Presentation 1: Noise canceling in 1-D data Seri Rahayu Abd Rauf Fatima Boujarwah Juan Chen Liyana Mohd Sharipp Arti Thumar M2.

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Presentation 1: Noise canceling in 1-D data Seri Rahayu Abd Rauf Fatima Boujarwah Juan Chen Liyana Mohd Sharipp Arti Thumar M2

Overview Part of a research done in CMU –Micron: Intelligent Microsurgical Instruments project led by Prof Pradeep Khosla This design uses adaptive weighted-frequency algorithm for noise canceling purposes.

Project Background: Noise Canceling Chip 1. An essential block in a research project done here at CMU: Micron: Intelligent Microsurgical Instrument 2. Main objective of the research : To provide a technique to compensate the amount of errors due to “physiological hand tremor, jerk, low- frequency wander and also pathological movement disorder.” To enhance the accuracy of human-machine interfaces. To improve the living of patients with movement disorders. To increase performance in microsurgery applications. Source:

Applications Microsurgery Instrument Rehabilitation Image sources:

Applications Vehicle / Aircraft Maneuvering Hearing Aid Image sources:

How does the chip work? It uses adaptive weighted frequency algorithm in 1-D data. Input: Human motion signal with noise introduced by pathological and physiological tremor. Output: Noise suppressed signal for higher precision.

Block Diagram Source: Modeling and Canceling Tremor in Human-Machine Interfaces

Result Source: Modeling and Canceling Tremor in Human-Machine Interfaces

Transistor Estimate PartTransistors 8-bit Adders12x8x24 = bit Multipliers10x1200 = ROM800 Registers10x8x14 = 1160 Misc3000 Total≈ 19210

Status Design Proposal √ Architecture (in progress) To be done: Floor Plan Gate Level Design Component Layout Chip Layout Spice Simulation of Entire Chip

Problems…  Several inputs are too small for bit shifting (10^-7…) Value of M could be made greater for higher precision (More complex) Transistor count seems too simple…really, it is NOT

Goal Higher throughput

Alternatives Serpent encryption Branch predictor

Summary Why do we choose this?

Questions?