Boids (bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog (a herding agent). Previous work assumed homogeneous shoids. Real-world observations of sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoids with different parameters within the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from live sheep herding trials by an unmanned aerial vehicle (UAV) to validate the proposed measures. The resultant measures will allow us in our future research to design more efficient control strategies for the UAV, by polarising the attention of the machine learning algorithm on those shoids with influence footprints, to drive the flock to improve the herding of sheep.

This article proposes a novel impedance standard substrate, as known as calibration kit, 17 for the calibration using radio frequency (RF) and microwave probes, especially the through 18 substrate which is able to calibrate not only the conventional horizontal but also the vertical 19 interconnects on the basis of the short-open-load-through (SOLT) standardized calibration 20 approach. In the measurement of a vertical connection where the probing ports are located on the 21 opposite surfaces of devices, a location change or rotate with the probes is required after the 22 calibration based on the conventional coplanar kit which only allows the probing in the same 23 plane. The location change or rotation of the probes and corresponding connected cables 24 inevitably affects the precision of the calibration since the probes and cables are sensitive to the 25 surroundings. Our proposed calibration kit can overcome the aforementioned shortcomings, and 26 the comparison of calibrated results between conventional and proposed through impedance 27 standard substrate is demonstrated. Validation of the proposed kit based on the coplanar meander 28 lines and vertical couplings by via arrays is also discussed.

Using instantaneous aggregate arrival rate as an admission control parameter will contribute to either bandwidth under-utilization or over-utilization. Being bursty in nature and variable in rate, video flows might encode any rate between a range of minimum and maximum values. At the time the decision is made, if the measured rate is at the minimum value, the bandwidth might be over-utilized due to accepting more sessions than the link can accommodate. In contrast, it might be under-utilized if the measured rate is at the maximum value due to rejecting more sessions than the link can accommodate. The burstiness can be taken into account by considering the past history of the traffic. This paper investigates the suitability of the average aggregate arrival rate instead of the instantaneous aggregate arrival rate for video admission decisions. It establishes a mathematical model to predict the relationship between the two rates. Simulation results confirm that the average aggregate arrival rate is a more efficient decision factor for a small number of flows. Although it has no additional advantage for moderate and large number of flows, it still can stabilize the admission decision by smoothing the burstiness of a set of the instantaneous rates (within the measurement period) over a period of time.

We characterize the third-order optical nonlinearity in PdSe2 dichalcogenide films via the Z-Scan technique. A strong and negative (self-defocusing) Kerr nonlinearity (n2) of ∼ ‒ 7.65×10-16 m2/W is observed at 800 nm.

The emergence of video applications and video capable devices have contributed substantially to the increase of video traffic on Internet. New mechanisms recommending video rate adaptation towards delivering enhanced Quality of Experience (QoE) at the same time making room for more sessions. This paper introduces a cross-layer QoE-aware architecture for video traffic over the Internet. It proposes that video sources at the application layer adapt their rate to the network environment by controlling their transmitted bit rate dynamically; and the edge of network at the network layer protects the quality of the active video sessions by controlling the acceptance of new session through a video-aware admission control. In particular, it will seek the most efficient way of accepting new video session and adapting transmission rates to free up resources for more session while maintaining the QoE of active sessions. The proposed framework will contribute to the preparation for the extreme growth of video traffic in the foreseeable future. Simulation results show that the proposed cross-layer architecture guarantees the QoE for the admitted sessions and utilizes the link more efficiently comparing to the rate adaptation only architecture.

With an increasing amount of elderly people needing home care around the clock, care workers are not able to keep up with the demand of providing maximum support to those who require it. As medical costs of home care increase the quality is care suffering as a result of staff shortages, a solution is desperately needed to make the valuable care time of these workers more efficient. This paper proposes a system that is able to make use of the deep learning resources currently available to produce a base system that could provide a solution to many of the problems that care homes and staff face today. Transfer learning was conducted on a deep convolutional neural network to recognize common household objects was proposed. This system showed promising results with an accuracy, sensitivity and specificity of 90.6%, 0.90977 and 0.99668 respectively. Real-time applications were also considered, with the system achieving a maximum speed of 19.6 FPS on an MSI GTX 1060 GPU with 4GB of VRAM allocated.

This paper presents detailed Markov models for the reliability assessment of multilevel neutral-point-clamped (NPC) converter leg topologies, incorporating their inherent fault-tolerance under open-circuit switch faults. The Markov models are generated and discussed in detail for the three-level and four-level active NPC (ANPC) cases, while the presented methodology can be applied to easily generate the models for higher number of levels and for other topology variants. In addition, this paper also proposes an extremely fast calculation method to obtain the precise value of the system mean time to failure from any given formulated system Markov model. This method is then applied to quantitatively compare the reliability of two-level, three-level, and four-level ANPC legs under switch open-circuit-guaranteed faults and varying degrees of device paralleling. The comparison reveals that multilevel ANPC leg topologies inherently present a potential for a higher reliability than the conventional two-level leg, questioning the suitability of the traditional search for topologies with the minimum number of devices in order to improve reliability. Experimental results are presented to validate the fault-tolerance assumptions upon which the presented reliability models for the three-level and four-level ANPC legs are based.

The test data can be found in Reference [22] in this manuscript.

Conventional techniques for noninvasive measurement of brain function such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS) have critical limitations of spatial or temporal resolution. Here, we developed a novel technique which enables the precise measurement of dynamic brain signals and localized identification of active brain regions. In this technique, termed as magnetically biased field (MBF), human brain signal is measured as the fluctuation of a transcranial static magnetic field emitted by a coil placed on the scalp. The validity of MBF was confirmed by the measurement of somatosensory evoked signals. Fast somatosensory evoked signals were successfully observed. Localized maximum positive and negative deflections appeared at the region which represents the right primary somatosensory area contralateral to the stimulated hand. The ability of MBF to detect dynamic brain activity precisely can have numerous applications such as diagnosing brain diseases and brain-machine interfaces.

The rapid shift toward video on-demand and real time information systems has affected mobile as well as wired networks. The research community has placed a strong focus on optimizing the Quality of Experience (QoE) of video traffic, mainly because video is popular among Internet users. Techniques have been proposed in different directions towards improvement of the perception of video users. This paper investigates the performance of a novel cross-layer architecture for optimizing the QoE of video traffic. The proposed architecture is compared to two other architectures; non-adaptive and adaptive. For the former, video traffic is sent without adaptation, whereas for the later video sources adapt their transmission rate. Both are compared in terms of the mean opinion score of video sessions, number of sessions, delay, packet drop ratio, jitter and utilization. The results from extensive simulations show that the proposed architecture outperforms the non-adaptive and adaptive architectures for video traffic.

Abstract—Inspired by the depth and breadth of developments on the theory of deep learning, we pose these fundamental questions: can we accurately approximate an arbitrary matrix-vector product using deep rectified linear unit (ReLU) feedforward neural networks (FNNs)? If so, can we bound the resulting approximation error? Attempting to answer these questions, we derive error bounds in Lebesgue and Sobolev norms for a matrix-vector product approximation with deep ReLU FNNs. Since a matrix-vector product models several problems in wireless communications and signal processing; network science and graph signal processing; and network neuroscience and brain physics, we discuss various applications that are motivated by an accurate matrix-vector product approximation with deep ReLU FNNs. Toward this end, the derived error bounds offer a theoretical insight and guarantee in the development of algorithms based on deep ReLU FNNs.

In this paper we study the problem of characterizing and computing the nonanticipative rate distortion function (NRDF) for partially observable multivariate Gauss-Markov processes with hard mean squared error (MSE) distortion constraints. For the finite time horizon case, we first derive the complete characterization of this problem and its corresponding optimal realization which is shown to be a linear functional of the current time sufficient statistic of the past and current observations signals. We show that when the problem is strictly feasible, it can be computed via semidefinite programming (SDP) algorithm. For time-varying scalar processes with average total MSE distortion we derive an optimal closed form expression by means of a dynamic reverse-waterfilling solution that we also implement via an iterative scheme that convergences linearly in finite time, and a closed-form solution under pointwise MSE distortion constraint. For the infinite time horizon, we give necessary and sufficient conditions to sure that asymptotically the sufficient statistic process of the observation signals achieves a steady-state solution for the corresponding covariance matrices and impose conditions that allow existence of a time-invariant solution. Then, we show that when a finite solution exists in the asymptotic limit, it can be computed via SDP algorithm. We also give strong structural properties on the characterization of the problem in the asymptotic limit that allow for an optimal solution via a reverse-waterfilling algorithm that we implement via an iterative scheme that converges linearly under a finite number of spatial components. Subsequently, we compare the computational time needed to execute for both SDP and reverse-waterfilling algorithms when these solve the same problem to show that the latter is a scalable optimization technique. Our results are corroborated with various simulation studies and are also compared with existing results in the literature.

We investigate integrated photonic resonators formed by coupled Sagnac loop reflectors (CSLR). Mode splitting in the CSLR resonator is tailored to provide flat-top spectral response for wavelength interleaving and non-blocking switching functions in C band.

This paper is all about the different constructs of RDF data processing. This paper covers the storage, indexing, language, query planning.

We demonstrate broadband RF channelizers based on microcombs with 49 GHz and 200 GHz FSRs. Up to 92 parallel channels and an instantaneous bandwidth of up to 8.08 GHz are achieved, together with high-resolution RF spectral channelization and shaping. This approach is promising for high-performance integrated photonic RF receivers and processors.

Enhanced four-wave mixing in silicon nitride waveguides integrated with 2D graphene oxide (GO) films is experimentally demonstrated. We achieve a high conversion efficiency improvement of ~7.3 dB for a 2-cm-long waveguide with monolayer GO film.

COVID-19 has ended up being the greatest pandemic to come to pass for on humanity in the last century. It has influenced all parts of present day life. The best way to confine its spread is the early and exact finding of infected patients. Clinical imaging strategies like Chest X-ray imaging helps specialists to assess the degree of spread of infection. In any case, the way that COVID-19 side effects imitate those of conventional Pneumonia brings few issues utilizing of Chest Xrays for its prediction accurately. In this investigation, we attempt to assemble 4 ways to deal with characterize between COVID-19 Pneumonia, NON-COVID-19 Pneumonia, and an Healthy- Normal Chest X-Ray images. Considering the low accessibility of genuine named Chest X-Ray images, we incorporated combinations of pre-trained models and data augmentation methods to improve the quality of predictions. Our best model has achieved an accuracy of 99.5216%. More importantly, the hybrid did not predict a False Negative Normal (i.e. infected case predicted as normal) making it the most attractive feature of the study.

In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional neural network (CNN) for unsupervised optical flow estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and classification; and 3) an illegally parked car (IPC) candidate investigation module developed based on visual SLAM. The proposed framework was successfully embedded in a drone from ATG Robotics. The experimental results demonstrate that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art unsupervised optical flow estimation approaches in terms of both speed and accuracy; secondly, IPC candidates can be effectively and efficiently detected by our proposed Flow-RCNN, with a better performance than our baseline network, Faster-RCNN; finally, the actual IPCs can be successfully verified by our investigation module after drone re-localization.