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.

Road accidents caused by human error are among the main causes of the death in the world. Specifically, drowsiness and unconsciousness while driving are responsible for many fatal accidents on highways. Accuracy and performance are key metrics related to many researched techniques for the detection of drivers’ drowsiness. To improve these metrics, in this paper, a new method based on image processing and deep learning is proposed. The proposed method is based on facial region diagnosing using the Haar-cascade method and convolutional neural network for drowsiness probability detection. Evaluation analysis of the proposed method on the UTA-RLDD dataset with stratified 5-fold cross-validation showed a high accuracy of 96.8% at a speed of 10 frames per second, which is higher than those that have previously been reported in the literature. For further investigation, a custom dataset including 10 participants in different light conditions was collected. The result of all experiments showed the great potential of the proposed method for practical applications in intelligent transportation systems

This paper presents the design and bit error rate (BER) analysis of a phase-independent non-orthogonal multiple access (NOMA) system. The proposed NOMA system can utilize amplitude-coherent detection (ACD) which requires only the channel amplitude for equalization purposes. In what follows, three different designs for realizing the detection of the proposed NOMA are investigated. One is based on the maximum likelihood (ML) principle, while the other two are based on successive interference cancellation (SIC). Closed-form expressions for the BER of all detectors are derived and compared with the BER of the coherent ML detector. The obtained results, which are corroborated by simulations, demonstrate that, in most scenarios, the BER is dominated by multiuser interference rather than the absence of the channel phase information. Consequently, the BER using ML and ACD are comparable for various cases of interest. The paper also shows that the SIC detector is just an alternative approach to realize the ML detector, and hence, both detectors provide the same BER performance.

The linear system with missing information is investigated in this paper. New methods are introduced to improve the Mean Squared Error (MSE) on the test set in comparison to state-of-the-art method s, through appropriate tuning of Bias-Variance trade-off. The concept is to cluster the data and adapt the learning model to each cluster. Hence, we set forth a controlled bias into the problem and positively utilize it to enhance learning capability on the instances considered in some specific neighborhood. To deal with missing infrormation, we propose a novel algorithm “Missing-SCOP” based on SCOP-KMEANS algorithm introduced by Wagstaff, et al., utilizing the missing pattern of the dataset for construction of a soft-constraint matrix and clustering in missing scenario. It is shown that controlled over-fitting suggested by our algorithm improves prediction accuracy in various cases. Numerical experiments approve the efficacy of our proposed algorithm in enhancing the prediction accuracy.

In this paper we present a vision based hardware-software control system enabling autonomous landing of a mul-tirotor unmanned aerial vehicle (UAV). It allows the detection of a marked landing pad in real-time for a 1280 x 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption.

Recently, the low-rank and sparse decomposition problem has attracted attention in several applications, especially surveillance videos. Due to the physical limitations in acquisition systems, measured frames are blurred by a low-pass filter. In this article, we aim to decompose blurred videos' frames into low-rank and sparse components, in order to extract the background. Unlike conventional methods, we simultaneously take into account the blurring effect, as well as the missing data. Our simulation results confirmed the advantage of this approach in extracting low-rank components in surveillance videos.

This work focuses on the usability of Ultraviolet (UV) on humans as an economical way of contesting against COVID-19. Specifically, far-UVC is presented as a promising candidate against COVID-19 since it can inactivate pathogens including viruses and bacteria without harming mammalian skin or eyes. Furthermore, the work also points out the applicability of far-UVC lights for public spaces and treatment of selective region in COVID-19 patients by using fiberoptic as the medium for high-risk cases where chances of survival are low and such treatment may avoid fatality due to the COVID-19.

In semiconductor manufacturing, metrology is generally a high cost, non-value added operation that impacts significantly on cycle time. As such, reducing wafer metrology continues to be a major target in semiconductor manufacturing efficiency initiatives. A novel data-driven spatial dynamic sampling methodology is presented that minimises the number of sites that need to be measured across a wafer surface while maintaining an acceptable level of wafer profile reconstruction accuracy. The methodology is based on analysing historical metrology data using Forward Selection Component Analysis (FSCA) to determine, from a set of candidate wafer sites, the minimum set of sites that need to be monitored in order to reconstruct the full wafer profile using statistical regression techniques. Dynamic sampling is then implemented by clustering unmeasured sites in accordance with their similarity to the FSCA selected sites, and temporally selecting a different sample from each cluster. In this way, the risk of not detecting previously unseen process behaviour is mitigated. We demonstrate the efficacy of the proposed methodology using both simulation studies and metrology data from a semiconductor manufacturing process.

Low-rank matrix factorization problems such as non negative matrix factorization (NMF) can be categorized as a clustering or dimension reduction technique. The latter denotes techniques designed to find representations of some high dimensional dataset in a lower dimensional manifold without a significant loss of information. If such a representation exists, the features ought to contain the most relevant features of the dataset. Many linear dimensionality reduction techniques can be formulated as a matrix factorization. In this paper, we combine the conjugate gradient (CG) method with the Barzilai and Borwein (BB) gradient method, and propose a BB scaling CG method for NMF problems. The new method does not require to compute and store matrices associated with Hessian of the objective functions. Moreover, adopting a suitable BB step size along with a proper nonmonotone strategy which comes by the size convex parameter $\eta_k$, results in a new algorithm that can significantly improve the CPU time, efficiency, the number of function evaluation. Convergence result is established and numerical comparisons of methods on both synthetic and real-world datasets show that the proposed method is efficient in comparison with existing methods and demonstrate the superiority of our algorithms.

In order to fulfill transportation demands, people have well-explored ground, waterborne, and high-altitude spaces (HAS) for transportation purposes, as well as the underground space under cities (namely, subway systems). However, due to the increased burdens of population and urbanization in recent decades, huge pressures on public transportation and freight traffic are introduced to cities, plaguing the governors and constraining the development of economics. By observing the fact that near-ground space (NGS) has rarely been utilized, researchers and practitioners started to re-examine, propose and develop flying cars, which are not a totally novel idea, aiming at solving the traffic congestion problem and releasing the strains of cities. Flying cars completely differ from traditional grounded transportation systems, where automobiles/trains are suffering track limitations and are also different from the air flights in HAS for long-distance transfer. Therefore, while observing the lack of specific literature on flying cars and flying car transportation systems (FCTS), this paper is motivated to study the advances, techniques, and challenges of FCTS imposed by the inherent nature of NGS transportation and to devise useful proposals for facilitating the construction and commercialization of FCTS, as well as to facilitate the readers understanding of the incoming FCTS. We first introduce the increased requirements for transportation and address the advantages of flying cars. Next, a brief overview of the developing history of flying cars is presented in view of both timeline and technique categories. Then, we discuss and compare the state of the art in the design of flying cars, including take-off \& landing (TOL) modes, pilot modes, operation modes, and power types, which are respectively related to the adaptability, flexibility & comfort, stability & complexity, environmental friendliness of flying cars. Additionally, since large-scale operations of flying cars can improve the aforementioned transportation problem, we also introduce the designs of FCTS, including path and trajectory planning, supporting facilities and commercial designs. Finally, we discuss the challenges which might be faced while developing and commercializing FCTS from three aspects: safety issues, commercial issues, and ethical issues.

Despite numerous advantages, mobile phones cause serious health issues to people due to electromagnetic radiation. Various head models already exist to study the impact of radiation on a human head. The accuracy of the measurement of power absorbed by different layers of a head should be high. A new head model with six layers is proposed in this paper. Parameters such as dielectric constant, conductivity and mass density of different tissue layers skin, fat, bone, Dura, cerebrospinal fluid (CSF), and brain are extracted from the Federal Communications Commission (FCC) database. To study the impact of radiation in the proposed model, standard planar inverted F-antennas (PIFA) capable to radiate at 1.7 GHz and 2.4 GHz are used. Simulations are performed using ANSYS Electromagnetics Suite. The analysis shows that the specific absorption rate (SAR) in the brain layer decreased in the proposed model when compared to the existing model.

Our goal is to propose a suitable approach for validating blockchains. For this purpose, we intend to adopt formal methods which are based on strong mathematical foundations. More precisely, we follow a model-based testing approach. The latter consists in describing the behavior of the system using a specific formalism, deriving test cases from the obtained model and then executing the obtained tests on the implementation to check whether it is correct or not. The adopted formalism corresponds to the timed automaton Model. The generated tests may be either digital or analog. Moreover, we propose several techniques which allow to solve the state explosion which may be encountered during the verification and test generation phases.

Cyclic redundancy check (CRC) is a well-known error detection code that is widely used in Ethernet, PCIe, and other transmission protocols. The existing FPGA-based implementation solutions are faced with the problem of excessive resource utilization in high-performance scenarios. The padding zeros problem and the introduction of programmability further exacerbate this problem. In this brief, the stride-by-5 algorithm is proposed to achieve the optimal utilization of FPGA resources. The pipelining go back algorithm is proposed to solve the padding zeros problem. The method of reprogramming by HWICAP is proposed to realize programmability with a small and constant resource utilization. The experimental results show that the resource utilization of proposed non-segmented architecture is 84.1% and 37.6% lower than those of two state-of-the-art FPGA-based CRC implementations, and the proposed segmented architecture has a lower resource utilization by 83.9% and 8.9% compared wtih the two state-of-the-art architectures; meanwhile, the throughput and programmability are guaranteed. We made the source code available on GitHub.

Next-generation communication networks are expected to integrate newly-used technologies in a smart way to ensure continuous connectivity in rural areas and to alleviate the traffic load in dense regions. The prospective access network in 6G should hinge on satellite systems to take advantage of their wide coverage and high capacity. However, adopting satellites in 6G could be hindered because of the {additional latency introduced}, which is not tolerable by all traffic types. Therefore, we propose a traffic offloading scheme that integrates both the satellite and terrestrial networks to smartly allocate the traffic between them while satisfying different traffic requirements. Specifically, the proposed scheme offloads the Ultra-Reliable Low Latency Communication (URLLC) traffic to the terrestrial backhaul to satisfy its stringent latency requirement. However, it offloads the enhanced Mobile Broadband (eMBB) traffic to the satellite since eMBB needs high data rates but is not always sensitive to delay. Our scheme is shown to reduce the transmission delay of URLLC packets, decrease the number of dropped eMBB packets, and hence improve the network’s availability. Our findings highlight that the inter-working between satellite and terrestrial networks is crucial to mitigate the expected high load on the limited terrestrial capacity.