![Waltenegus Dargie](/img/default-banner.jpg)
- 34
- 74 983
Waltenegus Dargie
Приєднався 25 лип 2007
Deployment of a Wireless Sensor Network on North Biscayne Bay, Miami, Florida
A network of five wireless sensor nodes was deployed on North Biscayne Bay, Miami, Florida, to study the effect of the movement of the water on the wireless link quality the nodes established between themselves. It turned out that the effect was significant and initial results suggest that there is a direct correlation between the two parameters. However, a more rigorous study is needed because the change in the RSSI value (signifying a change in the link quality) is a one-dimensional aspect whereas the movement of the water, as captured by the use of IMU, is a three-dimensional aspect.
Переглядів: 164
Відео
Deployment of a Wireless Sensor Network in Extremely Rough Environment
Переглядів 61Рік тому
At Florida International University (FIU), Knight Foundation School of Computing and Information Sciences we developed a waterproof wireless sensor node to deploy on the surface of a rough water to monitor various parameters. The initial prototype consists of two different radios (CC2538 and cc1200), a 3D accelerometer, a 3D gyroscope, and power bank, and a waterproof marine antenna. The inerti...
Wireless Sensor Deployment at Crandon Beach
Переглядів 43Рік тому
At Florida International University (FIU), Knight Foundation School of Computing and Information Sciences we developed a waterproof wireless sensor node to deploy on the surface of a rough water to monitor various parameters. The initial prototype consists of two different radios (CC2538 and cc1200), a 3D accelerometer, a 3D gyroscope, and power bank, and a waterproof marine antenna. The inerti...
Effects of Water Movement on Link Quality Fluctuation
Переглядів 32Рік тому
At Florida International University (FIU), Knight Foundation School of Computing and Information Sciences we developed a waterproof wireless sensor node to deploy on the surface of a rough water to monitor various parameters. The initial prototype consists of two different radios (CC2538 and cc1200), a 3D accelerometer, a 3D gyroscope, and power bank, and a waterproof marine antenna. The inerti...
The Significance of Time in TS Eliot's Burnt Norton
Переглядів 6773 роки тому
In this video the philosophies of Plato, Aristotle, Boethius, Newton, Leibniz, Kant, and Einstein as regards TIME are briefly discussed. With this background, the significance of time in Burnt Norton is analysed.
Indoor Localization using Recursive Estimation
Переглядів 3073 роки тому
In this lecture the principles of recursive filters in the context of estimating the position of mobile robots in an indoor environment is discussed.
ENERGY LAB @ TU DRESDEN, GERMANY
Переглядів 5183 роки тому
The Energy Lab focuses on the practical implementation and testing of stochastic models; applying the models on mobile robots, Unmanned Aerial Vehicles, and wireless sensor networks. Randomly deployed wireless sensor nodes self-organise to autonomously undertake a sensing task in dangerous, inacessible, or extensive places. Self-managing and self-navigating mobile robots interact with the wirel...
Introduction to Linear Estimation
Переглядів 1,1 тис.3 роки тому
In this lecture the underlying assumptions and the mathematical foundations of linear estimation will be discussed.
Particle Filter -- Part II: Conceptual Aspects and Mathematical derivations
Переглядів 7553 роки тому
In this lecture the principles of particle filter (the conceptual and mathematical basis) will be discussed. Erratum: From 12:46 to 12:48, I speak of summation when, obviously, I should be speaking of product. The sign I use, however, is signifies a product, which is correct.
Particle Filter -- Part I: Preliminaries
Переглядів 1,5 тис.3 роки тому
In this lecture the principles of particle filter will be discussed. Since particle filter builds up on many existing technqiues, including Bayes Theorem (conditional probability), principles of Markov process, and importance sampling, the lecture focuses on establishing the ground work. Erratum: At 11:04, the second x_p(t) should be x_m(t).
Particle Filter -- Part III: Importance Sampling and Sequential Importance Sampling (SIS)
Переглядів 1,4 тис.3 роки тому
In this lecture, importance sampling and sequential sampling, as components of Particle Filter, will be discussed. ERRATUM: Towards the end I used \delta and yet keep on referring to it as sigma. The Dirac delta function \delta (x) has a value of one at x = 0 and zero everywhere else. Any signal can be described in terms of it, whether discrete or continuous. For a discrete case, f[n] = \sum_{i...
Hidden Markov Models (Part IV: Belief Revision): Dr. Waltenegus Dargie (TU Dresden)
Переглядів 1224 роки тому
This lecture foccuses on updating the parameters of a Hidden Markov Models (HMM) given a set of observation and the original model parameters.
Hidden Markov Models (Part III: Uncovering Hidden States): Dr. Waltenegus Dargie (TU Dresden)
Переглядів 964 роки тому
In this lecture uncovering the hidden states of a HMM which could have generated a sequence of observations will be discussed. Two of the interesting aspects of this lecture are: (1) The backward algorithm and (2) the Viterbi algorithm.
Hidden Markov Models (Part I: Introduction): Dr. Waltenegus Dargie (TU Dresden)
Переглядів 3744 роки тому
This lecture introduces discrete Markov Processes (DMP) and Hidden Markov Models.
Hidden Markov Models (Part II. Likelihood Probability): Dr. Waltenegus Dargie (TU Dresden)
Переглядів 1494 роки тому
In this lecture the computation of the likelihood probability of a sequence of observations given a Hidden Markov Model will be discussed.
Application of Tensor Decomposition (Wireless Electrocardiogram): Dr. Waltenegus Dargie (TU Dresden)
Переглядів 2564 роки тому
Application of Tensor Decomposition (Wireless Electrocardiogram): Dr. Waltenegus Dargie (TU Dresden)
Removing Motion Artifacts from the Measurements of a Wireless Electrocardiogram
Переглядів 2704 роки тому
Removing Motion Artifacts from the Measurements of a Wireless Electrocardiogram
The Dry Salvages by T. S. Eliot (Dr. Waltenegus Dargie)
Переглядів 1 тис.4 роки тому
The Dry Salvages by T. S. Eliot (Dr. Waltenegus Dargie)
Routing Protocols for Wireless Sensor Networks
Переглядів 11 тис.4 роки тому
Routing Protocols for Wireless Sensor Networks
Medium Access Control Protocols for Wireless Sensor Networks
Переглядів 4,9 тис.4 роки тому
Medium Access Control Protocols for Wireless Sensor Networks
Physical Layer: Wireless Sensor Networks
Переглядів 3,3 тис.4 роки тому
Physical Layer: Wireless Sensor Networks
Operating Systems for Wireless Sensor Networks
Переглядів 3,5 тис.4 роки тому
Operating Systems for Wireless Sensor Networks
Architecture of a Wireless Sensor Node
Переглядів 4,6 тис.4 роки тому
Architecture of a Wireless Sensor Node
Introduction to Wireless Sensor Networks
Переглядів 22 тис.4 роки тому
Introduction to Wireless Sensor Networks
Applications of Wireless Sensor Networks
Переглядів 4,7 тис.4 роки тому
Applications of Wireless Sensor Networks
The Reason for Life -- Dr. Waltenegus Dargie's Interview with VoA
Переглядів 7905 років тому
The Reason for Life Dr. Waltenegus Dargie's Interview with VoA
Minimum Mean Square Estimation (Part I: Linear Estimation) by Dr. Waltenegus Dargie (TU Dresden)
Переглядів 2,6 тис.5 років тому
Minimum Mean Square Estimation (Part I: Linear Estimation) by Dr. Waltenegus Dargie (TU Dresden)
Minimum Mean Square Estimation (Part II: Nonlinear Estimation) by Dr. Waltenegus Dargie (TU Dresden)
Переглядів 9786 років тому
Minimum Mean Square Estimation (Part II: Nonlinear Estimation) by Dr. Waltenegus Dargie (TU Dresden)
Kalman Filter (Part III: Application of Minimum Mean Square Estimation)
Переглядів 4,3 тис.6 років тому
Kalman Filter (Part III: Application of Minimum Mean Square Estimation)
Your Lecture is a Life Saver. Thanks
After watching this now I can teach Kalman Filter 😊 Best content on Kalman Filter. Thank you, Doctor Dargie... Respect.
HOW CAN I CONTACT YOU
1:06:12 TinyOS 1:17:03 Contiki OS
Thanks a million for this
Thank you Prof for this informative piece. My research is centered on WSN, May I have your email to contact you for assistance and guide
Hi sir Can I get your mail id please Am doin PhD in wsn Need to clarify some doubts
🏠🏹
🛖✅
thank you so much Dr.! it is very useful video for me. please do more videos about navigation and control system related topics like kalman filter if you can.🍏🍋🍎✅✅✅
There is already a video on Kalman Filter: ua-cam.com/video/gnJqY7s2Lhk/v-deo.html.
Thank You
GOOD JOB
Promo>SM
puras diapositivas nadamas
puras leidas
yes -> this is one of the best lectures about Kalman filter -> thank you !
Thank you sir
I was lucky to locate your videos on WSNs here. please how can I get more materials on this. Thanks so much for these insights
Unfortunately, the PPT slides are available from within the TU Dresden's domain.
how i can get the material ,i need a link please and thank you so much , sir <3
The slides are based on the book I co-authored. The book is entitled: Fundamentals of Wireless Sensor Networks: Theory and Practice (W. Dargie and C. Poellabauer, Wiley and Sons, 2011).
Sir can you tell me what is the application of Sybil attack and sinkhole attack in wsn
In a wireless sensor network, the predominant direction of traffic is directed towards the sink or the base station. All nodes must send their data to the sink. A malicious pretending to be a sink and deceiving nodes to direct their packet towards it causes a sink redirection and the attack is labelled as a sinkhole attack. It is one of the most dangerous types of attacks. This attack takes places when the malicious node propagates a false routing update request pretending to be the authentic sink. Sybil attack is in general the attempt by several malicious nodes into the network. These nodes, if admitted, can congest the network by overwhelming intermediate nodes to forward spurious packets, sabotage the network by dropping packets which they are requested to forward; etc.
Is there a leap between the formula of w_k and the posterior distribution P(x^i_(0:k) | D_k) in the last 5 mins? I can't link them together and where does the drac function come from?
You should note two distinct steps we have made in getting representative samples: In the first, we expressed the posterior probability in terms of three components, namely, the prediction, transition, and measurement probabilities. Then we transform this expression into an iterative weight function, but still a posterior probability (17:30). In the second, we discretise the weight function using the Dirac delta function. Any continuous signal can be expressed by the Dirac delta function, which is what we did in the last step. This enables us to get a countable number of samples which represent the continuous (and often a complex) posterior function.
@@waltenegus Thank you so much for the help. I am confused about the transformation from steps 1 to 2. In particular, I wonder how the expression of posterior probability P(X_{0:k} | D_k) is transformed to a expected function E{f(x)}. It seems to me in video(14:33) that P(x_{0:k} | D_k) = E{f(x)} in some way, but I can make sense of it myself and would much appreciate it if you could clarify it further.
Another question in 21:23, why q(x^i_k, x^i_{0:k-1 | D_k) equals q(x^i_k, x^i_{0:k-1 | D_{k-1}) ? I noticed the measurements D change from D_k to D_{k -1} in the denominator and wonder how to derive it.
Finally an intelligent, sensitive and penetrating insight into the Four Quartets and not just a fraudulent Christian imposition.
Thank you!, ETH
Astonishingly explored and expressed. Thank you, Dr. Waltenegus.
Hello and thanks a lot for this very nice video! I have a question about the probabilities: at m.ua-cam.com/video/jinhvR9jDZY/v-deo.html%3Ft%3D1h07m50s The equation p+pˆ2 + 2 pˆ3 can be greater than 1. Some normalization needed, or I did not get it well? Thank you so much!
This expression refers to the number of routes leading to the node and yes, it can be (indeed, it must be) greater than one. To condition it between 0 and 1, there must be a normalisation factor.
Thanks for your answer!
nietsche>>>>>>>>>>>>>fyodor.
Great Sir...!
Audio quality is poor.. Good content
Great lecture.
thank you
thank you sir
Excellent material, excellent presentation, thank you.
sir i am looking to do a project related to agriculture field study, can you suggest which routing protocols is apt for that to implement sir
If you are intending to monitor such parameters as relative humidity, temperature, moisture, radiation, and so on, you may consider RTL or any of the tree-based routing protocols, which are relatively easy to deploy. Most of them are implemented for the Contiki platform (github.com/contiki-ng/contiki-ng). The question is how big is your network? Alternatively, you may cluster the network (ieeexplore.ieee.org/abstract/document/9082144) and use the cluster-heads to collect data ifficiently.
thank you 💐
That was so intersting
Thank you for the material, really informative
Hello Teacher WALT DARGIE thank you verry match for the video. 1.please Teacher if you can help me in my subject of doctorate I have just made my first inscription in a subject in the networks of sensors. The subject of my doctaort is The reliability of data transmission in wireless sensor networks. 2. PLEASE IF YOU CAN SEND ME THIS PRESENTATION. THANKS
great lecture sir..Thank you
great lecture sir...lots of respect and love from india
please i need security WSN can help me
Can you PPT link?
This is a really good series for someone who knows the basics of communication engineering to understand the fundamentals of wireless sensor networks. Thanks a ton professor.
Best faster than 1.75x
Where are parts I & ii ?
Part I and II are on Minimum Mean Square Estimation (linear and nonlinear), which are the basis for Kalman Filter. You will find these lectures in the same playlist.
Professor, you changed the variable notations too often in this lecture, which makes it hard to follow. x_underscore should have been kept S. Nonetheless thank you for the lecture.
You are right; I apologise. I am planning to release a new version this semester. Bear with me. But if you have any question, let me know.
@@waltenegus Great! Thanks Professor.
Dear prof, Slides are not available through the course website, how can i get them? Thanks in advance
Unfortunately this is available only from within the university's website. The slides are based on the book: Fundamentals of Wireless Sensor Networks: Theory and Practice.
thank you again for this lecture, Can we ask a series of lectures from a to z regarding modelling sensor networks .
very good lecture , thank you
Appreciate the videos. Dr, recently I am doing the project of optimizing the smart farming WSN(energy consumption, latency) using ML. Can you recommend some papers related to this topic with the energy module, which are better based on python? The last question is, I was confused about the differences between MAC protocol and other routing protocols.
Difference between MAC and Routing: Suppose you are in a class room, with many students. You would like to address your professor. What do you do? You wait until all are silent (otherwise, there will be interference). This action is equivalent to the decision of a MAC protocol. Nodes first listen to communicate. Suppose now you wish to pass a book to one of your peers sitting at another end of the room. What do you do? You pass it to one of your neighbour (which you choose) and your neighbour passes it to one his neighbour (which he chooses) and so on, until the book arrives at the destination. That is equivalent to routing -- choosing a neighbour and passing the book. Two decisions at once.
Best explanation, sir!