The document describes a study on improving the reliability of an RFID-based psychiatric patient localization system. The system uses RFID tags worn by patients and field generators to detect tag locations. Interference between overlapping field generators decreases accuracy. The study presents a graph coloring with merging and deletion algorithm to schedule field generator transmissions to avoid interference. Experiments show the algorithm improves response rates, especially in areas with overlapping field generators.
Review of the paper: Traffic-aware Frequency Scaling for Balanced On-Chip Net...Luca Sinico
This work has been done as assignment and as part of the exam of the Distributed Systems course, while attending the Master's Degree in Computer Engineering at University of Padua.
If you find something wrong or not clear, or if you don't agree with me with the work done or the grades of the assessment, please tell me.
This paper advances the Domain Segmentation based on Uncertainty in the Surrogate (DSUS) framework which is a novel approach to characterize the uncertainty in surrogates. The leave-one-out cross-validation technique is adopted in the DSUS framework to measure local errors of a surrogate. A method is proposed in this paper to evaluate the performance of the leave-out-out cross-validation errors as local error measures. This method evaluates local errors by comparing: (i) the leave-one-out cross-validation error with (ii) the actual local error estimated within a local hypercube for each training point. The comparison results show that the leave-one-out cross-validation strategy can capture the local errors of a surrogate. The DSUS framework is then applied to key aspects of wind resource as- sessment and wind farm cost modeling. The uncertainties in the wind farm cost and the wind power potential are successfully characterized, which provides designers/users more confidence when using these models
Wireless sensor network is an interesting research area that has been extensively discussed because of its importance in the most applications such as environmental monitoring, healthcare purposes, traffic control, and military systems. Sensor network consists of a large number of sensor nodes that are widely distributed in the environment to collect phenomena data. In this thesis, a smart fire system is proposed to predict, control, and alert fire occurrences by using multiple fuzzy-based methods. This system aids less energy to be consumed for transmitting various messages between wireless nodes, network traffic to be reduced over the network, and network lifetime to be prolonged consequently. The proposed routing protocols are, generally, categorized into two groups: static and dynamic. The static protocols are used to transmit data packets between the stationary nodes placed in different locations. The dynamic protocols direct, control, and transmit messages between vehicles and rescue team members. Besides, several fuzzy systems are offered to detect explosion possibility, determine fire probability, measure the intensity and volume of the fire, estimate fire progress, detect the burn possibility, and determine suffocation probability. In addition, the system determines the active and passive nodes as well as detects failure nodes throughout the network. Rescue teams are dispatched to events on the best path, between fire department and event place, that is selected by another fuzzy-based procedure. This procedure leads the rescue and support teams to be dispatched to events in a short time. Simulation and evaluation results show that the proposed fire system has a high performance compared to the most existing fire systems.
Surrogate-based design is an effective approach for modeling computationally expensive system behavior. In such application, it is often challenging to characterize the expected accuracy of the surrogate. In addition to global and local error measures, regional error measures can be used to understand and interpret the surrogate accuracy in the regions of interest. This paper develops the Regional Error Estimation of Surrogate (REES) method to quantify the level of the error in any given subspace (or region) of the entire domain, when all the available training points have been invested to build the surrogate. In this approach, the accuracy of the surrogate in each subspace is estimated by modeling the variations of the mean and the maximum error in that subspace with increasing number of training points (in an iterative process). A regression model is used for this purpose. At each iteration, the intermediate surrogate is constructed using a subset of the entire train- ing data, and tested over the remaining points. The evaluated errors at the intermediate test points at each iteration are used for training the regression model that represents the error variation with sample points. The effectiveness of the proposed method is illustrated using standard test problems. To this end, the predicted regional errors of the surrogate constructed using all the training points are compared with the regional errors estimated over a large set of test points.
Measurement Procedures for Design and Enforcement of Harm Claim ThresholdsPierre de Vries
Presentation at DySPAN 2017, March 2017
Paper forthcoming on IEEE Xplore
Paper authors:
Janne Riihijärvi, Petri Mähönen (RWTH Aachen University, Germany)
J. Pierre de Vries (Silicon Flatirons Centre, University of Colorado, USA)
Review of the paper: Traffic-aware Frequency Scaling for Balanced On-Chip Net...Luca Sinico
This work has been done as assignment and as part of the exam of the Distributed Systems course, while attending the Master's Degree in Computer Engineering at University of Padua.
If you find something wrong or not clear, or if you don't agree with me with the work done or the grades of the assessment, please tell me.
This paper advances the Domain Segmentation based on Uncertainty in the Surrogate (DSUS) framework which is a novel approach to characterize the uncertainty in surrogates. The leave-one-out cross-validation technique is adopted in the DSUS framework to measure local errors of a surrogate. A method is proposed in this paper to evaluate the performance of the leave-out-out cross-validation errors as local error measures. This method evaluates local errors by comparing: (i) the leave-one-out cross-validation error with (ii) the actual local error estimated within a local hypercube for each training point. The comparison results show that the leave-one-out cross-validation strategy can capture the local errors of a surrogate. The DSUS framework is then applied to key aspects of wind resource as- sessment and wind farm cost modeling. The uncertainties in the wind farm cost and the wind power potential are successfully characterized, which provides designers/users more confidence when using these models
Wireless sensor network is an interesting research area that has been extensively discussed because of its importance in the most applications such as environmental monitoring, healthcare purposes, traffic control, and military systems. Sensor network consists of a large number of sensor nodes that are widely distributed in the environment to collect phenomena data. In this thesis, a smart fire system is proposed to predict, control, and alert fire occurrences by using multiple fuzzy-based methods. This system aids less energy to be consumed for transmitting various messages between wireless nodes, network traffic to be reduced over the network, and network lifetime to be prolonged consequently. The proposed routing protocols are, generally, categorized into two groups: static and dynamic. The static protocols are used to transmit data packets between the stationary nodes placed in different locations. The dynamic protocols direct, control, and transmit messages between vehicles and rescue team members. Besides, several fuzzy systems are offered to detect explosion possibility, determine fire probability, measure the intensity and volume of the fire, estimate fire progress, detect the burn possibility, and determine suffocation probability. In addition, the system determines the active and passive nodes as well as detects failure nodes throughout the network. Rescue teams are dispatched to events on the best path, between fire department and event place, that is selected by another fuzzy-based procedure. This procedure leads the rescue and support teams to be dispatched to events in a short time. Simulation and evaluation results show that the proposed fire system has a high performance compared to the most existing fire systems.
Surrogate-based design is an effective approach for modeling computationally expensive system behavior. In such application, it is often challenging to characterize the expected accuracy of the surrogate. In addition to global and local error measures, regional error measures can be used to understand and interpret the surrogate accuracy in the regions of interest. This paper develops the Regional Error Estimation of Surrogate (REES) method to quantify the level of the error in any given subspace (or region) of the entire domain, when all the available training points have been invested to build the surrogate. In this approach, the accuracy of the surrogate in each subspace is estimated by modeling the variations of the mean and the maximum error in that subspace with increasing number of training points (in an iterative process). A regression model is used for this purpose. At each iteration, the intermediate surrogate is constructed using a subset of the entire train- ing data, and tested over the remaining points. The evaluated errors at the intermediate test points at each iteration are used for training the regression model that represents the error variation with sample points. The effectiveness of the proposed method is illustrated using standard test problems. To this end, the predicted regional errors of the surrogate constructed using all the training points are compared with the regional errors estimated over a large set of test points.
Measurement Procedures for Design and Enforcement of Harm Claim ThresholdsPierre de Vries
Presentation at DySPAN 2017, March 2017
Paper forthcoming on IEEE Xplore
Paper authors:
Janne Riihijärvi, Petri Mähönen (RWTH Aachen University, Germany)
J. Pierre de Vries (Silicon Flatirons Centre, University of Colorado, USA)
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
2. Reference
Chieh-Ling Huang, Pau-Choo Chung, Ming-Hua Tsai,
Yen-Kuang Yang, Yu-Chia Hsu Reliability improvement
for an RFID-based psychiatric patient localization
system IEEE Computer Communications 31 (2008)
2039–2048
2
3. Outline
Introduction
System overview
Reliability improvement with field generator scheduling
Experiments
Conclusion
3
4. Introduction
Psychiatric patients often cannot control their actions,
occasionally resulting in dangerous behavior
RFID technology has been utilized in various
applications, including supply chain management, entry
and exit control
Localizing moving objects, e.g., freight localization or
human localization is a challenging and relatively
unexplored task
presents a novel graph coloring with merging and
deletion (GCMD) algorithm
4
5. System overview
The Department of Psychiatry, National Cheng Kung University
Hospital (NCKUH) uses an RFID-based psychiatric patient
localization system
The second floor serves as a clinic for psychiatric patients, and the third floor
is an activity area
Nurses : scheduling daily activities and providing basic care
Doctors : medical treatment
This RFID-based psychiatric patient localization system uses a
ultra high frequency (UHF) long range tracker. The Field
Generators operate on 433 MHz when triggering the Tag to
respond, and the Tag replies to a Reader with 916 MHz signal
once it is triggered
Psychiatric patients in the care center wear watch-like Tags, Tag
transmits information, including Tag ID and Field Generator ID
5
7. Reliability improvement with
field generator scheduling
This system relies on the Tag correctly receiving signal from
the Field Generators to estimate the Tag location
Two Field Generators with overlapping transmission ranges
simultaneously issuing trigger signals to a Tag causes signal
interferences in the overlapped region
This interference results in loss of signal and, therefore,
decreases localization accuracy
Transform the relationships among all Field Generators into
a graph
Vertex deletion and merging
Vertex coloring
Operation slot allocation
7
9. Transform the relationships among
all Field Generators into a graph
The relationships among Field Generators are
transformed into an undirected graph G, whereas V and
E are sets of vertices and edges, respectively. Where V
and E are derived based on the Field Generators and
their signal region overlapping situations, respectively
9
10. Vertex deletion
When the range of one Field Generator, FGx, is
completely covered by other Field Generators, the
function of FGx can be replaced by a combination of
these other Field Generators
10
11. Vertex merging
The entire overlapping region is covered by a union of
Field Generators, so other Field Generators can cover
the overlapping region
Consequently, the two vertices associated with the two
Field Generators can be merged, and no special care is
required to avoid signal interference from the two Field
Generators
11
12. Vertex coloring
A coloring algorithm is applied to the trimmed graph, in
which connected vertices are assigned different colors
The Field Generators with the same color are assigned
to the same group, and, therefore, can transmit signals
simultaneously
Conversely, the Field Generators with different colors
are assigned to different groups; scheduling must be
applied to avoid signal conflict
12
13. Operation slot allocation
A weighted TDMA is applied to assign time slots for
operation to each group
Consider that each partitioned group can occupy
different levels of importance
Another consideration is the size of an area covered by a
group of Field Generators
The importance factor for each group wi can be
approximated
The time slot ratio for each group
13
18. Elucidating system performance
Tags
send out responses periodically (reciprocated regularly)
only when triggered by Field Generators
A patient’s location is computed based on the
communication range of the patient’s Tag within the
Field Generators with respect to ranges of reference
Tags
18
19. Elucidating system performance
More than two Tags are sending reports back to the
Reader simultaneously Repetitive transmission
Repetitive transmission times are set at 6 and the
associated lasting time is Trep
19
20. Elucidating system performance
Time in field (TIF) time: a Tag can be programmed with
a TIF Time (TTIF) that specifies the time duration before
the Tag can be triggered again
A Reader receives two consecutive reports from the same Tag.
How can the Reader determine whether the two reports are
issued due to two separate triggers, or whether the two reports
are due to a repetitive response trigger?
Another aim of TIF time is to prevent Tags from wasting
energy replying to the same trigger from a Field Generator
20
21. Elucidating system performance
Trep + TTIF is defined as one round; if one of the six
responses in one round is received, this round is regarded as
successfully received
lost rate of responses L as the total number of lost rounds
divided by the total number of rounds that should trigger the
Tag: r denotes the number of rounds that
the Reader successfully received Tag’s reply signal and T
represents time cost
In this system, it takes roughly three rounds for a patient to
move from the building exit to the main gate. Under this
scenario, we define response rate Rn as: n is
the number of rounds – 3 in this case
21
22. Fixed-points test
positions 1–6 reside in single Field Generator range
positions 7–10 are located in the overlapping region of two
Field Gen-erators
positions 11–14 are in the overlapped region for three Field
Generators
position 15 is in the overlapping region of four Field
Generators
people wearing Tags stand at each fixed position for 1 min
group1 is assigned 2 s for operation and group2 is assigned
1s
22
26. Route test
5 routes
(a) Route1 is the path passing the 15 representative points
(b) Route2 is the path connecting with poor reliability in the
fixed-point test
(c) Route3 is the path connecting points with high reliability
(d) Route4 is the path of shortest distance from the building
exit to the main gate and
(e) Route5 is a route tracing through a region that is rarely
covered by routes (a–d)
26
27. (a) The route
connecting 15
representative
points
(b) The route
connecting the
lowest reliability
points
(c) The route
connecting the
highest reliability
points
(d) The route
having the shortest
path from exit to
main gate
(e) The route
tracing a region
not tested in (a–d)
27
32. Experiments
position 3: The Field Generator has difficulty reaching
this sharp corner and the Tag cannot reach the Reader.
Thus, a Reader is added at position 10
Experimental results: response rate for position 3 improves
from 0% to 57.81% in the unscheduled original system,
and from 14.26% to 83.36% using GCMD scheduling
Transmission time slots should be based on group
importance
For group covering important regions or large areas
should be allocated increased time periods
32
34. Conclusion
The RFID devices that are small and relatively cheap are
very appropriate for use in localizing psychiatric Patients
In this study, a GCMD scheduling model is utilized for
scheduling Field Generator transmissions in an RFID-based
psychiatric patient localization system, thereby reducing
interference caused by Field Generators located near one
another
Experimental results demonstrated that the system is highly
effective when using the proposed scheduling algorithm
34