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Graph Learning from Gaussian and Stationary Graph signals

Graph Learning from Gaussian and Stationary Graph Signals

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Andrei Buciulea Antonio G. Marques Dept. of Signal Theory and Communications King Juan Carlos University Madrid Spain

Graphs have become pervasive tools to represent information and datasets with irregular support. However, in many cases, the underlying graph is either unavailable or naively obtained, calling for more advanced methods to its estimation. Indeed, graph topology inference methods that estimate the network structure from a set of signal observations have a long and well established history. By assuming that the observations are both Gaussian and stationary in the sought graph, this paper proposes a new scheme to learn the network from nodal observations. Consideration of graph stationarity overcomes some of the limitations of the classical Graphical Lasso algorithm, which is constrained to a more specific class of graphical models. On the other hand, Gaussianity allows us to regularize the estimation, requiring less samples than in existing graph stationarity-based approaches. While the resultant estimation (optimization) problem is more complex and non-convex, we design an alternating convex approach able to find a stationary solution. Numerical tests with synthetic and real data are presented, and the performance of our approach is compared with existing alternatives.

关键词： Graphical models Network topology Estimation signal processing algorithms signal processing Markov processes Topology

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A Novel Lightweight Block Cipher for Robust Data Protection in IoT Devices

A Novel Lightweight Block Cipher for Robust Data Protection ...

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Innovations in High Speed Communication and signal processing (IHCSP), International conference on

作者： Manish Nagpal Umakanth S Prutha P Kulkarni K.S. Bhuvaneshwari Rajat Saini Srisathirapathy S Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka ENTC Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mech Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Due to the weak processing power and energy constraint associated with IoT devices, Data security and privacy are major issues enormously important for the growing environment of IoT. Cryptographic method is incompatible with the Internet of Things (IoT) as this concept is characterized by too high resource demand. In this work, a new lightweight block cypher, designed for the IoT devices is introduced which is supposed to provide proper protection to the data. There are advantages of the suggested cypher that reduces computational time, energy requirements and memory usage while promoting efficiency as well as high security. In order to avoid usual cryptographic attacks such as differential and linear, the cipher employs advanced encryption techniques together with an enhanced key scheduling mechanism. It is stated from experiments that the cypher is suitable for the implementation in energy-constrained IoT networks based on the fact that it attains an equilibrium between efficiency, security, and energy conservation. This work enhances data protection in IoT applications significantly but affords devices with limited power to avoid operating slower.

关键词： Ciphers Technological innovation Processor scheduling Memory management Data protection signal processing algorithms signal processing Encryption Computational efficiency Internet of Things

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A Fully-Automated Paper ECG Digitisation Algorithm for e-Health Applications

A Fully-Automated Paper ECG Digitisation Algorithm for e-Hea...

引用

International conference on Wireless Communications and signal processing (WCSP)

作者： Tao Wang Yiquan Wang Zhisen Chang Xiao Lyu Zhipei Huang Fei Qin University of Chinese Academy of Sciences Beijing China Beijing Alpha Yuan Technology Co. Ltd. Beijing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

The electrocardiogram (ECG) is an important non-invasive tool for diagnosing heart and cardiovascular diseases. However, traditional ECG systems often provide images, which makes it challenging to directly utilize the image-based data in various applications that require 1-D signals. Therefore, the digitalization of ECG reports is essential for the establishment of electronic medical archives, advanced diagnostic tools, and intelligent healthcare systems. Previous efforts to digitize paper-based ECGs have been limited by the need to manually select regions of interest, limited accuracy in handling overlapping ECG leads, and lack of batch processing capabilities. We present a fully automated method for digitizing ECG reports that requires only a predefined lead distribution, eliminating the need for manual lead position selection. This method effectively addresses the challenge of overlapping leads and has the capability for automated batch processing. Validation against a subset of the PTB-XL dataset demonstrated high accuracy and reliability (Root Mean Square Error: 0.036 (mV), Pearson Correlation Coefficient: 0.977). To further verify the generalizability of the method, we applied the algorithm to hospital ECG reports. The results, evaluated by expert cardiologists, confirmed that our method produced consistently high-quality digitalization and demonstrated its applicability to different types of ECG reports.

关键词： Wireless communication Accuracy Hospitals signal processing algorithms Batch production systems Electrocardiography Robustness Root mean square Standards signal resolution

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Robust signal processing Approaches for Enhancing Communication Reliability in Ultra-Dense Massive MIMO Networks Facing Fading Challenges

Robust Signal Processing Approaches for Enhancing Communicat...

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Automation and Computation (AUTOCOM), International conference on

作者： Rashmy Moray Savita Kumari Deepak Upadhyay Rashmi Sharma Aakash Singh Ranswal Nookala Venu Symbiosis Institute of Management Studies (SIMS) A constituent of Symbiosis International Deemed University School of Computing Science and Engineering Galgotias University Greater Noida India Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The goal of this paper is to introduce robust signal processing techniques for realistic communication scenarios with fading in ultra-dense massive MIMO communications. The proposed techniques tackle major challenges like channel estimation accuracy, interference management, and energy efficiency, which are essential for sustaining high performance in highly dense and dynamic scenarios. The proposed system outperforms traditional algorithms through advanced techniques and pilot contamination mitigation, interference-to-signal ratio optimization, and hybrid relaying. It is shown by the simulation results those considerable gains in spectral efficiency, energy efficiency and packet errors are achieved, even in the presence of varying user mobility and hardware impairments. These approaches provide reliable communication and optimize the network performance and thus are desirable for future ultradense 6G networks.

关键词： Fading channels Wireless communication Spectral efficiency Prevention and mitigation Channel estimation Massive MIMO Energy efficiency Hardware Reliability Contamination

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Comparative Analysis of QRS Detection algorithms: A Study on Accuracy and processing Speed in Raspberry Pi Single-Board Computer

Comparative Analysis of QRS Detection Algorithms: A Study on...

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International conference on advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA)

作者： Dika Setiawan Aris Munandar Dwi Esti Kusumandari Edi Triono Nuryatno Ulfah Nadiya M Faizal Amri Artha Ivonita Simbolon Jony Winaryo Wibowo Taufik Ibnu Salim Muhammad Ilham Rizqyawan Research Center of Smart Mechatronics National Research and Innovation Agency Bandung Indonesia UWA Centre for Medical Research University of Western Australia Perth Australia

An ECG can provide useful information about the current state of the heart. The detection of QRS complex stands as a critical step in ECG signal processing. This preliminary study presents a comparative study of several prominent QRS detection algorithms, including the Pan-Tompkins, the Hamilton, the Elgendi, and the Kalidas algorithm. In this study, not only the accuracy but also the runtime speed of the algorithm on the Raspberry Pi single-board computer was tested. By conducting this evaluation, it is aimed to provide a direct implication of their speed when implemented in a real-world scenario. Results show that Elgendi’s algorithm is considered to be the most eligible real-time QRS detection algorithm to be implemented in a single-board computer platform since it demonstrates high accuracy with the lowest possible runtime.

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Voice Recognition Acquisition and Storage System using VHDL and the DE1-SoC FPGA

Voice Recognition Acquisition and Storage System using VHDL ...

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IEEE International Congress on Electronics, Electrical Engineering and Computing (INTERCON)

作者： Elmer Arellanos Luis Dominguez Remigio Jose Luis Ostos Marquez Moises Nunez Electrical Engineering Department Universidad de Ingenieria y Tecnologia - UTEC Lima Peru Electrical Engineering Department Universidad Tecnológica del Perú Lima Peru

ISBN: (数字)9798350378344

ISBN: (纸本)9798350378351

This paper presents the design of a voice acqui-sition and recognition system in VHDL, mainly optimized for FPGA DE1-SoC. The system implements advanced digital signal processing techniques combined with classification through the K-Nearest Neighbors (KNN) algorithm. The system operates at a 320 kHz sampling rate, with a latency of 10 ns and power consumption of 1.2 W. The design enhances precision and noise rejection through the use of Hamming windows and integer-based processing. Future improvements will focus on the implementation of neural networks to replace the KNN algorithm and further increase system accuracy.

关键词： VHDL Data acquisition signal processing algorithms Speech recognition Nearest neighbor methods Solids Registers Synchronization Field programmable gate arrays Clocks

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Synchrophasor Logger Based on Laboratory Oscilloscope and Raspberry Pi

Synchrophasor Logger Based on Laboratory Oscilloscope and Ra...

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International Colloquium on Smart Grid Metrology (SmaGriMet)

作者： Karla Natalli Vujević Marin Despalatović Antonio Sunjerga Goran Petrović University of Split FESB Split Croatia

Synchrophasor measurement plays a critical role in improving the efficiency, reliability and security of distribution grids. The use of synchrophasors allows for advanced monitoring, control and protection of the distribution grid, providing valuable information for decision-making in real-time. In this paper, we present a simple framework based on open-source codes that can convert a typical laboratory oscilloscope to a synchrophasors logger with minimum investment and use devices commonly available in electrical engineering laboratories. We use simple algorithms to extract electrical quantities of interest and log them to a highly efficient time-series database. Post-processing and data analysis can be done for the active and reactive power of each individual harmonic and can be done by accessing the database remotely from any location connected to the internet.

关键词： Databases Oscilloscopes signal processing algorithms signal processing Harmonic analysis Phasor measurement units Real-time systems

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Synchronizing Local Oscillator Frequencies in Beamforming Nodes

Synchronizing Local Oscillator Frequencies in Beamforming No...

引用

advanced Telecommunication and Networking Technologies (ATNT), IEEE International conference on

作者： Umar Suleiman Dauda Nik Noordini Nik Abd Malik Nurul Muazzah Abdul Latiff Department of Electrical & Electronic Engineering Federal University of Technology Minna Nigeria Department of Communication Engineering Universiti Teknologi Malaysia Johar Malaysia Faculty of Electrical Engineering Universiti Teknologi Malaysia Jahar Malaysia

ISBN: (数字)9798350353501

ISBN: (纸本)9798350353518

Collaborative Beamforming (CB) which is a cooperative transmission technique where nodes act as a virtual antenna array can achieve an order of magnitude in improved signal strength, directional signal transmission, energy efficiency and node redundancy when practically deployed. However, signals arrive out of phase at the target receiver due to unsynchronized clock frequencies of local oscillators (LOs). In this article, an iterative procedure synchronizes the common message signal from multiple transmitters to the receiver. LO frequency synchronization and random phase adjustments are carried out by CB nodes at each time slot to increase the signal to noise ratio (SNR). Mean square error (MSE) of the frequency offset of the feedback signal are estimated with the developed LO estimation algorithm. This algorithm is seen to have tracked the modified Cramer-Rao lower bound (MCRLB) more closely for SNR between 7 dB and 20 dB. An improvement of 99.57% and 93.74% is recorded for LO frequency offset MSE for CB node 1 and CB node 2, respectively, when compared to another estimator. When beamforming is enabled, the percentage increase in amplitude between single and combine node with convergence achieved is 66.67%.

关键词： Local oscillators Array signal processing Estimation Collaboration Receivers Filtering algorithms Frequency estimation Synchronization Frequency synchronization signal to noise ratio

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Deep Learning and IoT-Enabled Framework for Accurate Classification and Monitoring of Alzheimer's Disease Based on EEG signal Analysis

Deep Learning and IoT-Enabled Framework for Accurate Classif...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International conference on

作者： R. Sathish R. Muthukumar K Dhivya S. Karthikkumar Department of Electrical and Electronics Engineering Builders Engineering College Tirupur Tamilnadu India Department of Electrical and Electronics Engineering Erode Sengunthar Engineering College Perundurai Tamilnadu India Department of Electrical and Electronics Engineering Jai Shriram Engineering College Tirupur Tamilnadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Alzheimer's disease is a brain ailment that impairs thinking, memory, and behaviour. The efficacy of Brain- Computer Interface (BCI) systems must be enhanced to increase their prevalence in the biomedical sector. The biomedical sector is crucial for precise and efficient patient diagnostics using digital signal processing and wireless sensor network technologies. Alzheimer's disease (AD) is an irreversible brain disorder. Inadequate Alzheimer's disease diagnosis in the early stages, together with exact identification, may aid in halting the progression of the illness. Precise and effective detection and categorization of Alzheimer's disease enhance quality of life and increase life expectancy for patients. The advanced deep learning technology surpasses traditional machine learning methods in classifying complex configurations in high-dimensional composite data, particularly in digital signal processing. The deep learning application for the early detection and accurate classification of Alzheimer's disease might be suggested for a neuron-signal methodology in extensive data analysis. This research paper proposes an intelligent decision-making system (IDMS) for the diagnosis and categorization of Alzheimer's disease (AD) and the continuous monitoring of patients utilizing connected wearable devices. Methods for measuring neuronal synchrony are proposed to diagnose and characterize Alzheimer's disease.

关键词： Deep learning Heuristic algorithms Decision making Digital signal processing Electroencephalography Encryption Security Alzheimer's disease Monitoring S Box

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SACCESS: Towards A Software Acceleration Framework for 5G Radio Access Networks

SACCESS: Towards A Software Acceleration Framework for 5G Ra...

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IEEE International Mediterranean conference on Communications and Networking (IEEE MeditCom)

作者： Filo, Marcin Xia, Yong Nikitopoulos, Konstantinos Univ Surrey 5G & 6G Innovat Ctr Inst Commun Syst Wireless Syst Lab Guildford GU2 7XH Surrey England

ISBN: (纸本)9781665445054

While the deployment of 5G mobile networks is still in its early stage, we are currently experiencing a paradigm shift towards Open Radio Access Network (RAN) architectures. In this context, RAN solutions that are heavily designed on software can reduce the implementation cost and time-to-market, as well as increase flexibility by making such solutions 'future-ready' through enabling new features, such as new advanced signal processing algorithms, to be included even via a simple software update. Realization of software-based RANs enabled by this paradigm shift, although attractive is however non-trivial, especially the realization of physical layer which mandates fast and efficient processing to meet strict real-time requirements. In this direction, we propose in this work a specialized acceleration software solution (SACCESS) to accelerate computationally expensive physical layer processing operations. We show that SACCESS can provide a slot processing speed-up of over 2.2, compared to OpenAirInterface (OAI) which emerged as one of the most advanced software-based 5G-NR RAN solutions available to a wider community. Our results demonstrate, for first time, that a peak throughput for a 40MHz TDD and FDD single antenna 5G-NR system based on OAI's development can be achieved without any hardware acceleration.

关键词： 5G mobile communication signal processing algorithms Physical layer Throughput Software Real-time systems Hardware acceleration

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