ENROLL LINK
https://forms.gle/e9etdgn5pQmrUFyT9
Friday, 20 September 2024
Thursday, 15 August 2024
How to dump FPGA the RTL code
How to dump the RTL code into the FPGA
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RTL codes are developed in VHDL (Very high speed integrated circuirs - Hardware description language) or Verilog HDL.
The logical designs are realized using the Logical gates through combinational and sequential logics.
The circuits are converted into hardware understandable code
The converted codes are synthesized using XILINX ISE or any other integrated software environment and dumped into the FPGA through Hardware emulator
The important pins for making the programming or the dumping part is the TCK, TDI,TDO, TMS
TCK is the test clock, TDI is the Test data input, TDO is the Test data output and TMS are the Test mode select provided in the XILINX emulator
The emulator and the JTAG from fpga kit is configured one to one mechanism
usign Boundary scan the cable connectivities are tested and dumping process takes place.
the Dump file should be .JED
DM for more details.
Keep learning.
Thursday, 14 December 2023
VLSI-Pro : Chip Design fundamentals - Free Session Link
To initiate the VLSI technology learning, Here we are providing the Free session accessible to everyone.
VLSI Pro: Chip Design fundamentals
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Course Link : VLSI-Pro : Chip Design fundamentals
- The digital course teaches you how to get started with VLSI design fundamentals, starting with digital design and programming using VHDL.
- The session starts with Introduction to VLSI technology and Why we have to learn VLSI ?
- The basic VHDL code syntax and structure are explored with clear examples, which are helpful for making the learning easier.
- The software EDA tool utilized here is MODELSIM 6.3 or QUARTUS II Software. The tool is easy to download and no license is required for the tool usage.
- The Course is apt for Freshers, Job seekers looking for Skill development in VLSI design fundamentals.
- For more details |& Advanced session Contact us.
Keep learning !!!
Tuesday, 12 December 2023
Tech news update : New Video / 5G communication enhancement through QAM 64 & 256
DM FOR MORE DETAILS
Wednesday, 6 December 2023
Tech news update : New video uploaded on - ON CHIP IMAGE PROCESSING
PROJECT IDEAS
DM for more details
Sunday, 15 October 2023
Parkinson disease detction through ensemble of Voice patterns and Key stroke dynamics #PhD #researchtopic
What is Parkinson disease?
Parkinson's, is a progressive neurodegenerative disorder that primarily affects movement and can also cause a wide range of non-motor symptoms. It is a chronic condition that worsens over time, and while there is no cure, there are treatments available to manage its symptoms.
What are the symptoms of Parkinson disease ?
The primary symptoms of parkinson includes,
- tremers, Bradykinesia we call as slow in doing any activity,
- Muscle Rigidity that creates challenging muscle stiffness,
- Postural Instability unable to balance the co-ordination.
These symptoms are called as motorists symptoms since we can see the changes in movements. on the other hand, other symptoms are
- Depression and Anxiety: Many individuals with Parkinson's experience mood disorders, including depression and anxiety.
- Cognitive Changes: Cognitive impairment can occur, and some people may develop dementia in advanced stages.
- Sleep Disturbances: Sleep problems such as insomnia and restless leg syndrome are common.
- Autonomic Dysfunction: This can lead to issues like constipation, urinary problems, and orthostatic hypotension (a drop in blood pressure when standing up).
- Loss of Smell: A reduced ability to smell is often an early non-motor symptom.
- Speech and Swallowing Difficulties: Parkinson's can affect the muscles involved in speech and swallowing, leading to issues in these areas
How Voice data is helpful in analyzing Parkinson's?
Voice data analysis can be a valuable tool in diagnosing and monitoring Parkinson's disease. Parkinson's disease is a neurodegenerative disorder that affects motor function and can also impact speech.
Early Detection: Changes in speech patterns can be one of the early indicators of Parkinson's disease. Analyzing voice data for subtle changes in pitch, tone, and articulation can help in early diagnosis.
Monitoring Progression: As Parkinson's disease progresses, it can lead to more pronounced speech issues. Voice data analysis can be used to monitor how speech changes over time, which can be valuable for tracking the progression of the disease.
Objective Assessment: Unlike traditional clinical assessments, voice data analysis can provide objective data about speech patterns. This can be useful for both patients and healthcare professionals to track the impact of the disease.
Treatment Efficacy: Voice data analysis can also be used to assess the effectiveness of treatments and interventions. It can help determine if medication or therapy is improving speech quality.
How to Overcome Parkinson's?
There is currently no cure for Parkinson's disease, but various treatments are available to help manage its symptoms. Medications, physical therapy, occupational therapy, and, in some cases, surgical interventions like deep brain stimulation can be used to improve the quality of life and manage the condition. Parkinson's disease is a chronic condition that requires ongoing management and support, often involving a healthcare team that includes neurologists, physical therapists, and other specialists.
DM for more details !!!
#phdproject #researchtopic #phdsupport
Friday, 13 October 2023
Optimized SAD (OSAD) algorithm for Image similarity analysis
Optimized SAD (OSAD) algorithm
The Sum of Absolute Differences
(SAD) algorithm is a simple and widely used image processing technique used for
various computer vision tasks, including template matching and motion
estimation. It measures the similarity between two images by calculating the
absolute differences between corresponding pixel values and then summing up
these differences. The SAD algorithm is particularly useful in finding
similarities between a small template image and a larger target image.
Here's how the SAD algorithm works step by step:
- Template
and Target Images:
You have a template image (usually smaller) and a target image (usually
larger). The goal is to find where the template image best matches the
target image.
- Sliding
Window:
Place the template image at the top-left corner of the target image.
- Pixel-wise
Absolute Differences:
For each corresponding pixel in the template and target images, calculate
the absolute difference in pixel values. This is done by subtracting the
value of the corresponding pixel in the template from the value of the
pixel in the target image and taking the absolute value of the result.
- Sum
of Absolute Differences:
Sum up all the absolute differences calculated in step 3 to get a single
value representing the dissimilarity or "error" between the
template and the portion of the target image it currently covers.
- Move
the Window:
Slide the template one pixel to the right (or in any desired direction)
and repeat steps 3 and 4 to calculate the SAD value for the new position.
- Repeat: Continue sliding the
template over the target image until you have covered all possible
positions or until you find the position with the lowest SAD value.
- Matching
Location:
The position with the lowest SAD value represents the best match for the
template within the target image.
- Optimize
: the
results by comparing the obtained values with the reference images.
Applications of the SAD algorithm
include object detection, facial recognition, motion estimation in video
processing, and various pattern recognition tasks.
While the SAD algorithm is
conceptually straightforward, it can be computationally intensive, especially
for large images or when used in real-time applications. Therefore, optimizing
the algorithm, as mentioned in the previous response, can be crucial for
achieving good performance. This may involve using parallel processing,
efficient data structures, and other optimization techniques to speed up the
calculations.
Expected Energy Optimization for Real-Time Multiprocessor SoCs Running Periodic Tasks with Uncertain Execution Time
Design of energy-saving optimization approach for Multi-processor SoC task scheduling
Power saving is a challeging task in VLSI design perhaps the multiple processing SOC carry out more power consumption through system on chip model.
The low power VLSI architecture is capable of handling the leakage power within the circuit by altering the circuit level changes we call it as optimization.
Multi processor SOC architectures required optimized SOC systems with stable operating clock frequency.
The SoC blocks are composed of FSM enabled pipelines control mechanism. the prioritizing algorithm that holds various SOC modules put priority in the index thus handle the modules one by one without interrupting the other modules.
The best SOC platform also capable of handling the uncertain exceutions in the system architecture by providing safegaurd module as error monitor. the dynamic error monitor keep tracks the glitches and stack the pulses to produce recycled clock with stable Duty Cycle
DM for more details !!!
Thursday, 7 September 2023
Myocardial function detection through FPGA using Real-time sensor
Myocardial function detection through FPGA using Real-time sensor
Myocardial infection takes place when a continuous block of blood supply happens to the myocardium and the reason behind it is due to blood clots. the similar occurrence persists longer duration then it causes severe damage to heart muscles.
Due to viral infection, the myocardium is affected. it narrows down the activity of the coronary artery and causes severe heart attack.
The myocardial function is the ability of the myocardium to pump blood properly into the arteries and function properly.
understanding the stable functionality of the myocardium is important to treat various diseases. hence detection of myocardinal function is keenly monitored.
FPGAs and ASICs are utilized in various medical equipment to treat diseases in the early stages, so the detection mechanism needs to be more accurate.
FPGA-based Real-time MC infection diagnosis requires the following supporting modules.
Hardware modules
- Heart rate sensor
- PPG sensor
- Microcontroller
- FPGA/ ASIC
- Display devices
Software Modules
- SImulation Tool XILINX ISE
- QUARTUS II 6.3G
- Arduino Studio ISE
- Thing Speak Platform
Hardware Modules
Thursday, 3 August 2023
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