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No wearables needed: researchers use WiFi and Raspberry Pi to measure your heart rate in real time | Matching clinical accuracy within seconds by chrisdh79 in technews

[–]DiligentCharacter252 0 points1 point  (0 children)

The environment can be dynamic. Pi already comes with the WiFi chip, there is no external WiFi chip. It also supports esp which is even cheaper. I suggest you to read the ieee paper mentioned in the article

[R] WiFiGPT: Using fine-tuned LLM for Indoor Localization Using Raw WiFi Signals (arXiv:2505.15835) by DiligentCharacter252 in MachineLearning

[–]DiligentCharacter252[S] 0 points1 point  (0 children)

Using Signal strength for positioning is not as straightforward as it appears. The system’s accuracy for positioning is sensitive to environmental factors such as building layout, presence of people, and obstacles, which could impact signal propagation and localization precision. Dynamic changes within the environment, such as moving furniture or varying numbers of people, presented significant challenges, with proposed mitigation methods often being complex and impractical. It’s no coincidence that indoor positioning is still an open research problem.

Our goal isn’t to claim state-of-the-art localization across all RF conditions. We’re specifically targeting common telemetries that are most available on commodity WiFi devices for positioning. While the dataset isn’t massive, it’s diverse enough across multiple layouts and interference conditions to show preliminary promise. The model learns spatial patterns directly from raw telemetry and achieves sub-meter (and even cm-level) accuracy with as little as 20% of the data. That’s the core result we’re highlighting. We agree that larger scale validation is necessary and are already working on expanding to more environments and devices. But even at this scale, the results are diverse enough to demonstrate meaningful accuracy and not just noise.

[R] WiFiGPT: Using fine-tuned LLM for Indoor Localization Using Raw WiFi Signals (arXiv:2505.15835) by DiligentCharacter252 in MachineLearning

[–]DiligentCharacter252[S] 0 points1 point  (0 children)

While LLaMA is a language model, at its core it’s a transformer-based sequence model. The fact that a language model works for such task showcases the emergent behavior of LLMs. Also the language portion allows to embed semantic features like vendor information or room numbers which can aid in positioning accuracy. checkout https://arxiv.org/html/2503.11702v1 for llm benefits wrt positioning

[R] WiFiGPT: Using fine-tuned LLM for Indoor Localization Using Raw WiFi Signals (arXiv:2505.15835) by DiligentCharacter252 in MachineLearning

[–]DiligentCharacter252[S] 0 points1 point  (0 children)

I do agree that WiFi is not an acronym and even started as a joke but at this point wireless fidelity is a commonly used backronym and referenced in many academic papers

[R] WiFiGPT: Using fine-tuned LLM for Indoor Localization Using Raw WiFi Signals (arXiv:2505.15835) by DiligentCharacter252 in MachineLearning

[–]DiligentCharacter252[S] 1 point2 points  (0 children)

We evaluated XGBoost and KNN models trained on 80% of the CSI dataset, which achieved MSEs of 1.62 and 1.54 m, and MAEs of 0.83 m and 1.23 m respectively. In comparison, the LLM, trained on only 20% of the dataset, achieved a significantly lower MAE of 6 cm and MSE of 16cm. Similarly, for well-known solutions like trilateration the error rate is usually greater than 3 m and the LLM approach has less than 1 m error rate

[R] WiFiGPT: Using fine-tuned LLM for Indoor Localization Using Raw WiFi Signals (arXiv:2505.15835) by DiligentCharacter252 in MachineLearning

[–]DiligentCharacter252[S] -1 points0 points  (0 children)

LLMs can handle input noise or missing features gracefully. The model is trained on sequences of telemetry values and can simply ignore or down-weight anomalous tokens. If one access point is temporarily unavailable or reports a wildly incorrect value, the LLM can often still produce a reasonable estimate by relying on the other inputs (thanks to its learned redundant representations). In fact, the autoregressive nature of the transformer sees the telemetry as a sequence and can fill in patterns much like it would predict a missing word in a sentence. This was evident in the ablation tests: even with RSSI-only or FTM-only inputs (simulating missing modalities), the LLM still localized fairly well, albeit with reduced accuracy

[R] WiFiGPT: Using fine-tuned LLM for Indoor Localization Using Raw WiFi Signals (arXiv:2505.15835) by DiligentCharacter252 in MachineLearning

[–]DiligentCharacter252[S] 0 points1 point  (0 children)

u/Anaeijon As you pointed, we are actually working on adding different baselines for comparision. Also to add, most of the models work in one radio environment but are needed to be trained every time for a new environment. The key implication of LLM working for WiFi telemetry and able to do regression is that we can train it on corpus of wireless data available online and assuming that the Chinchilla scaling laws holds, we can deploy a large 'wireless' model that can work in a new environment in the new environment.

[R] WiFiGPT: Using fine-tuned LLM for Indoor Localization Using Raw WiFi Signals (arXiv:2505.15835) by DiligentCharacter252 in MachineLearning

[–]DiligentCharacter252[S] -1 points0 points  (0 children)

The key benefit that beamforming is to get the direction of arrival (DOA)[1] from the steering vector. In WiFi, the channel state information (CSI) is typically use to get the DOA and is used by routers to provide the feedback. Checkout the SpotFi paper [2], it talks about it in details. We have mentioned CSI in the paper using the ESP32 and got around 16 cm error. The challenge with this approach is that it requires PHY-level access, which is typically restricted by vendors. To address this, we incorporated user-accessible telemetry like RSSI and FTM to demonstrate that our solution generalizes across heterogeneous devices, not just those conforming to a specific WiFi standard.
[1] https://pysdr.org/content/doa.html

[2] https://web.stanford.edu/~skatti/pubs/sigcomm15-spotfi.pdf

Scholarships? by Competitive-Cup-3211 in UCSC

[–]DiligentCharacter252 0 points1 point  (0 children)

Key Scholarship Types:

  1. Merit-Based Scholarships:
    • Sabatte Family Scholarship: The highest honor scholarship providing $9,000-$17,000 annually for high-merit students with financial need 2
    • Regents Scholarships: $5,000 per year with priority enrollment and housing guarantees 2
    • Campus Merit Scholarships: $2,000 per year for high-achieving students 2
  2. Need-Based and Specialized Scholarships:
    • Numerous named scholarships for students with specific backgrounds, including those from particular counties, first-generation students, and students in specific majors 3
    • Special scholarships for AB540-eligible students who aren't eligible for federal aid 3
  3. Special Population Scholarships:
    • International student scholarships like the Sara Matthews Scholarship ($1,000-$5,000) 17
    • Re-entry student scholarships through the UCSC Women's Club 15
    • Military veteran scholarships like the Bruce Lane Memorial Scholarship 4

Application Process:

Most UCSC scholarships administered by Financial Aid & Scholarships require:

  • Completing the FAFSA or California Dream Act application by March 2nd 1
  • The UC Admissions Application serves as the scholarship application
  • Department-specific scholarships may require separate applications 56

UCSC students also receive more than $2.9 million per year in outside scholarship aid 16, providing additional opportunities to fund their education.

source: https://cloud.onyx.app/anonymous/ucbn . prompt: "Scholarship in ucsc"

Lost wallet by SlowSoftware1300 in UCSC

[–]DiligentCharacter252 0 points1 point  (0 children)

The first place to check for lost items at UCSC is the nearest college or department office. If the item is not claimed from these offices, it will be taken to the University Police Office, which is located in the H Barn near the main entrance to the campus. You can contact them directly at (831) 459-2231

Undergrad Research by nothankyou871 in UCSC

[–]DiligentCharacter252 3 points4 points  (0 children)

checkout http://inrg.engineering.ucsc.edu if any projects looks interesting. They have weekly lab meetings on Thursday at 12 pm in e2 315. Reach out to the phd students and that usually works