This experiment aims to determine what common household materials can block or weaken a Wi-Fi signal. The experiment will measure the Wi-Fi signal strength with no blocking material as a baseline, and then with various materials placed between the wireless router and device, such as aluminum foil, cardboard, glass, plastic, water, and the human body. Signal strength will be measured in dBm units, with lower values indicating a weaker, more attenuated signal. Results will be recorded and plotted on a graph showing the attenuation caused by each test material.
Nanomaterials-based 3d printer filament electrical characterizationScott Merry
Measured materials properties of chemically modified and control PLA (polylactic acid) FDM (fused deposition modeling) filaments, including volume resistivity for conductive filament, and relative permittivity (dielectric constant) for a first prototype filament material for additive manufacturing.
Nanomaterials-based 3d printer filament electrical characterizationScott Merry
Measured materials properties of chemically modified and control PLA (polylactic acid) FDM (fused deposition modeling) filaments, including volume resistivity for conductive filament, and relative permittivity (dielectric constant) for a first prototype filament material for additive manufacturing.
Using common WiFi, this low-cost suspicious object detection system can detect weapons, bombs and explosive chemicals in bags, backpacks and luggage. ... WiFi can also be used to estimate the volume of liquids such as water, acid, alcohol and other chemicals for explosives,WiFi, or wireless, signals in most public places can penetrate bags to get the dimensions of dangerous metal objects and identify them, including weapons, aluminum cans, laptops and batteries for bombs. WiFi can also be used to estimate the volume of liquids such as water, acid, alcohol and other chemicals for explosives, according to the researchers.
This low-cost system requires a WiFi device with two to three antennas and can be integrated into existing WiFi networks. The system analyzes what happens when wireless signals penetrate and bounce off objects and materials.
Experiments with 15 types of objects and six types of bags demonstrated detection accuracy rates of 99 percent for dangerous objects, 98 percent for metal and 95 percent for liquid. For typical backpacks, the accuracy rate exceeds 95 percent and drops to about 90 percent when objects inside bags are wrapped.
BalloonNet: A Deploying Method for a Three-Dimensional Wireless Network Surro...Naoki Shibata
Aiming at fast establishment of a wireless network around a multi-level building in a disaster area, we propose an efficient method to determine the locations of network nodes in the air. Nodes are attached to balloons outside a building and deployed in the air so that the network can be accessed from anywhere in the building. In this paper, we introduce an original radio propagation model for predicting path loss from an outdoor position to a position inside a building. In order to address the three-dimensional deployment problem, the proposed method optimizes an objective function for satisfying two goals: (1) guarantee the coverage: the target space needs to be covered by over a certain percentage by wireless network nodes, (2) minimize the number of network nodes. For solving this problem, we propose an algorithm based on a genetic algorithm. To evaluate the proposed method, we compared our method with three benchmark methods, and the results show that the proposed method requires fewer nodes than other methods.
Solving Network Throughput Problems at the Diamond Light SourceJisc
From Jisc's campus network engineering for data-intensive science workshop on 19 October 2016.
https://www.jisc.ac.uk/events/campus-network-engineering-for-data-intensive-science-workshop-19-oct-2016
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Using common WiFi, this low-cost suspicious object detection system can detect weapons, bombs and explosive chemicals in bags, backpacks and luggage. ... WiFi can also be used to estimate the volume of liquids such as water, acid, alcohol and other chemicals for explosives,WiFi, or wireless, signals in most public places can penetrate bags to get the dimensions of dangerous metal objects and identify them, including weapons, aluminum cans, laptops and batteries for bombs. WiFi can also be used to estimate the volume of liquids such as water, acid, alcohol and other chemicals for explosives, according to the researchers.
This low-cost system requires a WiFi device with two to three antennas and can be integrated into existing WiFi networks. The system analyzes what happens when wireless signals penetrate and bounce off objects and materials.
Experiments with 15 types of objects and six types of bags demonstrated detection accuracy rates of 99 percent for dangerous objects, 98 percent for metal and 95 percent for liquid. For typical backpacks, the accuracy rate exceeds 95 percent and drops to about 90 percent when objects inside bags are wrapped.
BalloonNet: A Deploying Method for a Three-Dimensional Wireless Network Surro...Naoki Shibata
Aiming at fast establishment of a wireless network around a multi-level building in a disaster area, we propose an efficient method to determine the locations of network nodes in the air. Nodes are attached to balloons outside a building and deployed in the air so that the network can be accessed from anywhere in the building. In this paper, we introduce an original radio propagation model for predicting path loss from an outdoor position to a position inside a building. In order to address the three-dimensional deployment problem, the proposed method optimizes an objective function for satisfying two goals: (1) guarantee the coverage: the target space needs to be covered by over a certain percentage by wireless network nodes, (2) minimize the number of network nodes. For solving this problem, we propose an algorithm based on a genetic algorithm. To evaluate the proposed method, we compared our method with three benchmark methods, and the results show that the proposed method requires fewer nodes than other methods.
Solving Network Throughput Problems at the Diamond Light SourceJisc
From Jisc's campus network engineering for data-intensive science workshop on 19 October 2016.
https://www.jisc.ac.uk/events/campus-network-engineering-for-data-intensive-science-workshop-19-oct-2016
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
7. What’s dBm
• Wireless signal strength is measured in units called
dBm.
• Value that is closer to zero means a stronger signal
than a value that is farther away from zero.
9. Ready for experiment2
• Aluminum foil
• Steel baking pans
• Glass baking pans
• Cardboard
• Plastic
• Large container filled with water
• Human body
10. Ready for experiment3
• If you have Android, you use Wi-Fi analyzer.
• If you have a Macintosh running OS X 10.9 or
higher use Wi-Fi strength indicates
12. Experiment procedure2
• Start out by measuring the signal strength with no
blocking material.
• Repeat this two more times.
13. Experiment procedure3
• Set a materials and measure an average signal
strength .
• Calculate Attenuation in dBm.
14. Calculate attenuation
• Attenuation = (average signal strength) – (average
signal setting a materials)
• Ex. Attenuation = -15 - (- 40) = 25 (dBm)
15. Prot a graph
• Horizontal line is material type.Vertical line is dBm.
• And prot a attenuation.
16. Bibliography
Brain, M., Wilson, T., and Johnson, B. (n.d.). How WiFi Works.
HowStuffWorks.com. Retrieved March 25, 2014, from
http://computer.howstuffworks.com/wireless-network.htm
• Mistral Solutions Pvt. Ltd. (2009, August). Dos and Don'ts of Wi-fi
connectivity: Maximizing Range and Reception. Retrieved March 31,
2014 from http://www.mistralsolutions.com/hs-downloads/tech-
briefs/aug09-article-3.html.
• Bertolucci, J. (2011, May 16). Six Things That Block Your Wi-Fi, and
How to Fix Them. PCWorld. Retrieved March 31, 2014, from
http://www.pcworld.com/article/227973/six_things_that_block_your_w
ifi_and_how_to_fix_them.html.
• Wikipedia contributors. (2014, March 19). dBm. Retrieved March 25,
2014, from
http://en.wikipedia.org/w/index.php?title=DBm&oldid=600275060.
