Abstract: The invention refers to a diffusion plate and a backlight module having the diffusion plate. The diffusion plate comprises a plate-body and a plurality of pyramid-like structures arranged on a surface of the plate-body. Each pyramid-like structure has a bottom surface, a first convex portion and a second convex portion. The first convex portion and the second convex portion have different vertex angles, and therefore the pyramid-like structure can also be called as “pyramid-like structure with multiple vertex angles”. The pyramid-like structures with multiple vertex angles can increase the light splitting points, which can improve the light splitting effect of the diffusion plate. The light source of a single light-emitting diode can be divided into eight point-light sources (light splitting points) or more, which is double the number of light splitting points compared with the traditional pyramid structure with single vertex, and thus can greatly improve the light diffusion effect.

Abstract: A method for mobility enhancement in wireless communication systems is provided. The method is performed by a User Equipment (UE) configured with a first Small Data Transmission (SDT) configuration by a first cell. The method includes receiving a Radio Resource Control (RRC) release message including a suspend configuration from the first cell; transitioning to an RRC INACTIVE state in response to receiving the RRC release message; receiving, in the RRC INACTIVE state, a System Information Block Type 1 (SIB1) including a second SDT configuration from a second cell; camping on the second cell in response to receiving the SIB1 from the second cell; and while the UE is camping on the second cell, refraining from using the first SDT configuration to initiate an SDT procedure associated with the second cell in a case that the UE does not support performing the SDT procedure associated with the second cell.

Abstract: An example system can include a noise sensor communicatively coupled to a controller of a computing device to dynamically determine a sound pressure level (SPL) of an environment in which the computing device is present. The computing device can include a cooling fan and the controller comprising a processor in communication with a memory resource including instructions executable to dynamically determine a threshold speed of the cooling fan based on the determined SPL of the environment set a speed of the cooling fan based on the determined threshold speed.

Abstract: An inductor device includes a first trace, a second trace, and a capacitor. The first trace includes a first sub-trace and a second sub-trace. The first sub-trace includes a plurality of first wires. The second sub-trace includes a plurality of second wires, wherein the first wires and the second wires are disposed on the same layer. The first wires and the second wires are disposed to each other in an interlaced manner, and located at a first corner of the inductor device. The second trace includes a third sub-trace and a fourth sub-trace. The capacitor is coupled between the first sub-trace and the third sub-trace.

Abstract: An inductor device includes a first trace, a second trace, and a capacitor. The first trace includes at least two sub-traces and a first crossing connection portion. One terminal of the at least two sub-traces is coupled to a first node. The first crossing connection portion is coupled between the at least two sub-traces of the first trace in an interlaced manner. The second trace includes at least two sub-traces. One terminal of the at least two sub-traces is coupled at a second node. The capacitor is coupled between the first node and the second node.

Abstract: A radio frequency apparatus includes a power amplifier circuit, a signal coupling circuit, an extraction circuit, and a harmonic filter circuit. The power amplifier circuit is configured to amplify a differential signal to output a to-be-filtered signal. The signal coupling circuit includes a primary side inductor and a secondary side inductor. The signal coupling circuit is configured to convert the to-be-filtered signal received by the primary side inductor into a single-ended signal outputted from the secondary side inductor. The extraction circuit has a center tap. The extraction circuit is configured to inductively couple to the primary side inductor and output a common mode signal from the center tap. The harmonic filter circuit is configured to perform a harmonic filtering on the single-ended signal according to the common mode signal, such that the secondary side inductor of the signal coupling circuit outputs a filtered signal.

Abstract: The present invention provides a calibration method of a transmitter, wherein the transmitter includes a power amplifier, a transformer, an adjusting circuit and a coupling circuit, wherein the power amplifier receives an input signal to generate an amplified input signal, the transformer receives the amplified input signal to generate an output signal, the adjusting circuit adjusts phase and amplitude of a common mode signal of the amplified input signal to generate a first signal, and the coupling circuit generates a coupled signal to the output signal according to the first signal. In addition, the calibration method includes: controlling the adjusting circuit to have multiple combination; calculating a strength of a second harmonic of the output signal under each combination; and determining a specific condition according to the intensities of the second harmonics under the combinations.

Abstract: An inductor device includes a first trace, a second trace, and a capacitor. The first trace includes a first and a second sub-trace. The first sub-trace includes first wires, and the second sub-trace includes second wires. The second sub-trace is coupled to the first sub-trace at a first node. The first and the second wires are disposed to each other in an interlaced manner, and located at an outer side of the inductor device. The second trace includes a third and a fourth sub-trace. The third sub-trace includes third wires, and the fourth sub-trace includes fourth wires. The fourth sub-trace is coupled to the third sub-trace at a second node. The third and the fourth wires are disposed to each other in an interlaced manner, and located at an outer side of the inductor device. The capacitor is coupled between the first and the second node.

Abstract: A method for using artificial neural networks as a universal computing device to model the relationship between the training inputs and corresponding outputs and to solve all problems with estimation, classification, and ranking tasks in their nature. Raw data related to problems is obtained and a subset of that data is processed and distilled for application to this universal computing device. The training data includes inputs and their corresponding results, which values could be continuous, categorical, or binary. The goal of this universal computing device is to solve problems by the universal approximation property of artificial neural networks. In this invention, a practical solution is created to resolve the issues of local minima and generalization, which have been the obstacles to the use of artificial neural networks for decades. This universal computing device uses an efficient and effective search algorithm, Retreat and Turn, to escape local minima and approach the best solutions.

Abstract: A method for using machine learning to solve problems having either a “positive” result (the event occurred) or a “negative” result (the event did not occur), in which the probability of a positive result is very low and the consequences of the positive result are significant. Training data is obtained and a subset of that data is distilled for application to a machine learning system. The training data includes some records corresponding to the positive result, some nearest neighbors from the records corresponding to the negative result, and some other records corresponding to the negative result. The machine learning system uses a co-evolution approach to obtain a rule set for predicting results after a number of cycles. The machine system uses a fitness function derived for use with the type of problem, such as a fitness function based on the sensitivity and positive predictive value of the rules. The rules are validated using the entire set of training data.

Abstract: A method for using machine learning to solve problems having either a “positive” result (the event occurred) or a “negative” result (the event did not occur), in which the probability of a positive result is very low and the consequences of the positive result are significant. Training data is obtained and a subset of that data is distilled for application to a machine learning system. The training data includes some records corresponding to the positive result, some nearest neighbors from the records corresponding to the negative result, and some other records corresponding to the negative result. The machine learning system uses a co-evolution approach to obtain a rule set for predicting results after a number of cycles. The machine system uses a fitness function derived for use with the type of problem, such as a fitness function based on the sensitivity and positive predictive value of the rules. The rules are validated using the entire set of training data.

Abstract: A method for using machine learning to solve problems having either a “positive” result (the event occurred) or a “negative” result (the event did not occur), in which the probability of a positive result is very low and the consequences of the positive result are significant. Training data is obtained and a subset of that data is distilled for application to a machine learning system. The training data includes some records corresponding to the positive result, some nearest neighbors from the records corresponding to the negative result, and some other records corresponding to the negative result. The machine learning system uses a co-evolution approach to obtain a rule set for predicting results after a number of cycles. The machine system uses a fitness function derived for use with the type of problem, such as a fitness function based on the sensitivity and positive predictive value of the rules. The rules are validated using the entire set of training data.

Abstract: A method for using machine learning to solve problems having either a “positive” result (the event occurred) or a “negative” result (the event did not occur), in which the probability of a positive result is very low and the consequences of the positive result are significant. Training data is obtained and a subset of that data is distilled for application to a machine learning system. The training data includes some records corresponding to the positive result, some nearest neighbors from the records corresponding to the negative result, and some other records corresponding to the negative result. The machine learning system uses a co-evolution approach to obtain a rule set for predicting results after a number of cycles. The machine system uses a fitness function derived for use with the type of problem, such as a fitness function based on the sensitivity and positive predictive value of the rules. The rules are validated using the entire set of training data.

Abstract: A method for using machine learning to solve problems having either a “positive” result (the event occurred) or a “negative” result (the event did not occur), in which the probability of a positive result is very low and the consequences of the positive result are significant. Training data is obtained and a subset of that data is distilled for application to a machine learning system. The training data includes some records corresponding to the positive result, some nearest neighbors from the records corresponding to the negative result, and some other records corresponding to the negative result. The machine learning system uses a co-evolution approach to obtain a rule set for predicting results after a number of cycles. The machine system uses a fitness function derived for use with the type of problem, such as a fitness function based on the sensitivity and positive predictive value of the rules. The rules are validated using the entire set of training data.