Memory for emotional trait adjectives in clinically depressed youth

An experiment was conducted to examine memory for emotional trait adjectives in depressed children and adolescents. Two groups of children and adolescents, clinically depressed participants and non-clinical controls, were compared on computerized versions of recall and recognition memory tasks. Three groups of words (positive trait adjectives, negative trait adjectives, and categorized neutral words) were used in the experiment. Results showed that the depressed group recalled significantly more negative adjectives than positive adjectives, whereas the control group recalled the same number of positive and negative adjectives. This effect was predicted by the association between age and level of depression, with the depression-related bias becoming stronger with age. Signal detection analysis revealed that the depressed group did not show any bias in the recognition task. The findings are discussed with respect to cognitive theories of depression with consideration of the developmental implications.     

New Approaches to Routing in Mobile Ad hoc Networks

Worldwide Interoperability for Microwave Access (Wimax) is power station through which mobile network, commonly known as A Mobile Ad-hoc Network (MANET) is used by the people. A MANET can be described as an infrastructure-less and self-configure network with autonomous nodes. Participated nodes in MANETs move through the network constantly causing frequent topology changes. Designing suitable routing protocols to handle the dynamic topology changes in MANETs can enhance the performance of the network. In this regard, this paper proposes four algorithms for the routing problem in MANETs. First, we propose a new method called Classical Logic-based Routing Algorithm for the routing problem in MANETs. Second is a routing algorithm named Fuzzy Logic-based Routing Algorithm (FLRA). Third, a Reinforcement Learning-based Routing Algorithm is proposed to construct optimal paths in MANETs. Finally, a fuzzy logic-based method is accompanied with reinforcement learning to mitigate existing problems in FLRA. This algorithm is called Reinforcement Learning and Fuzzy Logic-based (RLFLRA) Routing Algorithm. Our proposed approaches can be deployed in dynamic environments and take four important fuzzy variables such as available bandwidth, residual energy, mobility speed, and hop-count into consideration. Simulation results depict that learning process has a great impact on network performance and RLFLRA outperforms other proposed algorithms in terms of throughput, route discovery time, packet delivery ratio, network access delay, and hop-count.

     

Low phase-noise VCO utilizing modified symmetric load and partial positive feedback for FDSM

The voltage-controlled oscillator (VCO) in frequency-based $\Updelta\Upsigma$ modulator (FDSM) systems behaves as a voltage-to-phase integrator converting an analog input voltage to phase information. Tuning range and phase noise are the most important factors of the basic design of a VCO in FDSM systems. In this paper a novel low phase-noise and wide tuning-range differential VCO based on a differential ring oscillator with modified symmetric load and a partial positive feedback in the differential delay cell is presented. The VCO is combined with a new bias circuit and implemented using 90 nm CMOS process technology. By using modified NMOS symmetric loads and a PMOS tail for delay cells, the VCO phase noise can be reduced with more than 13 dB compared to that of the conventional approach, achieving ?125 dBc/Hz at 500 kHz offset from the center frequency of 450 MHz. The wide tuning-range by using two added transistors (parallel with the active loads) increases the operating frequency range by about 22%, while the partial positive feedback provides the necessary bias condition for the circuit to oscillate. The designed VCO operating at a low power supply voltage of 0.6V can achieve low power consumption of 670???W at oscillation frequency of 800 MHz and good linearity reducing harmonic distortion in the $\Updelta\Upsigma$ modulator.     

Hybrid Fractal-Wavelet Method for Multi-Channel EEG Signal Compression

In this paper, a hybrid method is proposed for multi-channel electroencephalograms (EEG) signal compression. This new method takes advantage of two different compression techniques: fractal and wavelet-based coding. First, an effective decorrelation is performed through the principal component analysis of different channels to efficiently compress the multi-channel EEG data. Then, the decorrelated EEG signal is decomposed using wavelet packet transform (WPT). Finally, fractal encoding is applied to the low frequency coefficients of WPT, and a modified wavelet-based coding is used for coding the remaining high frequency coefficients. This new method provides improved compression results as compared to the wavelet and fractal compression methods.     

Synthesis of copolymers of a linoleic acid derivative and properties of the copolymer films

Addition copolymers of maleic anhydride and a commercially available conjugated fatty acid, Pamolyn 380, were synthesized via thermal initiation. The copolymers had moderately high molecular weights and were obtained in reasonable yields. The copolymers were characterized by nuclear magnetic resonance and infrared spectroscopy and by elemental analysis. Based on this analytical data and examples from the literature, the copolymers were assigned regularly alternating chain morphology. Coatings were formulated with the copolymers, and films over a metal substrate were evaluated. The films were found to have excellent solvent resistance, high hardness, and good gloss. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 80: 261–267, 2001