A PyDataGlobal 2020 talk focuses on digitizing and converting to spectra. A simple python module DEEPS shows the errors of signals having frequencies lower than Nyquist frequencies, which is verified on open datasets and indicates YouTube sounds are distorted.
Frequency Synthesis and Clock Generation for High Speed Systems - VE2013Analog Devices, Inc.
Frequency synthesis and clock generation are now key elements in all aspects of high speed data acquisition and RF design. In this session, the primary types of frequency synthesizers—phase-locked loops (PLL) and direct digital synthesizers (DDS)—are discussed, along with the applications for when each is appropriate. Also covered are detailed aspects of synthesizer design. Other applications, such as clock distribution and translation are addressed, and problems associated with poor clocking are identified. Examples of poor clocking are shown, along with the results of doing it properly.
Spectra DTP4500 - A Closer Look
The Ideal development & test platform for SDR/SCA waveforms & application development.
A presentation introducing Spectras product family, competitive advantage and the users, configuration, spceification, software interfaces, tools & waveforms of Spectra DTP4500.
Frequency Synthesis and Clock Generation for High Speed Systems - VE2013Analog Devices, Inc.
Frequency synthesis and clock generation are now key elements in all aspects of high speed data acquisition and RF design. In this session, the primary types of frequency synthesizers—phase-locked loops (PLL) and direct digital synthesizers (DDS)—are discussed, along with the applications for when each is appropriate. Also covered are detailed aspects of synthesizer design. Other applications, such as clock distribution and translation are addressed, and problems associated with poor clocking are identified. Examples of poor clocking are shown, along with the results of doing it properly.
Spectra DTP4500 - A Closer Look
The Ideal development & test platform for SDR/SCA waveforms & application development.
A presentation introducing Spectras product family, competitive advantage and the users, configuration, spceification, software interfaces, tools & waveforms of Spectra DTP4500.
Design and implementation of sdr based qpsk transceiver using fpgaTarik Kazaz
Software-defined radio (SDR) technology enables
implementation of wireless devices that support multiple air interfaces and modulation formats, which is very important
if consider the proliferation of wireless standards. To enable such functionality SDR is using reconfigurable hardware platform such as Field Programmable Gate Array (FPGA). In this paper, we present design procedure and implementation result of SDR based QPSK modulator on Altera Cyclone IV FPGA. For design and implementation of QPSK modulator we used Altera DSP
Builder Tool combined with Matlab/Simulink, Modelsim and
Quartus II design tools. As reconfigurable hardware platform
we used Altera DE2-115 development and education board with
AD/DA daughter card. Software and Hardware-in-the-loop (HIL)
simulation was conducted before hardware implementation and
verification of designed system. This method of design makes
implementation of SDR based modulators simpler ad faster.
Index Terms—SDR, FPGA, QPSK, DSP Builder, NCO, RRC
Here, an active RFID tag is used in place of voter id where the system can scan the tag and matches with the fingerprints collected in the Aadhar database.
The voter has to scan the RFID tag for the identification and further the voter has to confirm the identity with the fingerprints.
When fingerprints get matched then voters can cast their votes as the data gets matched.
A LCD is applicable in the process of displaying the corresponding information of the voter from the database, thus illegal voting or impersonating can be avoided since the finger prints is not the same for every individual
A Glimpse into Developing Software-Defined Radio by PythonAlbert Huang
Software-defined radio~(SDR) has been emerging for many years in
various fields, including military, commercial communication
systems, and scientific research, e.g. space exploration. GNU Radio
is an open source SDR framework written in Python. This talk will introduce from basic concept of software-defined radio and various
front-end hardware, and then illustrate how to use Python to develop
SDR.
SCA To Date and Motivation for Change. These slides will discuss why the JTRS Program Executive Office (JPEO) is aggressively procuring Software Defined Radio (SDR) consortium and industry assistance to spearhead a high impact evolution of the Software Communications Architecture (SCA) intended to deliver better radio performance along with a smaller footprint for waveforms and radio software. The webcast audience will learn about innovative SCA change proposal details and identified opportunities for near term radio performance impact with rapid market availability of these new capabilities via highly motivated COTS SDR software and development tool vendors.
Liquid Sensing: Visible light absorption spectroscopy and colorimetry are two fundamental tools used in chemical analysis. Most of these light-based systems use photodiodes as the light sensor, and require similar high input impedance signal chains. This session examines the different components of a photodiode amplifier signal chain, including a programmable gain transimpedance amplifier, a hardware lock-in amplifier, and a Σ-Δ ADC that can measure a sample and reference channel to greatly reduce any measurement error due to variations in intensity of the light source.
Gas Sensing: Many industrial processes involve toxic compounds, and it is important to know when dangerous concentrations exist. Electrochemical sensors offer several advantages for instruments that detect or measure the concentration of toxic gases. This session will describe a portable toxic gas detector using an electrochemical sensor. The system presented here includes a potentiostat circuit to drive the sensor, as well as a transimpedance amplifier to take the very small output current from the sensor and translate it to a voltage that can take advantage of the full-scale input of an ADC.
Why Is Expensive Equipment Necessary When A QRC WBT Will Do?bluejeanstest1
The Wide Band Transcorder (WBT) is an inexpensive fully self-contained radio frequency (RF) recording and playback system that is man portable and easily used by both novices and experts.
Hardware Accelerated Software Defined Radio Tarik Kazaz
Advanced 5G wireless infrastructure should support any-to-any connectivity between densely arranged smart objects that form the emerging paradigm known as the Internet of Everything (IoE). While traditional wireless networks enable communication between devices using a single technology, 5G networks will need to support seamless connectivity between heterogeneous wireless objects, and consequently enable the proliferation of IoE networks. To tackle the complexity and versatility of the future IoE networks, 5G has to guarantee optimal usage of both spectrum and energy resources and further support technology-agnostic connectivity between objects. This can be realized by combining intelligent network control with adaptive software-defined air interfaces. In order to achieve this, current radio technology paradigms like Cloud RAN and Software Defined Radio (SDR) utilize centralized baseband signal processing mainly performed in software. With traditional SDR platforms, composed of separate radio and host commodity computer units, computationally-intensive signal processing algorithms and high-throughput connectivity between processing units are hard to realize. In addition, significant power consumption and large form factor may preclude any real-life deployment of such systems. On the other hand, modern hybrid FPGA technology tightly couples a FPGA fabric with hard core CPU on a single chip. This provides opportunities for implementing air interfaces based on hardware/software co-processing, resulting in increased processing throughput, reduced form factor and power consumption, while at the same time preserving flexibility. This paper examines how hybrid FPGAs can be combined with novel ideas such as RF Network-on-Chip (RFNoC) and partial reconfiguration, to form a flexible and compact platform for implementing low-power adaptive air interfaces. The proposed platform merges software and hardware processing units of SDR systems on a single chip. Therefore, it can provide interfaces for on-the-fly composition and reconfiguration of software and hardware radio modules. The resulting system enables the abstraction of air interfaces, where each access technology is composed of a structured sequence of modular radio processing units.
The Building of Pulsed NQR/NMR Spectrometer IJECEIAES
NQR spectrometer designed is composed of four modules; Transmitter, Probe, Receiver and computer controlled (FPGA & Software) module containing frequency synthesizer, synchronous demodulator, pulse programmer and display. The function of the Transmitter module is to amplify the RF pulse sequence to about 200 W power level into the probe (50 Ohm) which is a parallel resonance circuit with a tapped capacitor. The probe excites the nucleus and picks-up the signal emitted from the nuclei. The nuclear signal at the same frequency as the excitation, which is typically in the range of a few microvolts is amplified, demodulated and filtered (1 kHz to 100 kHz) by receiver module. 14 N NQR, 1 H and 2 H NMR signals are observed from the spectrometer.As the SNR of NQR signal is very low, NQR signal processing based on Adaptive Line Enhancement is presented.
Automatic Detection of Heart Disease Using Discreet Wavelet Transform and Art...Editor IJMTER
ECG plays an important role for analysis and diagnosis of heart disease. ECG signals are
affected by different noises. These noises can be removed by de noise the ECG signal. After de
noising ECG signals, a pure ECG signal is used to detect ECG parameters. Then Feature extraction
of ECG signal is carried out by DWT techniques which are applied to ANN for classification to
detect cardiac arrhythmia. This paper introduces the Electrocardiogram (ECG) pattern recognition
method based on wavelet transform and neural network technique has been used to classify two
different types of arrhythmias, namely, Left bundle branch block (LBBB), Right bundle Branch
block (RBBB) with normal ECG signal. The MIT-BIH arrhythmias ECG Database has been used for
training and testing our neural network based classifier. The simulation results given at the end.
Software Defined Radio Engineering course samplerJim Jenkins
This 3-day course is designed for digital signal processing engineers, RF system engineers, and managers who wish to enhance their understanding of this rapidly emerging technology. Most topics include carefully described design analysis, alternative approaches, performance analysis, and references to published research results. Many topics are illustrated by Matlab simulation demos. An extensive bibliography is included.
Embedded systems increasingly employ digital, analog and RF signals all of which are tightly synchronized in time. Debugging these systems is challenging in that one needs to measure a number of different signals in one or more domains (time, digital, frequency) and with tight time synchronization. This session will discuss how a digital oscilloscope can be used to effectively debug these systems, and some of the instrumentation considerations that go along with this.
Spektralanalyse am technologischen Limit: Anwendungen in der Radioastronomie
Wo Prozessoren in ihrer Leistungsfähigkeit nicht mehr ausreichen, wird programmierbare Logik in Form von FPGAs (Field Programmable Gate Arrays) eingesetzt. Hunderte von Rechenoperationen können damit innert Nanosekunden erledigt werden. Diese Technologie eignet sich vorzüglich für die Echtzeit-Spektralanalyse von Signalen.
Typische Anwendungen finden sich in der Radioastronomie oder der Atmosphärenphysik. Typisch sind die enormen Abtastraten von mehreren Giga-Samples pro Sekunde bei Wandler-Auflösungen von ³ 10 Bit, Signal-Bandbreiten von > 1 GHz, aufgelöst in ³ 16'384 Kanälen. Die Leistungsgrenze wird in diesen Bereichen stetig nach oben geschoben.
Mit dem präsentierten Projekt wurde ein neuer Meilenstein punkto Funktionalität und Verarbeitungsgeschwindigkeit gesetzt. So ist das realisierte Spektrometer 1- oder 2-kanalig konfigurierbar, in der 2-kanaligen Version können beispielsweise die Summen- und Differenzspektren gerechnet werden, oder das Kreuzleistungs-Spektrum. Anstelle der "normalen" Fast Fourier Transform (FFT) wurde eine digitale Filterbank implementiert.
Im Vortrag werden aktuelle und künftige Anwendungen im Bereich der Radioastronomie vorgestellt. Es sind Anlagen und Projekte, die weltweit in Betrieb sind oder in Planung stehen. Messresultate zeigen die enorme Leistungsfähigkeit, aber auch die Grenzen der digitalen Spektralanalyse.
Bruno Stuber, Hochschule für Technik FHNW und Christian Monstein, ETH Zürich
Design and implementation of sdr based qpsk transceiver using fpgaTarik Kazaz
Software-defined radio (SDR) technology enables
implementation of wireless devices that support multiple air interfaces and modulation formats, which is very important
if consider the proliferation of wireless standards. To enable such functionality SDR is using reconfigurable hardware platform such as Field Programmable Gate Array (FPGA). In this paper, we present design procedure and implementation result of SDR based QPSK modulator on Altera Cyclone IV FPGA. For design and implementation of QPSK modulator we used Altera DSP
Builder Tool combined with Matlab/Simulink, Modelsim and
Quartus II design tools. As reconfigurable hardware platform
we used Altera DE2-115 development and education board with
AD/DA daughter card. Software and Hardware-in-the-loop (HIL)
simulation was conducted before hardware implementation and
verification of designed system. This method of design makes
implementation of SDR based modulators simpler ad faster.
Index Terms—SDR, FPGA, QPSK, DSP Builder, NCO, RRC
Here, an active RFID tag is used in place of voter id where the system can scan the tag and matches with the fingerprints collected in the Aadhar database.
The voter has to scan the RFID tag for the identification and further the voter has to confirm the identity with the fingerprints.
When fingerprints get matched then voters can cast their votes as the data gets matched.
A LCD is applicable in the process of displaying the corresponding information of the voter from the database, thus illegal voting or impersonating can be avoided since the finger prints is not the same for every individual
A Glimpse into Developing Software-Defined Radio by PythonAlbert Huang
Software-defined radio~(SDR) has been emerging for many years in
various fields, including military, commercial communication
systems, and scientific research, e.g. space exploration. GNU Radio
is an open source SDR framework written in Python. This talk will introduce from basic concept of software-defined radio and various
front-end hardware, and then illustrate how to use Python to develop
SDR.
SCA To Date and Motivation for Change. These slides will discuss why the JTRS Program Executive Office (JPEO) is aggressively procuring Software Defined Radio (SDR) consortium and industry assistance to spearhead a high impact evolution of the Software Communications Architecture (SCA) intended to deliver better radio performance along with a smaller footprint for waveforms and radio software. The webcast audience will learn about innovative SCA change proposal details and identified opportunities for near term radio performance impact with rapid market availability of these new capabilities via highly motivated COTS SDR software and development tool vendors.
Liquid Sensing: Visible light absorption spectroscopy and colorimetry are two fundamental tools used in chemical analysis. Most of these light-based systems use photodiodes as the light sensor, and require similar high input impedance signal chains. This session examines the different components of a photodiode amplifier signal chain, including a programmable gain transimpedance amplifier, a hardware lock-in amplifier, and a Σ-Δ ADC that can measure a sample and reference channel to greatly reduce any measurement error due to variations in intensity of the light source.
Gas Sensing: Many industrial processes involve toxic compounds, and it is important to know when dangerous concentrations exist. Electrochemical sensors offer several advantages for instruments that detect or measure the concentration of toxic gases. This session will describe a portable toxic gas detector using an electrochemical sensor. The system presented here includes a potentiostat circuit to drive the sensor, as well as a transimpedance amplifier to take the very small output current from the sensor and translate it to a voltage that can take advantage of the full-scale input of an ADC.
Why Is Expensive Equipment Necessary When A QRC WBT Will Do?bluejeanstest1
The Wide Band Transcorder (WBT) is an inexpensive fully self-contained radio frequency (RF) recording and playback system that is man portable and easily used by both novices and experts.
Hardware Accelerated Software Defined Radio Tarik Kazaz
Advanced 5G wireless infrastructure should support any-to-any connectivity between densely arranged smart objects that form the emerging paradigm known as the Internet of Everything (IoE). While traditional wireless networks enable communication between devices using a single technology, 5G networks will need to support seamless connectivity between heterogeneous wireless objects, and consequently enable the proliferation of IoE networks. To tackle the complexity and versatility of the future IoE networks, 5G has to guarantee optimal usage of both spectrum and energy resources and further support technology-agnostic connectivity between objects. This can be realized by combining intelligent network control with adaptive software-defined air interfaces. In order to achieve this, current radio technology paradigms like Cloud RAN and Software Defined Radio (SDR) utilize centralized baseband signal processing mainly performed in software. With traditional SDR platforms, composed of separate radio and host commodity computer units, computationally-intensive signal processing algorithms and high-throughput connectivity between processing units are hard to realize. In addition, significant power consumption and large form factor may preclude any real-life deployment of such systems. On the other hand, modern hybrid FPGA technology tightly couples a FPGA fabric with hard core CPU on a single chip. This provides opportunities for implementing air interfaces based on hardware/software co-processing, resulting in increased processing throughput, reduced form factor and power consumption, while at the same time preserving flexibility. This paper examines how hybrid FPGAs can be combined with novel ideas such as RF Network-on-Chip (RFNoC) and partial reconfiguration, to form a flexible and compact platform for implementing low-power adaptive air interfaces. The proposed platform merges software and hardware processing units of SDR systems on a single chip. Therefore, it can provide interfaces for on-the-fly composition and reconfiguration of software and hardware radio modules. The resulting system enables the abstraction of air interfaces, where each access technology is composed of a structured sequence of modular radio processing units.
The Building of Pulsed NQR/NMR Spectrometer IJECEIAES
NQR spectrometer designed is composed of four modules; Transmitter, Probe, Receiver and computer controlled (FPGA & Software) module containing frequency synthesizer, synchronous demodulator, pulse programmer and display. The function of the Transmitter module is to amplify the RF pulse sequence to about 200 W power level into the probe (50 Ohm) which is a parallel resonance circuit with a tapped capacitor. The probe excites the nucleus and picks-up the signal emitted from the nuclei. The nuclear signal at the same frequency as the excitation, which is typically in the range of a few microvolts is amplified, demodulated and filtered (1 kHz to 100 kHz) by receiver module. 14 N NQR, 1 H and 2 H NMR signals are observed from the spectrometer.As the SNR of NQR signal is very low, NQR signal processing based on Adaptive Line Enhancement is presented.
Automatic Detection of Heart Disease Using Discreet Wavelet Transform and Art...Editor IJMTER
ECG plays an important role for analysis and diagnosis of heart disease. ECG signals are
affected by different noises. These noises can be removed by de noise the ECG signal. After de
noising ECG signals, a pure ECG signal is used to detect ECG parameters. Then Feature extraction
of ECG signal is carried out by DWT techniques which are applied to ANN for classification to
detect cardiac arrhythmia. This paper introduces the Electrocardiogram (ECG) pattern recognition
method based on wavelet transform and neural network technique has been used to classify two
different types of arrhythmias, namely, Left bundle branch block (LBBB), Right bundle Branch
block (RBBB) with normal ECG signal. The MIT-BIH arrhythmias ECG Database has been used for
training and testing our neural network based classifier. The simulation results given at the end.
Software Defined Radio Engineering course samplerJim Jenkins
This 3-day course is designed for digital signal processing engineers, RF system engineers, and managers who wish to enhance their understanding of this rapidly emerging technology. Most topics include carefully described design analysis, alternative approaches, performance analysis, and references to published research results. Many topics are illustrated by Matlab simulation demos. An extensive bibliography is included.
Embedded systems increasingly employ digital, analog and RF signals all of which are tightly synchronized in time. Debugging these systems is challenging in that one needs to measure a number of different signals in one or more domains (time, digital, frequency) and with tight time synchronization. This session will discuss how a digital oscilloscope can be used to effectively debug these systems, and some of the instrumentation considerations that go along with this.
Spektralanalyse am technologischen Limit: Anwendungen in der Radioastronomie
Wo Prozessoren in ihrer Leistungsfähigkeit nicht mehr ausreichen, wird programmierbare Logik in Form von FPGAs (Field Programmable Gate Arrays) eingesetzt. Hunderte von Rechenoperationen können damit innert Nanosekunden erledigt werden. Diese Technologie eignet sich vorzüglich für die Echtzeit-Spektralanalyse von Signalen.
Typische Anwendungen finden sich in der Radioastronomie oder der Atmosphärenphysik. Typisch sind die enormen Abtastraten von mehreren Giga-Samples pro Sekunde bei Wandler-Auflösungen von ³ 10 Bit, Signal-Bandbreiten von > 1 GHz, aufgelöst in ³ 16'384 Kanälen. Die Leistungsgrenze wird in diesen Bereichen stetig nach oben geschoben.
Mit dem präsentierten Projekt wurde ein neuer Meilenstein punkto Funktionalität und Verarbeitungsgeschwindigkeit gesetzt. So ist das realisierte Spektrometer 1- oder 2-kanalig konfigurierbar, in der 2-kanaligen Version können beispielsweise die Summen- und Differenzspektren gerechnet werden, oder das Kreuzleistungs-Spektrum. Anstelle der "normalen" Fast Fourier Transform (FFT) wurde eine digitale Filterbank implementiert.
Im Vortrag werden aktuelle und künftige Anwendungen im Bereich der Radioastronomie vorgestellt. Es sind Anlagen und Projekte, die weltweit in Betrieb sind oder in Planung stehen. Messresultate zeigen die enorme Leistungsfähigkeit, aber auch die Grenzen der digitalen Spektralanalyse.
Bruno Stuber, Hochschule für Technik FHNW und Christian Monstein, ETH Zürich
This Masterclass is divided in two parts. The first one presents a brief outline of the UHF passive RFID technology (air interface, protocol and new Gen2V2 features). The second one, devoted to Privacy Impact Assessment, presents the European Recommendation and the recently published EN 16571 standard.
These are the slides for the presentation titled "Neural source-waveform model," given at ICASSP 2019 in Brighton, UK.
Presenter: Xin Wang, National Institute of Informatics, Japan
Diapositivas presentadas para el grado de Magister en Ciencias de la Computacion en University of Queensland.
Titulo de la Tesis: A RFID Collision Avoidance Framework based on SDR
Access the video from this presentation for free from
http://www.rohde-schwarz-usa.com/DebuggingEMISS_On-Demand.html
Overview:
Electromagnetic interference is increasingly becoming a problem in complex systems that must interoperate in both digital and RF domains. When failures due to EMI occur it is often difficult to track down the sources of such failures using standard test receivers and spectrum analyzers. The unique ability of real-time spectrum analysis and synchronous time domain signal acquisition to capture transient events can quickly reveals details about the sources of EMI.
What You Will Learn:
How to isolate and analyze sources of EMI using an oscilloscope
Measurement considerations for correlating time and frequency domains
Near field probing basics
Presented By:
Dave Rishavy, Product Manager Oscilloscopes, Rohde & Schwarz
Dave Rishavy has a BS in Electrical Engineering from Florida State University and an MBA from the University of Colorado. Prior to joining Rohde and Schwarz, Mr. Rishavy gained over 15 years of experience in the test and measurement field at Agilent Technologies. This included positions in a wide range of technical marketing areas such as application engineering, product marketing, marketing management and strategic product planning. While at Agilent, Dave led the marketing and industry segment teams for the Infiniium line of oscilloscopes as well as high end logic analysis.
Digital Implementation of Costas Loop with Carrier RecoveryIJERD Editor
Demodulator circuit is a basic building block of wireless communication. Digital implementation of
demodulator is attracting more attention for the significant advantages of digital systems than analog systems.
The carrier signal extraction is the main problem in synchronous demodulation in design of demodulator based
on Software Defined Radio. When transmitter or receiver in motion, it is difficult for demodulator to generate
carrier signal same in frequency and phase as transmitter carrier signal due to Doppler shift and Doppler rate.
Here the digital implementation of Costas loop for QPSK demodulation in continuous mode is discussed with
carrier recovery using phase locked loop.
A 45Gb/s PAM-4 Transmitter Delivering 1.3Vppd Output Swing with 1V supply in ...aiclab
Pushed by the ever-increasing demand of high-speed connectivity, next generation 400Gb/s electrical links are targeting PAM-4 modulation to limit channel loss and preserve link budget. Compared to NRZ, a higher amplitude is desirable to counteract the 1/3 reduction of PAM-4 vertical eye opening. However, linearity is also key, and PAM-4 levels must be precisely spaced to preserve the horizontal eye opening advantage it has over NRZ. This paper presents a 45Gb/s PAM-4 transmitter able to deliver a very large output swing with enhanced linearity and state-of-the-art efficiency. Built around a hybrid combination of current-mode and voltage-mode topologies, the driver is embedded into a 4-taps 5-bits FFE, and allows tuning the output impedance to ensure good source termination. Implemented in 28nm CMOS FDSOI process, the full transmitter includes a half-rate serializer, duty-cycle correction circuit, >>2kV HBM ESD diodes, and delivers a full swing of 1.3Vppd at 45Gb/s, while drawing 120mA only from 1V supply. The power efficiency is ~2 times better than previously reported PAM-4 transmitters.
anritsu mp1800a Анализатор качества сигнала BERonlinestuden
Тест битовых ошибок BER
детектор ошибок до 10мВ;
измерения BER до 32х каналов;
Функции измерений для PON, усилителей EDFA
4PAM/8PAM генератор 32.4PAM BER;
Similar to Basic Pitfalls in Waveform Analysis (20)
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
1. Signal Frequency FSig, FSR, and FNyq
Summary
1/ 34
Nyquist frequency FNyq:= 0.5FSR, Historical upper limit
FSR =16kHz
FSig
0.1 FSR
0.5 FSR=FNyq
0.9 FSR
November 15, 2020
2. Basic Pitfalls in Waveform Analysis
- Introduce DEEPS -
Yukio Okuda
sf.yukio@gmail.com
an independent, Atsugi, Japan
November 2020
3. Me = Industrial Soft Hard +40 Years Introduction
3/ 34
Soft+Hard, 2 Years:
Mechanical Vibration of Mother Machines
4. Sensor Selection , Waveform Failure Analysis
Soft, 8 Years: OSS
Soft+Hard, 15 Years: Digital LSI Testing and Failure
Analysis
5. based on Data Analysis IEEE-ITC Papers
Soft, 17 Years: Developing LSI Design CAD tools
Soft, 6 Years: Developing Information Retrieval
Hard, 3 Years: Color TV Production
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
6. Outline Introduction
4/ 34
Digitizing Error at lower than Nyquist Frequency FNyq
Digitize
(interpolate)
No-Repeatability
Pseudo Amplitude Modulation AM
FNyq
DCASE-2
DCASE-4
• Models
• Error signatures
Verified by DEEPS
Digitize Error Estimation (Prediction) by (Spectrum)
Improve
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
8. ADC: Analog to Digital Converter
ADC
No error
Extractor
Feature
Analysis
Digitizing
Time
Time
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
9. Outline of Models Models
6/ 34
Show errors at lower than Nyquist Frequency
Rarely reported
Improving applications is the next stage
– Low Sampling Rate
— # of Signal Repeats
Systematic Error
Shape Spectrum
Random Error
DEEPS
˜ Clock Skew
Show errors
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
10. What is ADC Models
7/ 34
Sampling at the predefined intervals of TS
11. Sampling Rate FSR := 1/TS
ADC = Sampling
Distortions at high frequency
Deterministic error ¬ Repeatable
Start time is uncontrollable
Random error ¬ No-Repeatable
18. Un-Controllable Start Time Models
10/ 34
Errors between measurements in a measurement
Errors depend on FSig ? Robustness
ADC
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
19. Estimate Error Variance caused by Start Time Variance Models
11/ 34
At a Start Time variance of Clock offsets Co=[0, 0.3, 0.6]
Repeat number NR= 4, FSig= 6.1kHz, ∆ FSp := FSp− FSig
Wave
FFT
Spectrum
FSp
Estimation
∆ FSp
Amp.:Summarize,Normalize
Freq.:∆from6.1kHz
Heat Map
∆ FSp
6.1kHz
∆FSp
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
20. Compare Error Variances of FSigs at NR= 4 Models
12/ 34
Clock offsets Co= 0, 0.1, 0.2, .., 0.9 Assume random uniform
FSig= 6, 6.1, 6.2, .., 7.5 kHz, NR= 4
FSig
– —
∆FSp
– 6.4kHz shows the own frequency
— 7.1kHz shows the own frequency
Others show:
Different one frequency
Different two or three frequencies
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
21. Compare Error Variances of FSigs at NR= 8 Models
13/ 34
Clock offsets Co= 0, 0.1, 0.2, .., 0.9 Assume random uniform
FSig= 6, 6.1, 6.2, .., 7.5 kHz, NR= 8
FSig
– —˜
∆FSp
– 6.4kHz shows the own frequency
Same as at NR= 4
— 7.1kHz shows the own frequency
Same as at NR= 4
˜ 6.1kHz shows the own frequency
Three frequencies at NR= 4
Others show two or three frequencies
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
22. A Magic Signal of 6.4 kHz Models
14/ 34
Differently distorted waves yield a FSp of 6.4 kHz!
NR= 4
FFT
Spectrum
NR= 8
FFT
Spectrum
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
23. Summary of Models Models
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DEEPS estimates error signatures at a FSR of 16 kHz
Signals higher than 4 kHz are distorted
No error signatures
Except, the Signals of 6.4 kHz, 7.1 kHz
• Show the FSp of the FSig
• Signals of 7.1 kHz show AM modulation waves
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
24. Outline of Verification Verification
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A/B Testing is difficult for an independent
Challenge post-mortem analysis on DCASE Challenge Datasets
25. DCASE: Detection Classification of Acoustic Scenes Events
Task2 ĸ
• Obtained by the one tool Statistical analysis of spectra
• 13,000 nominal sounds
• 10 sec • FSR= 16 kHz • four device types
Task4 ĸ
• From Used by several Voice Recognition Activities
• 12,000 unlabeled sounds, non-controllable measurements
• 10 sec • FSR= 16 kHz, 44.1 kHz
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
26. Averaged Spectra of Task2 Verification
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Compare tails of spectra from 4kHz
– All 6.4kHz peaks — Slider/Valve 7.1kHz peaks
– –
–
–
—
—
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
27. How to locate error signals Verification
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Apply Low-Pass Filter-Bank
Spectrum has no time information, i.e. Non-temporal
Reversed signals show the same spectra
FFT
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
28. Task2 Ex1: 6.4kHz Verification
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Spectrum peaks around 6.4 kHz
Ex1: slider/train/normal id 06 00000075
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
29. Task2 Ex1: 7.1kHz Verification
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Spectrum peaks around 7.1 kHz
Ex1: slider/train/normal id 06 00000075
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
30. Spectrum Endpoints of Task4 Verification
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All sounds may include distortions
– 70% sounds are digitized by FSR=16 kHz, upconverted to 44.1 kHz
— 30% sounds are digitized by FSR=44.1 kHz
–
–
—
—
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
31. AM Signatures at a FSR of 44.1 kHz Verification
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Peak count (2, 3, 4, 5) identifies AM signatures FSig
19.9 kHz
— ˜ ™ š
˜ ™ ˜ ˜ ˜ ˜
— ˜
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
32. Summary of Verification Verification
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Demonstrated
1 Digitizing error at lower than FNyq
2 DEEPS estimations
3 sounds include distortions
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
33. Discussion for Improvements Discussion
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Application policies set Max FSig Max Error
Feature error predictions set FSR
ADC + Waves
Waveform Variations
Controllable
Non-Controllable
Max FSig
Max Error
FSR
Correct
Feature Extraction
Error Prediction
FFT Statistics
Time Series
Wavelet Cepstrum
Human Ear Eq.
. . .
DA
DataAnalysis
Appli.
Science
Medical
Industry
Home
. . .
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
34. Feature Error Predictions by DEEPS Discussion
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Feature extractor decide error from waveform variations
Wave Gen Drive Extractor
Features
FSig-N
Wave Gen Drive Extractor
Features
FSig-1
Sum
Indicator
FSig
Already FFT with spectrum
Challenging predictions: • Characteristics of waves
• References to Metrics • Parameters • Window size • FSig
dependancy • . . .
May be effective on non-controllable systems
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
36. Error Metrics for FFT: FSp
Discussion
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Ghost FSp, FSig of 4.2, 4.3, 4.4 kHz show FSp of 4.0, 4.3, 4.6 kHz
Co= 0, 0.1, 0.2, .., 0.9, FSig= 1, 1.1, .. 7.9, NR= 4
FSig
FSp
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
37. Aliasing Drops at near FNyq
Discussion
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Unremovable error signals ¬ Reject FSig ≥ FNyq by H/W filters
Co= 0, 0.1, 0.2, .., 0.9, FSig= 8.0, 8.5, 15.5, NR= 20
FSig
FSp
FNyq
Drops
FSp
Spectrum
Task2-Ex1
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
38. Waves are Noises or Signals Discussion
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Amplitude Ratio RAmp := AmpFSig
/Amp
Task2-Ex1, high pass filter of 7kHz ¬ Highly destorted signals
Amplitude
absRAmp
Time Time
Spectrum is a poor indicator of waveform amplitudes
Waves with different amplitudes show the same spectrum amplitude
FFT
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
39. Basic H/W Requirements Discussion
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MUST-1 Reject FSig ≥ FNyq by
– Mics or — Low-pass filters(LPFs)
MUST-2 Apply ADCs with enough high FSR
Option Reject FSig Max-FSig If need
– Mics or ™ digital LPFs, Not˜LPFs
MaxFSig
–
Low-Pass Filter
MaxFSig
—˜
ADC Low-Pass Filter
™
MaxFSig
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
40. Low-Pass Filter Distortions Discussion
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Phase Shifts of analogue low-pass filter(LPF) cause
distortions
Phase Shifts depend on Circuit Frequency
By Brews ohare ĸ
FFT ¬ Spectrum + Phase
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
41. Audio Recording Discussion
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Low FSR standard
1982 CD(Compact Disc) FSR= 44.1 kHz ¬ Challenging at ’80
• Poor Human Hearing, Ear Frequency Curve
1984 PC-AT Intel 80286 clock= 6, 8 MHz
2003 Pro audio standard AES3: 88.2, 96, 176.4, 192 kHz
Mics of up to 20kHz ¬ 90% FNyq of FSR= 44.1 kHz
Ear Frequency Curve ¬ Voice recognition unique features
EarGain
FSigɀ ĸ
Pre-emphasis
Gammatone
Spectrograms
Cepstrum
Mel Spectrogram
MFCCs
Chroma
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
42. Conclusion 33/ 34
DEEPS demonstrated
Existence of digitizing error at lower than FNyq
sounds include distortions
Indicated
Apply FSRs obtained from
Max FSig Max Error Feature error predictions
Check Audio recording for Data Analysis
Y. Okuda Basic Pitfalls in Waveform Analysis PyData Global 2020 talk-38
43. Thank you, the supporting staffs of
DCASE, PyData Global,
Your
Questions Comments