Augmentation of base classifier performance via HMMs on a handwritten character data set

This paper presents results of a study of the performance of several base classifiers for recognition of handwritten characters of the modern Latin alphabet. Base classification performance is further enhanced by utilizing Viterbi error correction by determining the Viterbi sequence. Hidden Markov Models (HMMs) models exploit relationships between letters within a word to determine the most likely sequence of characters. Four base classifiers are studied along with eight feature sets extracted from the handwritten dataset. The best classification performance after correction was 89.8%, and the average was 68.1%

Design of a SOIMUMPs Inertial Sensor and readout Charge Amplifier

This paper presents the design and post-layout characteristics of a differential capacitance based inertial accelerometer This includes a MEMS based mechanical sensing element and a CMOS charge amplifier, which is the first stage of a readout circuit. The mechanical sensor is designed according to the SOIMUMPs fabrication process technology, and the readout circuit targeted AMS 0.35um technology. Post layout simulations indicated a +/-5G dynamic range, a maximum bandwidth of 1.58 kHz, non-linearity of 0.077% and a resolution of 10.5 uG/Hz^0.5. The readout circuit charge amplifier is fully differential and incorporated in a switched capacitor (SC) topology with CDS.