Abstract: Target tracking in a video is a highly challenging problem as the target may be effected by its appearance changes along the video, partial occlusions, background clutter, illumination variations, surrounding environment and also due to changes in the motion of the target. Embodiments of the present disclosure address this problem by implementing neural network for convolution feature maps and their gradient maps generation. The proposed two-class neural network (TCNN) is guided by feeding it target of interest defined by a bounding box in a first frame of the video. With this target guidance TCNN generates target activation map by using convolutional features and gradient maps. Target activation map gives tentative location of target, and this is further exploited to locate target precisely by using correlation filter(s) and peak location estimator based on identified context. This process repeats for every frame of the video to track the target accurately.

Abstract: An integrated front-of-house and back-of-house restaurant operations system integrates automated and manual restaurant operations into an order-based system. The system interfaces with disparate devices and systems to provide order-based monitoring and control of operations within an establishment.

Abstract: A beverage operations control system of an establishment such as a bar communicates with numerous wireless beverage control devices that monitor and control the pouring of beverages. The system provides monitoring and predictive analysis based on the monitoring, including real-time modifications to the operations of the beverage control devices.

Abstract: An access category assigned to stations making probe requests, based on a station type determined. Responsive to a voice access category type determination, deep packet inspection on one or more network packets from a specific flow of the specific station to identify a specific voice application running on the specific station can be performed. A GSSID is assigned to the specific station based on the specific voice application identified, each GSSID from a plurality of GSSIDs having distinct QoS parameters for voice applications. The probe request is responded to with a probe response, wherein the probe response comprises the GSSID. Network packets can be transmitted for and network packets by the specific station comprising the GSSID utilizing the voice access category.

Abstract: A plurality of network packets is received at an input network interface of the network device coupled to the data communication network. The network packets are stored in a plurality of network packets in a hardware queue of a plurality of hardware queues dedicated to a CPU of a plurality of CPUs in accordance with a flow assigned to the network packet. Responsive to successfully identifying a stored hash matching the hash result in the hash table, an associated flow entry is fetched, and the network packet is forwarded to a fast processing path. Responsive to not successfully identifying a stored hash matching the hash result in the hash table, the hash is stored in the hash table, and the network packet is forwarded to the slow processing path. The network packet at an output network interface of the network device according to the flow entry.

Abstract: A WLAN driver of the TCP proxy device transmits network packets transmitted from a sender device over the data communication network to a wireless station. TCP network packets are diverted to a TCP proxy pipeline. First, the wireless receiver device is emulated to a wireless sender device by providing an ACK packet to the sender device in order to close the TCP session on the sender side by responding to a TCP handshake with the sender device. Second, the sender device is emulated to a wireless station over the wireless network connection by initiating a TCP handshake with a wireless station including receiving an ACK packet from the wireless station and suppressing the ACK packet from an initial destination associated with the sender device.

Abstract: A radar sensing system for a vehicle has multiple transmitters and receivers on a vehicle. The transmitters are configured to transmit radio signals which are reflected off of objects in the environment. There are one or more receivers that receive the reflected radio signals. Each receiver has an antenna, a radio frequency front end, an analog-to-digital converter (ADC), and a digital signal processor. The transmitted signals are based on spreading codes generated by a programmable code generation unit. The receiver also makes use of the spreading codes generated by the programmable code generation unit. The programmable code generation unit is configured to selectively generate particular spreading codes that have desired properties.

Abstract: The described embodiments employ aspects of distributed ledger technologies to facilitate electronic verification and sharing of profile information. Nodes maintaining a distributed ledger include—a first node that generates profile data, and second nodes that generate certificates verifying the generated profile data. The first node can be employed by a client device to generate profile data associated with a first identifier and referencing a second identifier for inclusion on a personal profile, such as a social media webpage. The client device can send the first node a request to have the profile data verified by the referenced second identifier. The profile data can be stored on a distributed ledger so that a second node associated with the second identifier can generate, on behalf of the second identifier, a certificate that verifies the stored profile data.

Abstract: Systems and methods for defection classification in a semiconductor process are provided. The system includes a communication line configured to receive a defect image of a wafer from the semiconductor process and a deep-architecture neural network in electronic communication with the communication line. The neural network has a first convolution layer of neurons configured to convolve pixels from the defect image with a filter to generate a first feature map. The neural network also includes a first subsampling layer configured to reduce the size and variation of the first feature map. A classifier is provided for determining a defect classification based on the feature map. The system may include more than one convolution layers and/or subsampling layers. A method includes extracting one or more features from a defect image using a deep-architecture neural network, for example a convolutional neural network.

Abstract: A system and computer-implemented method for managing a smart devices network using fog computing is provided. The system comprises an application manager configured to receive service requests from devices in a smart devices network and collect data related to fog computing nodes and intermediate computing nodes and a resource utilization predictor configured to predict availability of the fog computing nodes and the intermediate computing nodes. Furthermore, the system comprises a resource manager configured to dynamically allocate at least one of: a specific fog computing node and a specific intermediate computing node, schedule triggering of fog applications based on the predicted availability, trigger, at the specific fog computing node and the specific intermediate computing node, the fog applications for executing the received service requests corresponding to the devices and perform actions corresponding to the executed one or more service requests.

Abstract: Memory cell circuitry is disclosed. The memory cell circuitry includes a first transistor configured to have a threshold voltage of the first transistor modulated by hot carrier injection, a second transistor coupled to the first transistor and configured to have a threshold voltage of the second transistor modulated by hot carrier injection, a word line coupled to a gate of the first transistor and to a gate of the second transistor, a first bit line coupled to the first transistor and a second bit line coupled to the second transistor. In addition, the memory cell circuitry includes a source line coupled to the drain of the first transistor and to the drain of the second transistor, the word line and the source line configured to cause hot carrier injection (HCI) into the first transistor when a first supply voltage is applied to the word line and the source line, and the second bit line is floated and the first bit line is grounded.

Abstract: Biometric templates (template) are used to store biometric data of one or more users. As biometric data of each user is unique and non-replaceable, template security is a major concern. This disclosure relates generally to template protection, and more particularly to a method and system for biometric template protection. The system generates a final perturbation value for data in the template, based on a first binary code (BC), a second binary code (BC?), feature vectors corresponding to data in the template, and an initial random perturbation. The final perturbation and the feature vectors are mapped to the first binary code. The first binary code represents the template. Every time the first binary code is compromised, the value of first binary code is updated, and the whole process is repeated to generate corresponding final perturbation and then the mapping is done.

Abstract: A method to collect data and train, validate and deploy statistical models to predict overlay errors using patterned wafer geometry data and other relevant information includes selecting a training wafer set, measuring at multiple lithography steps and calculating geometry differences, applying a plurality of predictive models to the training wafer geometry differences and comparing predicted overlay to the measured overlay on the training wafer set. The most accurate predictive model is identified and the results fed-forward to the lithography scanner tool which can correct for these effects and reduce overlay errors during the wafer scan-and-expose processes.

Abstract: Visual target tracking is task of locating a target in consecutive frame of a video. Conventional systems observe target behavior frames of the video. However, dealing with this problem is very challenging when video has illumination variations, occlusion, change in size and view of the object due to relative motion between camera and object. Embodiments of the present disclosure addresses this problem by implementing Neural Network (NN), its features and their corresponding gradients. Present disclosure explicitly guides the NN by feeding target object of interest (ToI) defined by a bounding box in the first frame of the video. With this guidance, NN generates target activation map via convolutional features map and their gradient maps, thus giving tentative location of the ToI to further exploit to locate target object precisely by using correlation filter(s) and peak location estimator, thus repeating process for every frame of video to track ToI accurately.

Abstract: An access category assigned to stations making probe requests, based on a station type determined. Responsive to a voice access category type determination, deep packet inspection on one or more network packets from a specific flow of the specific station to identify a specific voice application running on the specific station can be performed. A GSSID is assigned to the specific station based on the specific voice application identified, each GSSID from a plurality of GSSIDs having distinct QoS parameters for voice applications. The probe request is responded to with a probe response, wherein the probe response comprises the GSSID.

Abstract: Capturing messages exchanged with field devices in an industrial process without disrupting communication of the messages. A diagnostic driver embedded within a gateway device detects abnormal conditions in connections between the gateway device and the field devices. The diagnostic driver captures messages indicative of the abnormal condition without disrupting message communication. The driver is capable of transmitting captured messages, on demand, to a workstation computing device for diagnosis of the abnormal condition.

Abstract: System and method for text localization in images are disclosed. In an embodiment, a line and graphic eliminated image is received. Further, horizontal projection is performed on rows of the image to obtain a first flag vector, the flag vector indicates whether there is text in each row. Furthermore, a number of run-lengths of consecutive 1's and 0's is computed in the first flag vector. Moreover, text lines is extracted in the image based on the computed number of run-lengths of consecutive 1's and 0's in the first flag vector. Also, vertical projection is performed on the text lines to obtain a second flag vector for the text lines. Further, a number of run-lengths of consecutive 1's and 0's is computed in the second flag vectors. Furthermore, text is localized in the image based on the computed number of run-lengths of consecutive 1's and 0's in the second flag vectors.

Abstract: A system and computer-implemented method for managing a smart devices network using fog computing is provided. The system comprises an application manager configured to receive service requests from devices in a smart devices network and collect data related to fog computing nodes and intermediate computing nodes and a resource utilization predictor configured to predict availability of the fog computing nodes and the intermediate computing nodes. Furthermore, the system comprises a resource manager configured to dynamically allocate at least one of: a specific fog computing node and a specific intermediate computing node, schedule triggering of fog applications based on the predicted availability, trigger, at the specific fog computing node and the specific intermediate computing node, the fog applications for executing the received service requests corresponding to the devices and perform actions corresponding to the executed one or more service requests.

Abstract: A high-dimensional variable selection unit determines a list of critical parameters from sensor data and parametric tool measurements from a semiconductor manufacturing tool, such as a semiconductor inspection tool or other types of semiconductor manufacturing tools. The high-dimensional variable selection model can be, for example, elastic net, forward-stagewise regression, or least angle regression. The list of critical parameters may be used to design a next generation semiconductor manufacturing tool, to bring the semiconductor manufacturing tool back to a normal status, to match a semiconductor manufacturing tool's results with that of another semiconductor manufacturing tool, or to develop a specification for the semiconductor manufacturing tool.

Abstract: The present disclosure relates to a method and a system for improving recovery of fluids comprising natural gas and water from a reservoir via at least one oil/gas well connected to at least one flowline/pipeline network. For improving recovery of fluids, a composition containing a specific foaming composition is injected at a pre-determined location at the oil/gas well and/or the flowline/pipeline network. The foaming composition is delivered to the predetermined injection location via umbilical. The foaming composition is then naturally agitated with the water and converted to foam. The agitation is carried out by the natural gas. The foam formed reduces the interfacial surface tension between the natural gas and the water, and thereby facilitates reduction in liquid holdup in the flowline/pipeline network.