This paper serves as an overview for sequential learning algorithms for single hidden layer neural nets. Cite as: M. H. Arshad, M. A. Abido. An Overview of Sequential Learning Algorithms for Single Hidden Layer Networks: Current Issues & Future Trends. Abstract: In this paper, a brief survey of the commonly used sequential-learning algorithms used with single hidden layer feed-forward neural networks is presented. A glimpse at the different kinds that are available in the literature up until now, how they have developed throughout the years, and their relative execution is summarized. Most important things to take note of during the designing phase of neural networks are its complexity, computational efficiency, maximum training time, and ability to generalize the under-study problem. The comparison of different sequential learning algorithms in regard to these merits for single hidden layer neural networks is drawn.

This work proposes a workload model for modern graphics APIs, named GAMORRA, to estimate each frame's workload and possibly predict rendering times on a target hardware. Also, a suite of benchmarks is described that evaluates the performance of the target hardware and the overhead of different graphical operations to be used in the proposed model for workload estimation.

High-order quasi-reflectionless bandpass filters with improved passband flatness and good impedance matching both in-band and out-of-band are proposed in this work. The proposed design consists of conventional coupled-lines bandpass sections loaded with the presented absorptive stubs at the input and output. Analysis shows that the absorptive stub is equivalent to a 2-pole bandstop filter. Compared to the prior art, the higher-order nature of the presented absorptive stub enables a flatter passband and better out-of-band rejection. The overall filter stopband attenuation can be readily improved by increasing the number of coupled-lines sections without altering the passband responses. Furthermore, cross-coupling between the two absorptive stubs can be used to improve the out-of-band rejection by introducing two transmission zeros without affecting the absorption characteristics. The proposed design concepts are experimentally validated by the design and fabrication of a set of 2.4-GHz 1-, 2-, and 3-pole microstrip quasi-reflectionless bandpass filters. Measured frequency responses of these filters closely match those of the simulation.

No one predicted the corona virus outbreak, and naturally, nobody was prepared. The corona virus, otherwise, adoringly known as COVID-19, has put the entire world in a difficult position because of the apparent life-threatening danger it poses and the rate of spread and infection worldwide. Besides taking away so many lives, it has caused a variety of problems such as unemployment, social distancing, disruption of businesses and daily life. Circumstances are worse for those who live from hand to mouth. Within the three months of its presence, COVID-19 has forced humanity to find and implement alternative ways to sustain businesses and life. In this research paper, we have unearthed 7 lessons learned from the COVID-19 pandemic. These lessons include aspects of business, education, online presence, network communication, cyber security, healthcare, and the purpose of life. This research delves deeper into the response to the unannounced pandemic in hand. It aims to provide the right direction to address a potential pandemic in the future should it happen.

Image segmentation is an important pre-processing step towards higher level tasks such as object recognition, computer vision or image compression. Most of the existing segmentation algorithms deal with grayscale images only. But in the modern world, color images are extensively used in many situations. A new approach for color image segmentation is presented in this paper. There are many ways to deal with image segmentation problem and in these techniques; a particular class of algorithms traces their origin from region-based methods. These algorithms group homogeneous pixels, which are connected to primitive regions. They are easy to implement and are promising. Therefore, here one of the most efficient region-based segmentation algorithms is explained. The color image is quantized adaptively, using a wavelet transform. Then the region growing process is adopted. As preprocess, before actual region merging, small regions are eliminated by merging them with neighbor regions depending upon color similarity. After this, homogeneous regions are merged to get segmented output.

Contrast limited adaptive histogram equalization is applied to obtain local contrast enhancement.retinal blood vessels are segmented by minimum spanning superpixel tree detector.

A document by Maryam Khalid . Click on the document to view its contents.

Contract-for-Difference financial instruments are available to renewable electricity generators in day-ahead electricity markets to allow them to hedge against revenue risk. Traditional CfDs while designed to hedge revenue risk, introduce other new risks such as counterparty credit, margining and third-party risks. We therefore propose a novel financial instrument - an Ethereum blockchain-based dual escrow smart contract, to serve as the mediator in a CfD agreement between a renewable electricity generator and supplier. This financial instrument addresses hedging related risks that result from traditional CfD agreements in day-ahead electricity markets. In this paper, we design the logic of the financial instrument, translate this logic to smart contract codes and demonstrate its expected performance. Overall, the proposed financial instrument has the benefits of reducing hedging related risks inherent in traditional CfDs. Likewise, it enables secure, efficient, cost-effective, consistent, reliable, transparent and frictionless transactions between contracting parties in a CfD agreement.

Background: At present, the gesture recognition using sEMG signals requires vast amounts of training data or limits to a few hand movements. This paper presents a novel dynamic energy model that can decode continuous hand actions with force information, by training small amounts of sEMG data. Method: As activating the forearm muscles, the corresponding fingers are moving or tend to move (namely exerting force). The moving fingers store kinetic energy, and the fingers with moving trends store potential energy. The kinetic and potential energy of fingers is dynamically allocated due to the adaptive-coupling mechanism of five-fingers in actual motion. At this certain moment, the sum of the two energies is constant. We regarded energy mode with the same direction of acceleration of each finger, but likely different movements, as the same one, and divided hand movements into ten energy modes. Independent component analysis and machine learning methods were used to model associations between sEMG signals and energy mode, to determine the hand action, including speed and force adaptively. This theory imitates the self-adapting mechanism in the actual task; thus, ten healthy subjects were recruited, and three experiments mimicking activities of daily living were designed to evaluate the interface: (1) decoding untrained configurations, (2) decoding the amount of single-finger energy, and (3) real-time control. Results:(1) Participants completed the untrained hand movements (100 /100, p < 0.0001). (2) The test of pricking balloon with a needle tip was designed with significantly better than chance (779 /1000, p < 0.0001).(3) The test of punching a hole in the plasticine on the balloon was with over 95% success rate (97.67±5.04 %, p <0.01). Conclusion: The model can achieve continuous hand actions with force information, by training small amounts of sEMG data, which reduces trained complexity.

A multi-functional photonic circuit implemented on silicon on insulator (SOI) platform is reported. Eight phase modulators are connected to provide four Mach-Zehnder interferometer structure in parallel. Multimode interference couplers are used as the splitter/combiner of the MZI structures to provide static DC bias. This architecture generates two principle spatially separated 1st order harmonics from a single light source, which may be collected from separate ports without using any optical demultiplexing filters which permits remote heterodyning. A carrier suppression of >20 dB and spurious sideband suppression > 12 dB relative to the principle harmonics is achieved with bias voltage tuning only. In addition to the frequency conversion, the circuit can also perform as a sub-carrier generator, IQ modulator and frequency multiplier.

The high rate of change of frequency (RoCoF) issue incurred by the integration of renewable energy sources (RESs) into a modern power system significantly threatens the grid security, and thus needs to be carefully examined in the operational planning. However, severe fluctuation of regional frequency responses concerned by system operators could be concealed by the conventional assessment based on aggregated system frequency response. Moreover, the occurrence probability of a high RoCoF issue is actually a very vital factor during the system planner’s decision making. Therefore, this paper proposes a fast-algorithmic evaluation method to determine the probabilistic distribution of regional RoCoF for the operational planning of a RES penetrated power system. Firstly, an analytical sensitivity (AS) that quantifies the relationship between the regional RoCoF and the stochastic output of the RES is derived based on the generator and network information. Then a linear sensitivity-based analytical method (LSM) is established to calculate regional RoCoF and the corresponding probabilistic distribution, which takes much less computational time when comparing with the scenario-based simulation (SBS) and involves much less complicated calculation procedure when comparing with the cumulant-based method (CBM). The effectiveness and efficiency of the proposed method are verified in a modified 16-machine 5-area IEEE benchmark system by numerical SBS and analytical CBM.

Novel Coronavirus (2019-nCoV) or Human Coronavirus (HCoV) could be a new coronavirus that belongs to Betacoronaviruses type Human Coronaviruses, just like the Middle-East Respiratory Syndrome (MERS) coronavirus and Severe Acute Respiratory Syndrome (SARS) coronavirus. Wuhan, the capital city of Hubei province, China recorded the initial cases of this virus in December 2019. Until 2 May 2020, 10:00 CET, near 23,8287 deaths were reported out of 3.34 million confirmed cases recorded across the world. By the tip of January 2020, China confirmed that the COVID-19 transmitted from human to human. This study aims to check a completely unique medicament called “Echinacea purpurea” against the crystal structure of 2019-nCoV Main Protease. This study presents an ideal model for Echinacea purpurea to be tested in silico against 2019-nCoV Main Protease.

Most of the data gathering devices used for monitoring driver’s behavior require large storage, strong cellular signals, and unlimited internet. Touching mobile devices, during driving, is prohibited by many law enforcement agencies. There are situations, especially in developing countries, where people get stuck on roads with a low battery, low device-memory, and no mobile network. The drivers in such situations are not able to report against reckless drivers effectively.This paper proposes the framework of the “citizen reporting program” (CRP) aided with mobile apps to reduce reckless driving in such resource-constrained situations (RCS). A mobile app was designed, developed, and tested as a tool for this purpose. It could convert speech to text without a cellular network, capture the nearest geolocation, and send data to a server on the network or internet availability. We tested its reliability for converting speech to text and got a “word error rate” (WER) of less than 5%. We tested its functional usability and got a score of more than 71% on the system usability scale (SUS). The survey showed a favorable response of 70 plus % in reducing reckless driving via CRP in RCS if aided with mobile apps.

In this paper, we proposed a new low-light image enhancement approach to overcome the above limitations. The proposed algorithm is named as Nature Preserving Lowlight Image Enhancement (NPLIE). NPLIE estimates initial illumination and performs optimal refinement. The proposed algorithm computes the reflectance component through an element-wise division of input image by illumination. The enhanced image is obtained as a product of adjusted illumination and reflectance component. In this work, we estimate initial illuminance from structure-aware smoothening of a low-light image using guided filters of variable box sizes. We compute refined illumination by solving the proposed multi-objective

This paper presents a false data injection attack against AC state estimation in distribution system. Such attack is called net local load redistribution attacks (NRLA) which is aimed at misleading the distribution system operator to observe illusory under-voltage issues in the AC state estimation. The attack vector is constructed with only local network information.

We report a large third-order nonlinear optical response of palladium diselenide (PdSe2) films – a two-dimensional (2D) noble metal dichalcogenide material. Both open-aperture (OA) and closed-aperture (CA) Z-scan measurements are performed with a femtosecond pulsed laser at 800 nm to investigate the nonlinear absorption and nonlinear refraction, respectively. In the OA experiment, we observe optical limiting behaviour originating from large two photo absorption (TPA) in the PdSe2 film of β = 3.26 × 10-8 m/W. In the CA experiment, we measure a peak-valley response corresponding to a large and negative Kerr nonlinearity of n2 = -1.33×10-15 m2/W – two orders of magnitude larger than bulk silicon. We also characterize the variation of n2 as a function of laser intensity, observing that n2 decreases in magnitude with incident laser intensity, becoming saturated at n2 = -9.96×10-16 m2/W at high intensities. These results verify the large third-order nonlinear optical response of 2D PdSe2 as well as its strong potential for high performance nonlinear photonic devices.

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.0px Helvetica} The deviation of continuous and discrete complex random variables from the traditional proper and symmetric assumption to a generalized improper and asymmetric characterization (accounting correlation between a random entity and its complex conjugate), respectively, introduces new design freedom and various potential merits. As such, the theory of impropriety has vast applications in medicine, geology, acoustics, optics, image and pattern recognition, computer vision, and other numerous research fields with our main focus on the communication systems. The journey begins from the design of improper Gaussian signaling in the interference-limited communications and leads to a more elaborate and practically feasible asymmetric discrete modulation design. Such asymmetric shaping bridges the gap between theoretically and practically achievable limits with sophisticated transceiver and detection schemes in both coded/uncoded wireless/optical communication systems. Interestingly, introducing asymmetry and adjusting the transmission parameters according to some design criterion render optimal performance without affecting the bandwidth or power requirements of the systems. This dual-flavored article initially presents the tutorial base content covering the interplay of reality/complexity, propriety/impropriety and circularity/noncircularity and then surveys majority of the contributions in this enormous journey.

COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. At the time of writing, more than 2.8 million people have tested positive. Infections have been growing exponentially and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise ongoing data science activities in this area. As well as reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies. As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works.