorithm is applied to separate multiple superimposed signals in order to identify the cyclic features of each transmitted signal on a cognitive radio (CR) system [6]. Recently, it is proposed in [7], [8] that the ICA is used in a temporal-critical industrial internet of things (IoT) application for anomaly detection or identification. The source signals are expected to be extracted with real-time in these various using scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…In short, the ICA preprocessors described in the literature support only real-valued signals and operate at low processing speeds. However, other applications require higher data rates such as modern wireless communication systems that could have a data rate of hundreds of megabits per second [16]- [18] with complex-valued signals or time-sensitive applications that require ultra-low latency such as IoT require strict real-time processing [7], [8]. Therefore, it is crucial to design a high-throughput ICA preprocessor that can be used in modern wireless communication or IoT systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Configurable Independent Component Analysis Preprocessing Accelerator

Lu¹,

Shen²,

Fouda³

et al. 2022

Preprint

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Independent component analysis (ICA) has been used in many applications, including self-interference cancellation in in-band full-duplex wireless communication systems. This paper presents a high-throughput and highly efficient configurable preprocessor for the ICA algorithm. The proposed ICA preprocessor has three major components for centering, for computing the covariance matrix, and for eigenvalue decomposition (EVD). The circuit structures and operational flows for these components are designed both in individual and in joint sense. Specifically, the proposed preprocessor is based on a high-performance matrix multiplication array (MMA) that is presented in this paper. The proposed MMA architecture uses time-multiplexed processing so that the efficiency of hardware utilization is greatly enhanced. Furthermore, the novel processing flow of the proposed preprocessor is highly optimized, so that the centering, the calculation of the covariance matrix, and EVD are conducted in parallel and are pipelined. Thus, the processing throughput is maximized while the required number of hardware elements can be minimized. The proposed ICA preprocessor is designed and implemented with a circuit design flow, and performance estimates based on the post-layout evaluations are presented in this paper. This paper shows that the proposed preprocessor achieves a throughput of 40.7 kMatrices per second for a complexity of 73.3 kGE. Compared with prior work, the proposed preprocessor achieves the highest processing throughput and best efficiency.

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Section: B Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Configurable Independent Component Analysis Preprocessing Accelerator

Lu¹,

Shen²,

Fouda³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Another school is ICA algorithms, working directly with input data and require only average hardware capability. Typical ICA algorithms are FastICA [6], InfoMax [7], and CdICA [8]. They are free from the constraints of ML-based solutions, so more feasible to fit an in-network processing scheme.…”

Section: Related Workmentioning

confidence: 99%

“…A natural idea is to first transfer all data to a centralized node; when all data are received, a sort of Blind Source Separation (BSS) [5] algorithm is applied to separate mixed data. BSS candidates include Independent Component Analysis (ICA)-based methods [6]- [8] or neural network-based methods [9], [10]. However, forwarding and then analyzing could delay critical decision-making actions due to i) possibly long waiting time of transferring the data, and ii) possibly long execution time of running the algorithm on a single node.…”

Section: Introductionmentioning

confidence: 99%

In-Network Processing Acoustic Data for Anomaly Detection in Smart Factory

Wu¹,

Shen²,

Xiao³

et al. 2021

Preprint

Self Cite

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Modern manufacturing is now deeply integrating new technologies such as 5G, Internet-of-things (IoT), and cloud/edge computing to shape manufacturing to a new level -Smart Factory. Autonomic anomaly detection (e.g., malfunctioning machines and hazard situations) in a factory hall is on the list and expects to be realized with massive IoT sensor deployments. In this paper, we consider acoustic data-based anomaly detection, which is widely used in factories because sound information reflects richer internal states while videos cannot; besides, the capital investment of an audio system is more economically friendly. However, a unique challenge of using audio data is that sounds are mixed when collecting thus source data separation is inevitable. A traditional way transfers audio data all to a centralized point for separation. Nevertheless, such a centralized manner (i.e., data transferring and then analyzing) may delay prompt reactions to critical anomalies. We demonstrate that this job can be transformed into an in-network processing scheme and thus further accelerated. Specifically, we propose a progressive processing scheme where data separation jobs are distributed as microservices on intermediate nodes in parallel with data forwarding. Therefore, collected audio data can be separated 43.75% faster with even less total computing resources. This solution is comprehensively evaluated with numerical simulations, compared with benchmark solutions, and results justify its advantages.

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Y-Net: A Dual Path Model for High Accuracy Blind Source Separation

Tömösközi

et al. 2020

2020 IEEE Globecom Workshops (GC WKSHPS

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Component-Dependent Independent Component Analysis for Time-Sensitive Applications

Cited by 9 publications

References 12 publications

Configurable Independent Component Analysis Preprocessing Accelerator

Configurable Independent Component Analysis Preprocessing Accelerator

In-Network Processing Acoustic Data for Anomaly Detection in Smart Factory

Y-Net: A Dual Path Model for High Accuracy Blind Source Separation

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