Abstract: The Cloud-based Video Streaming Service (CVSS) architecture is disclosed to transcode video streams in an on-demand manner. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) demands of video streams. In particular, the architecture includes a QoS-aware scheduling method to efficiently map video streams to cloud resources, and a cost-aware dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS demands. Simulation results based on realistic cloud traces and with various workload conditions, demonstrate that the CVSS architecture can satisfy video streaming QoS demands and reduces the incurred cost of stream providers up to 70%.

Abstract: Disclosed herein is a method and device for absolute average deviation (AAD) pooling for a convolutional neural network accelerator. AAD utilizes the spatial locality of pixels using vertical and horizontal deviations to achieve higher accuracy, lower area, and lower power consumption than mixed pooling without increasing the computational complexity. AAD achieves 98% accuracy with lower computational and hardware costs compared to mixed pooling, making it an ideal pooling mechanism for an IoT CNN accelerator.

Abstract: The method for obfuscating hardware partially imitates the neural network perceptron, obfuscating the hardware design. This method obfuscates the design functionality and immunes integrated circuits against Trojan insertion. This method can also be used to check for the existence of faults inside chips. This method resolves the concern related to security and reliability when outsourcing the manufacture of integrated circuits.

Abstract: The method for obfuscating hardware partially imitates the neural network perceptron, obfuscating the hardware design. This method obfuscates the design functionality and immunes integrated circuits against Trojan insertion. This method can also be used to check for the existence of faults inside chips. This method resolves the concern related to security and reliability when outsourcing the manufacture of integrated circuits.

Abstract: The Cloud-based Video Streaming Service (CVSS) architecture is disclosed to transcode video streams in an on-demand manner. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) demands of video streams. In particular, the architecture includes a QoS-aware scheduling method to efficiently map video streams to cloud resources, and a cost-aware dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS demands. Simulation results based on realistic cloud traces and with various workload conditions, demonstrate that the CVSS architecture can satisfy video streaming QoS demands and reduces the incurred cost of stream providers up to 70%.

Abstract: The Cloud-based Video Streaming Service (CVSS) architecture is disclosed to transcode video streams in an on-demand manner. The architecture provides a platform for streaming service providers to utilize cloud resources in a cost-efficient manner and with respect to the Quality of Service (QoS) demands of video streams. In particular, the architecture includes a QoS-aware scheduling method to efficiently map video streams to cloud resources, and a cost-aware dynamic (i.e., elastic) resource provisioning policy that adapts the resource acquisition with respect to the video streaming QoS demands. Simulation results based on realistic cloud traces and with various workload conditions, demonstrate that the CVSS architecture can satisfy video streaming QoS demands and reduces the incurred cost of stream providers up to 70%.

Abstract: The method for creating integrated circuits (IC) protects the design of a manufactured IC from being copied or counterfeited. This method protects the design of an IC chip from deliberate copying and counterfeiting by reverse engineering to gain access to the critical points in the IC chip and to siphon its functions and design. The method makes the copying, counterfeiting, and controlling by addition of Trojan circuits during manufacturing almost impossible task. It also allows chip designers to outsource the final bonding of the tiers without any fears that their design may get compromised.

Abstract: Hardware failures are undesired, but a common problem in circuits. Such failures are inherently due to the aging of circuitry or variation in circumstances. In critical systems, customers demand that the system never fail. Several self-healing and fault tolerance techniques have been proposed in the literature for recovering a circuitry from a fault. Such techniques are helpful when a fault has already occurred, but they are typically uninformed about the possibility of an impending failure (i.e., fault prediction), which can be used as a pre-stage to fault tolerance and self-healing. Presented herein is a method for early prediction of circuit faults. Using Fast Fourier Transformation (FFT), Principal Component Analysis (PCA), and Convolutional Neural Network (CNN), circuit faults can be predicted at a transistor level.

Abstract: Disclosed herein is a method for making embryonic bio-inspired hardware efficient against faults through self-healing, fault prediction, and fault-prediction assisted self-healing. The disclosed self-healing recovers a faulty embryonic cell through innovative usage of healthy cells. Through experimentations, it is observed that self-healing is effective, but it takes a considerable amount of time for the hardware to recover from a fault that occurs suddenly without forewarning. To get over this problem of delay, novel deep learning-based formulations are utilized for fault predictions. The self-healing technique is then deployed along with the disclosed fault prediction methods to gauge the accuracy and delay of embryonic hardware.

Abstract: Video streams, either in form of on-demand streaming or live streaming, usually have to be transcoded based on the characteristics of clients' devices. Transcoding is a computationally expensive and time-consuming operation; therefore, streaming service providers currently store numerous transcoded versions of the same video to serve different types of client devices. Due to the expense of maintaining and upgrading storage and computing infrastructures, many streaming service providers recently are becoming reliant on cloud services. However, the challenge in utilizing cloud services for video transcoding is how to deploy cloud resources in a cost-efficient manner without any major impact on the quality of video streams. To address this challenge, in this paper, the Cloud-based Video Streaming Service (CVSS) architecture is disclosed to transcode video streams in an on-demand manner.

Abstract: A seizure prediction algorithm based on deep learning that integrates the feature extraction and classification processes into a single automated architecture is claimed herein. In the method, the computation complexity is reduced because there is no feature engineering. The method uses a novel algorithm for EEG channel selection in which the number of EEG channels is decreased to reduce the required memory for storing the data and parameters. In one or more embodiments, an IoT based framework for accurate epileptic seizure prediction system is disclosed.

Abstract: The inventive method uses mean and standard deviation of attributes and factors the relationship between attributes by using the correlation coefficients between attributes to estimate missing data in any given data set. The current invention provides the following benefits over the prior art: (1) using mean, standard deviation of attributes, and correlation coefficients between attributes to estimate the missing value of an attribute and (2) the time complexity of the proposed algorithm is better than those of the existing, prior art algorithms.

Abstract: Disclosed herein is a novel approach to Long Short-Term Memory (LSTM) that uses fewer units for processing than other LSTM systems currently available. This LSTM system has the ability to retain memory and learn data sequences using one gate. The benefit of the disclosed system is performing the learning process at a faster speed to the lower number computation units.

Abstract: The inventive method, Unsupervised Ranking using Magnetic properties and Correlation coefficient (URMC) takes attributes of the dataset as inputs and returns a weight for each of the attributes in the dataset as output. URMC clusters the attributes into similar groups and updates the weight of attributes that can be used to rank the objects. The URMC algorithm assigns each attribute of a dataset to a positive or negative cluster with weights. This is done by using the correlation coefficients between all possible pairs of attributes. Initially, all the attributes are set in positive cluster with weight 0. If the correlation coefficient between two attributes is negative, it means that they should be in different clusters. Otherwise, they should be in the same positive cluster.

Abstract: The method for creating integrated circuits (IC) protects the design of a manufactured IC from being copied or counterfeited. This method protects the design of an IC chip from deliberate copying and counterfeiting by reverse engineering to gain access to the critical points in the IC chip and to siphon its functions and design. The method makes the copying, counterfeiting, and controlling by addition of Trojan circuits during manufacturing almost impossible task. It also allows chip designers to outsource the final bonding of the tiers without any fears that their design may get compromised.

Abstract: The method for creating integrated circuits (IC) protects the design of a manufactured IC from being copied or counterfeited. This method protects the design of an IC chip from deliberate copying and counterfeiting by reverse engineering to gain access to the critical points in the IC chip and to siphon its functions and design. The method makes the copying, counterfeiting, and controlling by addition of Trojan circuits during manufacturing almost impossible task. It also allows chip designers to outsource the final bonding of the tiers without any fears that their design may get compromised.

Abstract: The method for creating integrated circuits (IC) protects the design of a manufactured IC from being copied or counterfeited. This method protects the design of an IC chip from deliberate copying and counterfeiting by reverse engineering to gain access to the critical points in the IC chip and to siphon its functions and design. The method makes the copying, counterfeiting, and controlling by addition of Trojan circuits during manufacturing almost impossible task. It also allows chip designers to outsource the final bonding of the tiers without any fears that their design may get compromised.

Abstract: The method for obfuscating hardware partially imitates the neural network perceptron, obfuscating the hardware design. This method obfuscates the design functionality and immunes integrated circuits against Trojan insertion. This method can also be used to check for the existence of faults inside chips. This method resolves the concern related to security and reliability when outsourcing the manufacture of integrated circuits.

Abstract: The method for creating integrated circuits (IC) protects the design of a manufactured IC from being copied or counterfeited. This method protects the design of an IC chip from deliberate copying and counterfeiting by reverse engineering to gain access to the critical points in the IC chip and to siphon its functions and design. The method makes the copying, counterfeiting, and controlling by addition of Trojan circuits during manufacturing almost impossible task. It also allows chip designers to outsource the final bonding of the tiers without any fears that their design may get compromised.

Abstract: Driving while feeling drowsy can cause fatal accidents. Current drowsiness detection devices are slow and inefficient. Mental drowsiness detection can predict a fatigue state early enough to engage the autopilot mechanism. Disclosed is a wearable EEG BCI adaptive VLSI architecture that can detect sleep faster.