This paper describes two different types of GaAs-HBT compatible, base-collector diode 0/20-dB step attenuators--diode-linearizer type and harmonics-trap type--for 3.5-GHz-band wireless applications. The two attenuators use an AC-coupled, stacked type diode switch topology featuring high power handling capability with low bias current operation. Compared to a conventional diode switch topology, this topology can improve the capability of more than 6 dB with the same bias current. In addition, successful incorporation of a shunt diode linearizer and second- and third-harmonic traps into the attenuators gives the IM3 distortion improvement of more than 7 dB in the high power ranging from 16 dBm to 18 dBm even in the 20-dB attenuation mode when IM3 distortion levels are basically easy to degrade. Measurement results show that both the attenuators are capable of delivering power handling capability (P0.2 dB) of more than 18 dBm with IM3 levels of less than -35 dBc at an 18-dBm input power while drawing low bias currents of 3.8 mA and 6.8 mA in the thru and attenuation modes from 0/5-V complementary supplies. Measured insertion losses of the linearizer-type and harmonics-trap type attenuators in the thru mode are as low as 1.4 dB and 2.5 dB, respectively.

This paper presents a new dimension in e-learning by collecting and analyzing physiological data during real-world e-learning sessions. Two different content materials, namely Interactive (IM) and Non-interactive (N-IM), were utilized to determine the physiological state of e-learners. Electrocardiogram (ECG) and Skin Conductance Level (SCL) were recorded continuously while learners experienced IM and N-IM for about 25 minutes each. Data from 18 students were collected for analysis. As a result significant difference between IM and N-IM was observed in SCL (p <.01) meanwhile there were no significance in other indices such as heart rate and its variability, and skin conductance response (SCR). This study suggests a new path in understanding e-learners' physiological state with regard to different e-learning materials; the results of this study suggest a clear distinction in physiological states in the context of different learning materials.