Passive Displacement Sensing Using Backscatter RFID with Multiple Loads
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Backscatter radio frequency (RF) tags and radio frequency identification (RFID) tags can be used to create wireless sensors that do not require a battery by using the tag antenna's electrical load as a transducer. This paper presents such a backscatter sensor system operating in the 5.8 GHz unlicensed frequency band that wirelessly senses distance (i.e., displacement) between one and five millimeters. The system senses the variable impedance of a covered, open-circuit microstrip line and uses two fixed antenna load impedances to remove the effects of the radio channel. The primary contribution of this paper is a demonstration of the wireless sensing system in which the binary position of a human finger (e.g., “bent” or “straight”) is measured using a passive transducer for wireless human-computer-interaction (HCI) applications.
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RFID localization uses RFID readers and tags along with localization algorithms like multilateration and Bayesian inference. Three demo cases were presented: 1) Indoor localization of a person using active RFID tags, 2) An indoor localization system using passive RFID tags to track robots, and 3) Using active RFID to locate customers in a restaurant to deliver orders. Challenges with RFID localization include limited battery life for active tags and short reading distances for passive tags.
A benchmark dataset to evaluate sensor displacement in activity recognition
This work introduces an open benchmark dataset to investigate inertial sensor displacement effects in activity recognition. While sensor position displacements such as rotations and translations have been recognised as a key limitation for the deployment of wearable systems, a realistic dataset is lacking. We introduce a concept of gradual sensor displacement conditions, including ideal, self-placement of a user, and mutual displacement deployments. These conditions were analysed in the dataset considering 33 fitness activities, recorded using 9 inertial sensor units from 17 participants. Our statistical analysis of acceleration features quantified relative effects of the displacement conditions. We expect that the dataset can be used to benchmark and compare recognition algorithms in the future.
This presentation illustrates part of the work described in the following article:
* Banos, O., Toth, M. A., Damas, M., Pomares, H., Rojas, I., Amft, O.: A benchmark dataset to evaluate sensor displacement in activity recognition. In: Proceedings of the 14th International Conference on Ubiquitous Computing (Ubicomp 2012), Pittsburgh, USA, September 5-8, (2012)
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Hand Gesture Recognition for an Off-the-Shelf Radar by Electromagnetic Modeli...
Presentation of a full paper @ IUI 2022.
Paper available at https://dl.acm.org/doi/10.1145/3490099.3511107
Novel Spike-based architecture for RFID and Sensor Communication System
A novel system architecture to communicate ID and sensor information through wireless media is proposed which is narrow band, spike based and easy to implement.
1. It can transmit ID and sensor information in parallel.
2. It uses spike for communication
- An efficient use of harvested energy
- Almost no switching is involved in comparison with current RFID protocol.
3. It does not require a wideband pulse or a wideband amplitude and phase decoder.
4. It can incorporate ID and multiple sensors easily.
5. Provision to incorporate collision minimization scheme in multi-tag environment.
Wireless Indoor Localization with Dempster-Shafer Simple Support Functions
A mobile robot is localized in an indoor environment
using IEEE 802.11b wireless signals. Simple support
functions of the Dempster-Shafer theory are used to combine evidence
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are presented and discussed. Conclusions are drawn regarding
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Final Senior Design Presentation
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Sensor net
The document discusses wireless sensor networks and their applications. It provides information on key aspects of wireless sensor networks including smart sensors, ad hoc network topology, wireless communication modalities, and applications such as environmental monitoring, civil structure monitoring, and health care. Distributed relaying is shown to decrease the power consumption per sensor node. Hierarchical data processing and communication architectures can improve energy efficiency in large sensor networks.
10 Important AI Research Papers.pdf
This document summarizes 10 important AI research papers. It begins with a brief introduction on artificial intelligence and what the papers aim to provide information on. It then lists the 10 papers with their titles:
1. A Computational Approach to Edge Detection
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UNDERGROUND CABLE FAULT DISTANCE DETECTOR
The document describes a system to detect the location of faults in underground cables. It uses a PIC16F877A microcontroller to sense changes in voltage across series resistors in the cable based on the length of the fault. This precise digital data is then displayed on an LCD screen interfaced with the microcontroller showing the fault location in distance. It also uses an ESP8266 WiFi module to display the fault information over the internet. The system aims to accurately locate faults without needing to remove long stretches of underground cables.
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The document presents a novel three-dimensional adaptive localization (T-Dial) algorithm for wireless sensor networks. The proposed algorithm works in four primary phases: 1) Neighbor formation where nodes broadcast information to nodes within transmission range to form neighbor tables; 2) Group formation where anchor nodes connect to non-anchor nodes to divide the network into smaller manageable groups; 3) Edge node marking where edge nodes on the network boundary are detected and marked; and 4) Localization error correction where missing nodes from initial setup are rediscovered and corrected. Simulation results show the proposed algorithm improves localization rate, reduces localization error, and increases positioning rate compared to existing algorithms.
J017345864
The document presents a novel three-dimensional adaptive localization (T-Dial) algorithm for wireless sensor networks. The proposed algorithm works in four primary phases: 1) Neighbor formation where nodes broadcast information to nodes within transmission range to form neighbor tables; 2) Group formation where anchor nodes connect to nearby nodes to divide the network into manageable groups; 3) Edge node marking where edge nodes on the network boundary are detected and marked; and 4) Localization error correction where missing nodes from initial setup are rediscovered and corrected. Simulation results show the proposed algorithm improves localization rate, reduces localization error, and increases positioning rate compared to existing algorithms.
Ijetcas14 443
This document discusses cyclostationary feature detection for spectrum sensing in cognitive radio using various modulation schemes. It presents the block diagrams for cyclostationary feature detection without and with modulation. It simulates the detection using BPSK, QPSK, and 8-PSK modulation and analyzes the output cyclic spectral correlation function. The main results are that BPSK produces one primary and one secondary peak, QPSK produces one primary and two secondary peaks, and 8-PSK produces one primary and four secondary peaks in the output, allowing identification of the modulation scheme used.
Resume
Ali Nassib is seeking a full-time engineering position utilizing his experience in radar, remote sensing, computational electromagnetics, and RF/microwave devices. He has a Ph.D. in electrical engineering from the University of Dayton and experience managing radar systems and contracts for the FAA. His background includes extensive simulation, research, measurement, and teaching experience in areas such as radar imaging, antenna design, microwave components, and CMOS circuit design.
Optimum Sensor Node Localization in Wireless Sensor Networks
Abstract: Scientists, engineers, and researchers use wireless sensor networks (WSN) for a wide array of applications. Many of these applications rely on knowledge of the precise position of each node. An optimum localization algorithm can be used for determining the position of nodes in a wireless sensor network. This paper provides an overview of different approach of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Experiments were performed in a testbed area containing anchor and blind nodes deployed in it to characterize the pathloss exponent and to determine the localization error of the algorithm. Details regarding the implementation of new algorithm are also discussed in this paper.
A Pyroelectric Infrared Sensor
This document presents a non-terminal based indoor location system called PILAS that uses pyroelectric infrared (PIR) sensors. The PILAS system architecture includes PIR sensors installed in each room, room terminals, and a smart home server that generates a virtual map and provides intelligent services. The PILAS system can locate residents within a room by analyzing the output patterns of overlapping PIR sensors without requiring residents to carry any devices. An experimental test bed demonstrated that the PILAS system can accurately track a resident's movements and locate them with a maximum error of less than 30 cm.
sensors
This document provides an overview of sensors used in robots. It discusses that sensors allow robots to perceive their environment and perform tasks reliably. The document then describes various types of internal sensors like position, velocity, force sensors and external sensors like proximity, range finding, color and motion sensors. It provides details on specific position sensors like potentiometers, optical encoders, LVDTs and magnetic sensors. The document also discusses velocity sensors such as encoders and tachometers. Finally, it mentions new developments in sensor technology including MEMS, MOEMS and smart sensors, and provides an example of the humanoid robot ASIMO which utilizes various sensors for functions like vision, balance and intelligence.
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Null Bangalore | Pentesters Approach to AWS IAM
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
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- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
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- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
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- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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Passive Displacement Sensing Using Backscatter RFID with Multiple Loads
- 1. Passive Displacement Sensing using Backscatter RFID with Multiple Loads Jonathan Becker, Carnegie Mellon University Joshua Griffin, Disney Research Matthew Trotter, Disney Research
- 2. Goal Wirelessly sense the bend or displacement of an object with a low cost passive device. Prior Approaches • Piezoresis:ve materials • Fiber op:cs Problem with Prior Approaches May require analog-‐to-‐digital converters and transmiAng circuitry to sense bend – rela:vely high power consump:on Key Insight Use a displacement sensor aDached to hand to change scaDering proper:es of a backscaDer RFID tag as the hand bends and straightens. Applica8ons • Gestural interfaces • Virtual Reality • Mo4on capture 2 For example, see: • Olson et. al., “Piezoresistive strain gauges for use in wireless component monitoring systems” in 2008 IEEE Sensors Applications Symposium • Jahed et. at., “Enhanced resolution fiber optic strain sensor based on mach-zehnder interferometer and displacement sensing principles” in IEEE ELECO 2009
- 3. Diagram of 3-State RFID Tag Test Setup Want to determine this value D = Antenna Separation Can vary in time H ~Channel unknown a priori vv Z Z Z ⇔Γ ⇔Γ ⇔Γ 22 11 3 Displacement Sensor RFID Reader
- 4. Why are 3-States Needed? Γ1v = H Γ1 − Γv( ) Γ12 = H Γ1 − Γ2( ) Γv = Γ1 − Γ1v Γ12 Γ1 − Γ2( ) Known Known Known Unknown (Value desired)Unknown 4 Reference: S. Capdevila, L. Jofre, J. Romeu, and J. C. Bolomey, “Multi-Loaded Modulated Scatterer Technique for Sensing Applications,” IEEE Trans. Instrum. Meas., vol. 62, no. 4, pp. 794–805, 2013.
- 5. Sketches of Hand Mounted Displacement Transducer Straight Bent 5
- 6. Demo of Hand Mounted Displacement Sensor 6
- 7. Fig. 4 from Paper: Sensor in Test Setup Covered Microstrip Displacement Sensor for Precision Measurements 7 Sensor Parts Side by Side
- 8. Controlled Measurement Test Setup Displacement Sensor RF Switches Known Loads Tag Antenna Reader Antenna (not shown) 8
- 9. Results with Different Antenna Separations 9
- 10. Conclusions • Demonstrated a 5.8 GHz RFID tag with a sensor that detects displacement passively • Need a total of three impedance states to determine an unknown load state with an unknown channel – The first two impedance states contain channel information • Future work – Design a printed circuit board version of the tag – Integrate a charge pump into the circuit (passive tag) 10
- 12. Live Results: Hand Mounted Displacement Transducer 12
- 13. Why Use Three Loads? Removing DC offset creates ambiguity: Change in a load vs. moving antenna? DC Offset Removed Move Antenna Change Load Similar Rotation 13
- 14. Why Use Three Loads? (Cont.) Move Antenna Remove DC Offset Change Load Third load removes ambiguity created by DC block 14
- 15. Prototype 5.8 GHz 2-State RFID Tags 15