Engineering Quantum Jump Superoperators for Single Photon Detectors

We study the back-action of a single photon detector on the electromagnetic field upon a photodetection by considering a microscopic model in which the detector is constituted of a sensor and an amplification mechanism. Using the quantum trajectories approach we determine the Quantum Jump Superoperator (QJS) that describes the action of the detector on the field state immediately after the photocount. The resulting QJS consists of two parts: the bright counts term, representing the real photoabsorptions, and the dark counts term, representing the amplification of intrinsic excitations inside the detector. First we compare our results for the counting rates to experimental data, showing a good agreement. Then we point out that by modifying the field frequency one can engineer the form of QJS, obtaining the QJS's proposed previously in an ad hoc manner

Data-efficient Neuroevolution with Kernel-Based Surrogate Models

Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the surrogate model to make fitness predictions based on variable topologies, instead of a vector of parameters. Our main insight is that we can sidestep this problem by using kernel-based surrogate models, which require only the definition of a distance measure between individuals. Our second insight is that the well-established Neuroevolution of Augmenting Topologies (NEAT) algorithm provides a computationally efficient distance measure between dissimilar networks in the form of "compatibility distance", initially designed to maintain topological diversity. Combining these two ideas, we introduce a surrogate-assisted neuroevolution algorithm that combines NEAT and a surrogate model built using a compatibility distance kernel. We demonstrate the data-efficiency of this new algorithm on the low dimensional cart-pole swing-up problem, as well as the higher dimensional half-cheetah running task. In both tasks the surrogate-assisted variant achieves the same or better results with several times fewer function evaluations as the original NEAT.Comment: In GECCO 201

Weights from a Safety Perspective for Interchange Lighting Prioritization

The focus of this paper is to research and update weights (values indicating the effect) to multiply ratings of selected factors used in the Total Design Process (TDP) for interchange lighting prioritization from a safety perspective. Results based on analysis using data collected at 80 interchanges along nine segments in North Carolina showed differences in weights for currently used factors such as freeway median width, freeway number of lanes and night-time traffic volume per lane. Results also showed that considering the number of night-time crashes by severity instead of night-to-day crash rate ratio, for prioritization of interchange lighting system installation or maintenance, would reduce the bias towards interchanges with fewer numbers of crashes and lead to better utilization of limited available transportation funds

Implementation of FPGA in the Design of Embedded Systems

The use of FPGAs (Field Programmable Gate Arrays) and configurable processors is an interesting new phenomenon in embedded development. FPGAs offer all of the features needed to implement most complex designs. Clock management is facilitated by on-chip PLL (phase-locked loop) or DLL (delay-locked loop) circuitry. Dedicated memory blocks can be configured as basic single-port RAMs, ROMs, FIFOs, or CAMs. Data processing, as embodied in the devices’ logic fabric, varies widely. The ability to link the FPGA with backplanes, high-speed buses, and memories is afforded by support for various single ended and differential I/O standards. Also found on today’s FPGAs are system-building resources such as high speed serial I/Os, arithmetic modules, embedded processors, and large amounts of memory. Here in our project we have tried to implement such powerful FPGAs in the design of possible embedded systems that can be designed, burned and deployed at the site of operation for handling of many kinds of applications. In our project we have basically dealt with two of such applications –one the prioritized traffic light controller and other a speech encrypting and decrypting system

A Routing Delay Predication Based on Packet Loss and Explicit Delay Acknowledgement for Congestion Control in MANET

In Mobile Ad hoc Networks congestion control and prevention are demanding because of network node mobility and dynamic topology. Congestion occurs primarily due to the large traffic volume in the case of data flow because the rate of inflow of data traffic is higher than the rate of data packets on the node. This alteration in sending rate results in routing delays and low throughput. The Rate control is a significant concern in streaming applications, especially in wireless networks. The TCP friendly rate control method is extensively recognized as a rate control mechanism for wired networks, which is effective in minimizing packet loss (PL) in the event of congestion. In this paper, we propose a routing delay prediction based on PL and Explicit Delay Acknowledgement (EDA) mechanism for data rate and congestion control in MANET to control data rate to minimize the loss of packets and improve the throughput. The experiment is performed over a reactive routing protocol to reduce the packet loss, jitter, and improvisation of throughput

Unleashing the Power of VGG16: Advancements in Facial Emotion Recognization

In facial emotion detection, researchers are actively exploring effective methods to identify and understand facial expressions. This study introduces a novel mechanism for emotion identification using diverse facial photos captured under varying lighting conditions. A meticulously pre-processed dataset ensures data consistency and quality. Leveraging deep learning architectures, the study utilizes feature extraction techniques to capture subtle emotive cues and build an emotion classification model using convolutional neural networks (CNNs). The proposed methodology achieves an impressive 97% accuracy on the validation set, outperforming previous methods in terms of accuracy and robustness. Challenges such as lighting variations, head posture, and occlusions are acknowledged, and multimodal approaches incorporating additional modalities like auditory or physiological data are suggested for further improvement. The outcomes of this research have wide-ranging implications for affective computing, human-computer interaction, and mental health diagnosis, advancing the field of facial emotion identification and paving the way for sophisticated technology capable of understanding and responding to human emotions across diverse domains

DEVELOPMENT AND CHARACTERIZATION OF INDOMETHACIN-LOADED MUCOADHESIVE NANOSTRUCTURED LIPID CARRIERS FOR TOPICAL OCULAR DELIVERY

Objective: To develop and characterize indomethacin loaded-nanostructured lipid carriers (IND-NLCs) for topical ophthalmic delivery with different particle sizes and polymer coating to improve the mucoadhesive property on the ocular surface.Methods: Nanostructured lipid carriers (NLCs) with different solid lipids and surfactants were prepared by the high-pressure homogenization technique. The optimized IND-NLCs was coated with polyethylene glycol 400 (PEG). The physicochemical properties and entrapment efficacy (EE) were examined. In vitro release studies were investigated using the shake-flask method. Ex vivo mucoadhesive studies were assessed by the wash-off test. In addition, the cytotoxicity was assessed by the short time exposure test.Results: IND-NLCs of ~300 and ~40 nm in diameter were successfully produced with a zeta potential of -30 mV and EE of 60–70 %. IND-NLCs prepared with Tween 80 as surfactant could be sterilized by autoclaving. The PEG coating of IND-NLCs did not affect either the particle size or EE. In vitro release showed a prolonged release for 360 min with a burst release of 50-60% occurring within 5 min. The smaller-sized IND-NLCs showed slightly faster release rates and better mucoadhesion to cornea compared to the larger IND-NLCs. PEG-coated IND-NLCs showed the highest mucoadhesion. In addition, IND-NLCs showed less cytotoxicity compared to IND alone. Conclusion: The small and PEG-coated NLCs represents a potentially useful carrier for safe delivery of indomethacin to the ocular surface with increased residence time

Prediction of Emotional Score of the multiple faces of a Photo Frame through Facial Emotion Recognition using the Deep Convolutional Neural Network

The facial movement of his or her is an important mark of understanding the emotion. The emotions are of different categories like angry, sad, neutral, happy, disgust, fear, surprise, etc. To classify the image into an appropriate class of emotion using the deep Convolutional neural network is a more scientific approach of classification. The classification is not only from the current observations but also from the past evidence, i.e. a training model is used to do this job. In this research article, we developed a model that derives the perception of a frame that consists of multiple faces by measuring each of its facial emotions. The developed model, therefore, claimed to be more efficient and robust against the variety of inputs