This paper presents an electromyographic (EMG) pattern recognition method to identify motion commands for the control of a prosthetic arm by evidence accumulation based on artificial intelligence with multiple parameters. The integral absolute value, variance, autoregressive (AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

A new architecture to improve the SNR of the readout circuit for capacitive TSPs is introduced. The architecture shows better performance in spite of requiring less power consumption and using an integration capacitor that is roughly half in size compared with a previous work. It averages noises by repeated integration and substantially mitigates the effect of display noise through a differential sensing technique. Furthermore, it does not require additional calibration circuitry to correct the settling error by different delay paths because two adjacent lines in differential sensing have almost the same R-C time constant. The circuit is designed in a 0.35 m CMOS technology.

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To verify the spectrum of CD99-expressing lymphoid malignancy, an immunohistochemical study for CD99 was carried out in 182 cases of non-Hodgkin's lymphoma, including 21 lymphoblastic lymphomas, 11 small lymphocytic lymphomas, 9 mantle cell lymphomas, 12 follicular lymphomas, 37 diffuse large B cell lymphomas, 18 Burkitt's lymphomas, 28 NK/T-cell lymphomas, 8 angioimmunoblastic T-cell lymphomas, 23 peripheral T-cell lymphomas, unspecified, and 15 systemic anaplastic large cell lymphomas. CD99 was positive in all T-lymphoblastic lymphomas and in 60% of B-lymphoblastic lymphomas. Majority of T and NK cell lymphomas were negative for CD99, except anaplastic large cell lymphomas (ALCLs). Eight of 15 cases (54%) of ALCLs reacted with anti CD99 antibody. Seven of 10 (70%) ALK positive ALCLs expressed CD99, whereas only 1 of 5 (20%) ALK negative ALCLs were positive. Of the mature B-cell lymphomas, 5.4% (2/37) of diffuse large B cell lymphomas and 11.1% (2/18) of Burkitt's lymphomas expressed CD99. In conclusion, CD99 is infrequently expressed in mature B and T cell lymphomas, except ALK-positive ALCL. High expression of CD99 in ALK-positive ALCL is unexpected finding and its biologic and clinical significances have yet to be clarified.