I need a hostel room but the hostel warden said that if any room is vacant after 31 August it will be given. So should i wait till 31 August or is there a chance that after spot allotment some room may be vacant in ramanujan hostel
I got year repeat in first year as now I have two options either Ex-student or Re-admission what's the difference between them and how they are different
This is to say that I have completed my 12th this year, and I am looking for admission into B.Tech in Computer Science Engineering. At present, I am not sure whether to go for BBDU or BBDIT and hence your help will be appreciated.
These are my queries:
Which college has better CSE placements?
How is the teaching faculty and academics?
Does both of them have a decent coding community? (Clubs, hackathons, coding competition, etc.)
How is the scope of internships?
Overall campus life, crowd and hostels?
Any other difference between BBDU and BBDIT that should be known before admissions?
last year spot me iet lucknow chemical me admission liya tha aur partial drop ke liye socha tha lekin 8lakh rank hai ab is saal aur sem1 me m1 aur chem lab me back thi aur sem2 nahi diye the please help what should i do know 😭
last year spot me chemical me admission liya tha aur partial drop ke liye socha tha lekin 8lakh rank hai ab is saal aur sem1 me m1 aur chem lab me back thi aur sem2 nahi diye the please help what should i do know 😭
I recently built a Wi-Fi RSSI-based indoor positioning system entirely on ESP32 microcontrollers.
I used 3 ESP32s as anchor nodes broadcasting Wi-Fi signals
1)TestNetwork1
2)TestNetwork2
3)TestNetwork3
and a 4th ESP32 for scanning the RSSI signal strength from each anchor. It applies a Kalman filter to clean up the noise and then uses those filtered distances for trilateration to compute a real-time 2D position — all running on-device in MicroPython, no external computer needed.
To calculate the A and n parameters used in the path loss equation, I collected 50 RSSI samples at 1, 2, 3, and 4 metre distances and applied a moving average to smooth the readings. Then used least-squares regression to fit A = −61.92 dBm and n = 1.64.
Raw Captured Data For Computation Of A And N
Setup Used To Capture Signals At 1M
Distance Estimation After Capturing RSSI Signals
Kalman Filter vs Raw On Network1
Kalman Filter vs Raw On Network2
Moving Average Curve On Raw RSSI Captured For Calibration Of A And N
I recently built a Wi-Fi RSSI-based indoor positioning system entirely on ESP32 microcontrollers.
I used 3 ESP32s as anchor nodes broadcasting Wi-Fi signals
1)TestNetwork1
2)TestNetwork2
3)TestNetwork3
and a 4th ESP32 for scanning the RSSI signal strength from each anchor. It applies a Kalman filter to clean up the noise and then uses those filtered distances for trilateration to compute a real-time 2D position — all running on-device in MicroPython, no external computer needed.
To calculate the A and n parameters used in the path loss equation, I collected 50 RSSI samples at 1, 2, 3, and 4 metre distances and applied a moving average to smooth the readings. Then used least-squares regression to fit A = −61.92 dBm and n = 1.64.
Raw Captured Data For Computation Of A And N
Setup Used To Capture Signals At 1M
Distance Estimation After Capturing RSSI Signals
Kalman Filter vs Raw On Network1
Kalman Filter vs Raw On Network2
Moving Average Curve On Raw RSSI Captured For Calibration Of A And N
I recently built a Wi-Fi RSSI-based indoor positioning system entirely on ESP32 microcontrollers.
I used 3 ESP32s as anchor nodes broadcasting Wi-Fi signals
1)TestNetwork1
2)TestNetwork2
3)TestNetwork3
and a 4th ESP32 for scanning the RSSI signal strength from each anchor. It applies a Kalman filter to clean up the noise and then uses those filtered distances for trilateration to compute a real-time 2D position — all running on-device in MicroPython, no external computer needed.
To calculate the A and n parameters used in the path loss equation, I collected 50 RSSI samples at 1, 2, 3, and 4 metre distances and applied a moving average to smooth the readings. Then used least-squares regression to fit A = −61.92 dBm and n = 1.64.
Raw Captured Data For Computation Of A And N
Setup Used To Capture Signals At 1M
Distance Estimation After Capturing RSSI Signals
Kalman Filter vs Raw On Network1
Kalman Filter vs Raw On Network2
Moving Average Curve On Raw RSSI Captured For Calibration Of A And N
