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A Low-Complexity LSTM Network to Realize Multibeam Beamforming

A Low-Complexity LSTM Network to Realize Multibeam Beamformi...

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IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

作者： Hansaka Aluvihare Carina Shanahan Sirani M. Perera Sivakumar Sivasankar Umesha Kumarasiri Arjuna Madanayake Xianqi Li Department of Mathematics Embry-Riddle Aeronautical University Daytona Beach FL USA Department of Electrical and Computer Engineering Florida International University Miami FL USA Department of Mathematics and Systems Engineering Florida Institute of Technology Melbourne FL USA

ISBN: (数字)9798350351118

ISBN: (纸本)9798350351125

Massive data structures can be embedded in the form of weight matrices, enabling to design of neural networks with low-complexity learning algorithms. These data can be organized in rows of matrices, containing progressive phase shifts for a specific beam, along with input and output vectors consisting of time-domain signals. In our previous work, we have identified that multi-beam beamformers based on true-time-delays (TTDs) can be mathematically formulated as the elements of delay Vandermonde matrices (DVM). Thus, by adopting a frequency domain variable, we can express TTDs of time delay data in terms of elements of the DVM. Learning from prior work, we propose to present a low-complexity neural network to realize multibeam beamforming leveraging a novel LSTM network. The goal of our work is to reduce the complexity of the multibeam beamforming algorithm from ${\mathcal{O}}({N^2}L)$ to ${\mathcal{O}}({N^s}L)$, where 1 < s < 2, by imposing factorization of the DVM in an LSTM network having L layers.

关键词： Wireless communication Time-frequency analysis Array signal processing Neural networks Vectors Complexity theory Delays Sparse matrices Time-domain analysis Long short term memory

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PD-Refiner: An Underlying Surface Inheritance Refiner with Adaptive Edge-Aware Supervision for Point Cloud Denoising 24

PD-Refiner: An Underlying Surface Inheritance Refiner with A...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Chengwei Zhang, Xueyi Yue, Xianghu Lao, Mingrui Jiang, Tao Wang, Jiawei Zhang, Fubo Chen, Longyong National Key Laboratory of Microwave Imaging Aerospace Information Research Institute Chinese Academy of Sciences Beijing China Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China Department of Electrical and Computer and Engineering National University of Singapore Singapore Singapore National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China School of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9798400706868

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity. © 2024 ACM.

关键词： edge-aware supervision

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Risk Sensitivity and Entropy Regularization in Prototype-based Learning

Risk Sensitivity and Entropy Regularization in Prototype-bas...

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Mediterranean Conference on Control and Automation (MED)

作者： Christos Mavridis Erfaun Noorani John S. Baras Department of Electrical and Computer Engineering and the Institute for Systems Research University of Maryland College Park USA

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Prototype-based learning methods have been extensively studied as fast, recursive, data-driven, interpretable, and robust learning algorithms. We study the effect of entropy regularization in prototype-based learning regarding (i) robustness with respect to the dataset and the initial conditions, and (ii) the generalization properties of the learned representation. A duality relationship, with respect to a Legendre-type transform, between free energy and Kulback-Leibler divergence measures, is used to show that entropy-regularized prototype-based learning is connected to exponential objectives associated with risk-sensitive learning. We use these results to incentivize the development of entropy-regularized prototype-based learning algorithms by highlighting its properties, including (i) memory and computational efficiency, (ii) gradient-free training rules, and (iii) the ability to simulate an annealing optimization process that results in progressively growing competitive-learning neural network architectures.

关键词： Training Sensitivity Neural networks Memory management Energy measurement Transforms Simulated annealing

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Inverse Kalman Filtering for systems with Correlated Noises

Inverse Kalman Filtering for Systems with Correlated Noises

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IEEE Conference on Decision and Control

作者： Yibei Li Xiaoming Hu Bo Wahlberg Lihua Xie School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Mathematics Division of Optimization and Systems Theory KTH Royal Institute of Technology Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Sciencem KTH Royal Institute of Technology Sweden

This paper focuses on two inverse problems of the Kalman filter in which the process and measurement noises are correlated. The unknown covariance matrix in a stochastic system is reconstructed from observations of its posterior beliefs. For the standard inverse Kalman filtering problem, a novel duality-based formulation is proposed, where a well-defined inverse optimal control (IOC) problem is solved instead. Identifiability of the underlying model is proved, and a least squares estimator is designed that is statistically consistent. The time-invariant case using the steady-state Kalman gain is further studied. Since this inverse problem is ill-posed, a canonical class of covariance matrices is constructed, which can be uniquely identified from the dataset with asymptotic convergence. Finally, the performances of the proposed methods are illustrated by numerical examples.

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Bi-Level Nonstationary Kernels for Online Gaussian Process Regression 19

Bi-Level Nonstationary Kernels for Online Gaussian Process R...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： He, Hans J. Koppel, Alec Bedi, Amrit Singh Farhood, Mazen Stilwell, Daniel J. Virginia Tech Bradley Department of Electrical and Computer Engineering BlacksburgVA24060 United States Jp Morgan Ai Research New York United States Institute of Systems Research The University of Maryland College ParkMD20742 United States Virginia Tech Kevin T. Crofton Department of Aerospace and Ocean Engineering BlacksburgVA24061 United States

ISBN: (纸本)9798350320695

In this work, we consider realtime mapping of spatial phenomena using Gaussian processes (GP). For the case that the spatial field changes in variability over different areas, stationary GPs whose kernel hyperparameters are constant may be insufficient in capturing local behaviors. To accurately model and represent data with evolving variability, we propose to use a Gibbs kernel where the length scale is allowed to vary over space. The length scale is modeled as a secondary Gaussian process, and we formulate a maximum a posteriori solution to estimate training values for the length scale GP. Combining this kernel with online sparse methods and nearest neighbor approximations allows us to control computational complexity involved in training and inference, making our approach suitable for real-time applications. The resulting bi-level Gaussian process framework efficiently processes streaming data and maintains good prediction accuracy as measured by standardized mean squared error. Empirical analysis on a bathymetric dataset that has been acquired by an autonomous underwater vehicle suggests that our framework produces comparable predictive performance with respect to recent online GP regression methods while maintaining acceptable computational speed for streaming applications. © 2023 IEEE.

关键词： Autonomous underwater vehicles

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Innovative Data Encryption Techniques using AI for Wireless Sensor Actuator Network Security

Innovative Data Encryption Techniques using AI for Wireless ...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： M Dharmateja Prabha K.P. Rama N Asha P Nithya S. Lalitha P Manojkumar Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of computer science and Engineering Arunai Engineering college Tiruvannamalai Tamil Nadu India Department of Electrical and Electronics Engineering Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India

Wireless Sensor Actuator Networks (WSANs) are sensitive to cyber threats due to present static encryption techniques. Adaptive security methods are required in WSAN settings to reduce evolving dangers and maintain data integrity. The paper introduces a novel method for encrypting WSAN data using AI and machine learning. Long Short-Term Memory (LSTM) networks process data, Genetic Algorithms (GAs) optimize encryption, and Isolation Forests detect threats in real-time. The proposed system achieved remarkable performance, with scores of 98% data integrity, 96% error repair, and 95% threat detection accuracy. Encryption rates reach their max at 200 Mbps, with optimal resource usage (30% CPU, 25% RAM) and a 5% false positive rate. The proposed AI-driven security architecture improves data integrity, encryption speed, and overall security efficacy in WSANs, surpassing existing methods. The paper overcomes the limitations of static encryption methods and proposes a proactive protection mechanism against developing cyber threats, making a substantial contribution to WSAN security improvement.

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Granger Causal Inference from Spiking Observations via Latent Variable Modeling

Granger Causal Inference from Spiking Observations via Laten...

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Asilomar Conference on Signals, systems & computers

作者： Sahar Khosravi Anuththara Rupasinghe Behtash Babadi Department of Electrical & Computer Engineering Institute for Systems Research University of Maryland College Park MD

Extracting directional connectivity in a neuronal ensemble from spiking observations is a key challenge in understanding the circuit mechanisms of brain function. Existing methods proceed in two stages, by first estimating the latent processes that govern spiking, followed by characterizing connectivity using said estimates. As such, the extracted networks in the second stage are highly sensitive to the accuracy of the estimates in the first stage. In this work, we introduce a framework to directly extract Granger causal links from spiking observations, without requiring intermediate time-domain estimation, by explicitly modeling the endogenous and exogenous latent processes that underlie spiking activity. Our proposed method integrates several techniques such as point processes, state-space modeling and Pólya-Gamma augmentation. We demonstrate the utility of our proposed approach using simulated data and application to real data from the rat brain during sleep.

关键词： computers Process modeling Sociology External stimuli Estimation Rats Brain modeling

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Towards No Penalty Control Hazard Handling

Towards No Penalty Control Hazard Handling

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Midwest Symposium on Circuits and systems (MWSCAS)

作者： Linknath Surya Balasubramanian Maher Rizkalla John J. Lee Trond Ytterdal Mukesh Kumar Department of Electrical and Computer Engineering Indiana University Purdue University Indianapolis Indianapolis USA Department of Electronic Systems Norwegian University of Science and Technology Trondheim Norway Department of Electrical Engineering Indian Institute of Technology Indore Indore India

Achieving higher throughput is one of the most important requirements of a modern microcontroller. It is therefore not affordable for it to waste a considerable number of clock cycles in branch mispredictions. This paper proposes a novel mechanism that makes microcontrollers forgo branch predictors and thereby eliminate branch mispredictions. Further, we synthesized the proposed design with 7nm FinFET process and compared its latency with other designs to make sure that the microcontroller's operating frequency is not degraded by using this design. The proposed technique is implemented as five different modules that work together to forward required operands, resolve branches without prediction, and calculate the next instruction's address in the first stage of an in-order five stage pipelined micro-architecture. Since the address of successive instruction to a control transfer instruction is calculated in the first stage of pipeline, branch prediction is no longer necessary, thereby eliminating the clock cycle penalties occurred when using a branch predictor. The designed system was tested using test-bench with modifications to make the test-code compatible with the designed architecture. The designed architecture was able to successfully calculate the address of next correct instruction and fetch it without any wastage of clock cycles. Moreover, the latency of instruction fetch stage of pipeline integrated with the proposed architecture is 330 ps (3 GHz approx.) which is more than the operating frequencies of microcontrollers. Therefore, the proposed design can be integrated in high performance microcontrollers.

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Chatbots and Customer Service: Enhancing Marketing Management with AI Technologies

Chatbots and Customer Service: Enhancing Marketing Managemen...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rajchandar, K. Hari Krishna, Somanchi Khalil Wani, Showkat Pathak, Supriya Jain, Amit Kumar Naveen Kumar, C.M. Sr University School of Cs & Ai Telangana Warangal India Vignana Bharathi Institute of Technology Department of Business Management Telangana State 501301 India Saudi Electronic University College of Administrative and Financial Sciences Department of Business Administration Riyadh Saudi Arabia Oriental University Faculty of Management and Commerce Madhya Pradesh Indore India Poornima University Department of Electrical & Electronics Engineering Rajasthan JaipurIS-2027-2031 India Malnad College of Engineering Department of Computer Science & Business Systems Karnataka Hassan573202 India

ISBN: (纸本)9798331508685

By increasing engagement, expediting client contacts, and boosting satisfaction through AI-driven solutions, chatbot integration in customer service has revolutionised marketing management. With an emphasis on how model assessment techniques - such as accuracy, precision, recall, and F1 score - can be applied to enhance chatbot efficacy in customer service scenarios, this paper investigates the role of machine learning models in optimising chatbot performance. In order to appropriately meet a variety of consumer demands, this research offers perspectives into the selection, training, and evaluation of chatbot models by analysing a range of AI technologies, such as natural language processing (NLP) and predictive analytics. The influence of chatbots on customer loyalty and retention is highlighted in this article, which also explores the benefits and limitations of utilising AI in real-time consumer interactions. According to research, well-considered ML models can produce more precise and sympathetic answers, which enhances brand perception and opens up new channels for CRM in marketing plans. © 2025 IEEE.

关键词： Sales

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SRAM Process and Debug Sensor

SRAM Process and Debug Sensor

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Anoop Gopinath Trond Ytterdal John Lee Maher Rizkalla Mukesh Kumar Department of Electronic Systems Norwegian University of Science and Technology Trondheim Norway Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indianapolis USA Department of Electrical Engineering Indian Institute of Technology Indore Indore India

In this paper, a RAZOR-based in-situ monitoring scheme is described for process variation characterization and silicon debug in SRAM arrays. The scheme can identify whether the datapath being monitored has slowed-down due to process variation or hard errors. This information can be used to estimate the impact of process variation or hard errors on memory access time setting critical path in SRAM arrays. Pre-silicon characterized datapath delay via selectable data delay lines referred to as data TRIMs are obtained via SPICE simulations. These pre-silicon data TRIMs, along with post-silicon error output can be used to estimate the datapath slow-down with a margin of error. The margin of error can be reduced by using finer granularity in data TRIMs but comes at the cost of area and power overheads. The scheme can be shared across multiple SRAM arrays, and can also be used in a column-by-column manner to test memory access time with respect to individual bitcells in an array. This provides a deep level of visibility into SRAM macros post-silicon for debug purposes too. The scheme can estimate the slow-down on memory access time setting critical path in global TT corner with up to 99.2 % accuracy.

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