Abstract: The present disclosure relates to a novel integrate-and-fire (IF) neuron circuit using a single-gated feedback field-effect transistor (FBFET) to realize small size and low power consumption. According to the present disclosure, the neuron circuit according to one embodiment may generate potential by charging current input from synapses through a capacitor. In this case, when the generated potential exceeds a threshold value, the neuron circuit may generate and output a spike voltage corresponding to the generated potential using a single-gated feedback field-effect transistor connected to the capacitor. Then, the neuron circuit may reset the generated spike voltage using transistors connected to the feedback field-effect transistor.

Abstract: The present disclosure relates to a novel neuron circuit using a p-n-p-n diode to realize small size and low power consumption. The neuron circuit according to one embodiment of the present disclosure may generate potential by charging current input from synapses through a capacitor. In this case, when the generated potential exceeds a critical value, the neuron circuit may generate and output a spike voltage corresponding to the generated potential using a p-n-p-n diode connected to the capacitor.

Abstract: An integrated artificial neuron device includes an input signal node, an output signal node and a reference supply node. An integrator circuit receives and integrates an input signal to produce an integrated signal. A generator circuit receives the integrated signal and, when the integrated signal exceeds a threshold, delivers the output signal. The integrator circuit includes a main capacitor coupled between the input signal node and the reference supply node. The generator circuit includes a main MOS transistor coupled between the input signal node and the output signal node. The main MOS transistor has a gate that is coupled to the output signal node, and a substrate that is mutually coupled to the gate.

Abstract: A circuit for implementing an artificial neuron comprises: an integrator for an input signal to produce a voltage signal; a signal generator linked to the integrator output producing two output signals when the voltage is at or above a predetermined voltage, a first signal for an output pulse of the neuron and a second signal for a control pulse; a resistive memory comprising two terminals switching from a high to low resistance state in a time following a statistical distribution specific to the memory, a first terminal linked to the output of the integrator; a transistor linked to a branch at zero potential to a second terminal of the resistive memory, controlled by the second output signal such that in the presence of a pulse of voltage the resistive memory switches from its high resistance state to its low resistance state with a view to lowering the voltage.

Abstract: A learning and training process that memorizes traces of a learner solving an exercise, attaches meta labels to the learner traces, and compares the learner traces to reference traces of a referent solving the exercise, meta labels being attached to the reference traces.

Abstract: A system, computer program and computer-based method for managing casino operations. The system provides for employee management and information storage and retrieval for a significant number of casino operations. The system includes an attendance controller that receives, stores and displays attendance information for casino personnel. Casino personnel are issued cards that uniquely identify them and monitor them in the fulfillment of their roles. Players may also receive these types of cards, which can be used to record their casino activity and reward them for playing. The system provides for dealer rotation so that dealer rotation information may be received, stored and displayed. Organic light emitting diode devices, resident at casino tables, may be used to request and manage the table, including food and beverage requests for the table.

Abstract: A graph structure is generated to describe an area with a free area and an occupied area. In this case a topological graph structure for the free area is determined. A point of the topological graph structure is selected and for this a nearest adjacent occupied area point is determined. For this nearest adjacent occupied area point location information is determined. The graph structure is formed from at least the selected point of the topological graph structure and from the associated location information of the nearest adjacent occupied area point.

Abstract: A method of sensing the air/fuel ratio in a combustion chamber of an internal combustion engine that may be easily implemented by a respective low-cost device includes a pressure sensor and a learning machine that generates a sensing signal representing the air/fuel ratio by processing the waveform of the pressure in at least one cylinder of the engine. In practice, the learning machine extracts characteristic parameters of the waveform of the pressure and as a function of a certain number of them generates the sensing signal.

Abstract: A graph structure is generated to describe an area with a free area and an occupied area. In this case a topological graph structure for the free area is determined. A point of the topological graph structure is selected and for this a nearest adjacent occupied area point is determined. For this nearest adjacent occupied area point location information is determined. The graph structure is formed from at least the selected point of the topological graph structure and from the associated location information of the nearest adjacent occupied area point.

Abstract: A device for calculating numerical solutions for partial differential equations in successive intervals using adaptive meshes, comprises: a neural network part for producing predictions of gradients at a following interval based on gradients available from previous intervals, and a mesh adaptation part, associated with the neural network part, configured for adapting a mesh over a domain of a respective partial differential equation using the predictions, such that the mesh adaptively refines itself about emerging regions of complexity as the partial differential equation progresses over the successive intervals. The neural network part succeeds in its predictions since its use herein is equivalent to using time series function fitting techniques.

Abstract: A neural network for predicting values in non-linear functional mappings having a single hidden layer function generator (12) and an output layer (40). The single hidden layer function generator (12) is operable to receive one or more mapping inputs (x1) and generate a plurality of terms (14) from each mapping input. The plurality of terms generated by the single hidden layer function generator (12) includes at least one trigonometric term selected from the group comprising sin(x1), sin(2x1), sin(3x1), cos(x1), cos(2xl), cos(3xl), cosec(xl), cotan(xl), and being free of Gaussian and Sigmoidal terms.

Abstract: An output parameter estimation apparatus for estimating an output parameter from an input data set that is composed of a plurality of input parameters and that is obtained whenever sampling time series input data. In this output parameter estimation apparatus, a fuzzy inference rule is used to calculate a fitness degree of the input data set in one of a plurality of fields included in a space that is formed using at least one input parameter. According to the calculated fitness degree, introduction routes through which the input data set is to be inputted into a neural network are selected. The neural network is set in a connection condition corresponding to the field to which the input data set belongs, the connection condition having been determined in advance as a result of learning process. With this connection condition, the neural network estimates the output parameter from the input data set inputted through the selected introduction routes.

Abstract: In a computation system for a topological map, a winner-unit-candidate-set calculation module calculates a winner unit candidate set which is the set of candidates for a winner unit corresponding to an input vector at a current input timing, on the topological map. The calculation module calculates the winner unit candidate set on the basis of, for example, a winner unit having been calculated at a past input timing near the current input timing. A winner-unit calculation module calculates the winner unit which corresponds to the input vector at the current input timing, as the unit which has received the maximum input among the units contained in the winner unit candidate set calculated by the winner-unit-candidate-set calculation module.