An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes....

Anticipatory neural activity preceding behaviorally important events has been reported in cortex, striatum, and midbrain dopamine neurons. Whereas dopamine neurons are phasically activated by reward-predictive stimuli, anticipatory activity of cortical and striatal neurons is increased during delay periods before important events. Characteristics of dopamine neuron activity resemble those of th...

Surprise drives learning. Various neural "prediction error" signals are believed to underpin surprise-based reinforcement learning. Here, we report a surprise signal that reflects reinforcement learning but is neither un/signed reward prediction error (RPE) nor un/signed state prediction error (SPE). To exclude these alternatives, we measured surprise responses in the absence of RPE and account...

In this paper we present a complex linear prediction analysis method for estimating the formant frequencies of noisy speech. The proposed method effectively utilizes the signal (being the analytic signal) which is ignored in the conventional complex linear prediction analysis to achieve noise reduction. Also, the covariance and forward-backward linear prediction (FBLP) methods are compared, and...

Brain processing of reward information is essential for complex functions such as learning and motivation. Recent primate electrophysiological studies using concepts from information, economic and learning theories indicate that the midbrain may code two statistical parameters of reward information: a transient reward error prediction signal that varies linearly with reward probability and a su...

Digital sync signal processing for asynchronously digitized video signals is presented. A simplified matched filter for sync extraction and a linear prediction method for sync pulse filtering improves image stability significantly in comparison with a conventional phase-locked-loop (PLL) approach. Due to the linear prediction method, additional adaption to phase skips in the input signal (non-s...

In this paper, a new statistic prediction model has been proposed. The new model can be adaptive to the signal sources. By combining the model with arithmetic coding, a new prediction-entropy coding method is proposed. This method can gain finer result when the signal source has obvious local comparability such as the texture characteristic in an image. Its all-around performance is better than...

A general parameter (GP) extension to expand the applicability of discrete time finite impulse response (FIR) filters designed for sinusoidal or polynomial signal prediction while attenuating white noise is proposed. These FIR filters are simple and powerful linear methods for filtering, predicting and modeling many real world phenomena. With only a single adaptive GP, it is computationally eff...

Accurate prediction on energy consumption in machining is helpful to evaluate process energy characteristics and choose process methods for energy saving. Specific energy consumption expresses the required energy consumption when cutting unit volume material. The Back Propagation (BP) neural network prediction method for specific energy consumption in machining is set up in the paper. The predi...

This paper presents new site specific propagation prediction methods for emerging personal communication system (PCS) services which will offer high capacity in urban settings. The propagation prediction models, composed of three dimensional dif· fracti"on and ray tracing, are being implemented as software tools on SUN computer workstations. A Geo'graphical Information System (GIS) software pac...