A Network Charge-Orineted MOS Transistor Model

The MOS transistor physical model as described in [3] is presented here as a network model. The goal is to obtain an accurate model, suitable for simulation, free from certain problems reported in the literature [13], and conceptually as simple as possible. To achieve this goal the original model had to be extended and modified. The paper presents the derivation of the network model from physical equations, including the corrections which are required for simulation and which compensate for simplifications introduced in the original physical model. Our intrinsic MOS model consists of three nonlinear voltage-controlled capacitors and a dependent current source. The charges of the capacitors and the current of the current source are functions of the voltages $V_{gs}$, $V_{bs}$, and $V_{ds}$. The complete model consists of the intrinsic model plus the parasitics. The apparent simplicity of the model is a result of hiding information in the characteristics of the nonlinear components. The resulted network model has been checked by simulation and analysis. It is shown that the network model is suitable for simulation: It is defined for any value of the voltages; the functions involved are continuous and satisfy Lipschitz conditions with no jumps at region boundaries; Derivatives have been computed symbolically and are available for use by the Newton-Raphson method. The model"s functions can be measured from the terminals. It is also shown that small channel effects can be included in the model. Higher frequency effects can be modeled by using a network consisting of several sections of the basic lumped model. Future plans include a detailed comparison of the network model with models such as SPICE level 3 and a comparison of the multi- section higher frequency model with experiments.

A Trainable Object Detection System: Car Detection in Static Images

This paper describes a general, trainable architecture for object detection that has previously been applied to face and peoplesdetection with a new application to car detection in static images. Our technique is a learning based approach that uses a set of labeled training data from which an implicit model of an object class -- here, cars -- is learned. Instead of pixel representations that may be noisy and therefore not provide a compact representation for learning, our training images are transformed from pixel space to that of Haar wavelets that respond to local, oriented, multiscale intensity differences. These feature vectors are then used to train a support vector machine classifier. The detection of cars in images is an important step in applications such as traffic monitoring, driver assistance systems, and surveillance, among others. We show several examples of car detection on out-of-sample images and show an ROC curve that highlights the performance of our system.

Static Pricing for a Network Service Provider

This article studies the static pricing problem of a network service provider who has a fixed capacity and faces different types of customers (classes). Each type of customers can have its own capacity constraint but it is assumed that all classes have the same resource requirement. The provider must decide a static price for each class. The customer types are characterized by their arrival process, with a price-dependant arrival rate, and the random time they remain in the system. Many real-life situations could fit in this framework, for example an Internet provider or a call center, but originally this problem was thought for a company that sells phone-cards and needs to set the price-per-minute for each destination. Our goal is to characterize the optimal static prices in order to maximize the provider's revenue. We note that the model here presented, with some slight modifications and additional assumptions can be used in those cases when the objective is to maximize social welfare.