This is a survey for the comparison between existing model which is used in classical database and quantum computing. Theoretically it is possible to converge both of this quantum database in classical database as we will always have Hamiltonian of the signal which are written in binary.Quantum computing holds immense amount of capabilities to solve any classical problems those are unresolved.This report mostly identifies the gap between these classical database and quantum database but it shows no result rather it proposes only the hypothesis of existing mechanism / architecture with new perspective.

Stock Expectation plays a crucial part in fund and  financial matters. Numerous individuals contribute billions of  monetary standards on the stocks anticipating the benefit after  every single stock is obtained. Depending on the behavior of  advertising there are ups and downs within the benefit, so the  issue of quick and exact estimating of share cost becomes vital.  Such a prediction may be enhanced by using Machine and Deep  Learning, and this dissertation proposes a novel strategy for  predicting stock cost, oil, and gold forecast based on profound  learning and outfit learning strategies. Therefore, this  dissertation assesses and describes the following two types of  frequently used models: Economic Factors in Standard Time  Series based on various approaches and features. Previous  studies attempted to forecast future gold and prices using  previous data and Structural Models which attempt to explain  the links between oil and gold prices about a range of economic  considerations using machine learning models: SVM, Random  Forest, Linear Regression, Random Forest Regression, and  LSTM. These predictions are based on the sliding window  technique and try to implement such a system and apply it for  forecasting using oil and gold time series. By using Machine and  Deep Learning methods, more accurate forecasting in stock  prices may be obtained and this dissertation sought to answer the  following questions about this general premise: What is the effect  of the major oil and company financial results on future price?  What is the effect between oil and price and how that will affect  the gold future price? What is the coloration between US prices  and the stock market? How will natural disasters, disease, and  wars affect oil, and gold prices? The accompanying  macroeconomic variables were utilized which are S&P 500 List,  New York Stock Trade List (NYSE), and US Dollar List and  Consumer Feeling Record (CSI). Findings that NYSE is affected  a positive strong correlation on oil price while oil price affects  positive strong correlation on the gold price. Also, USD affects a  strong negative correlation with the stock market (oil and gold).  Finally, by analyzing the increase and decrease in oil prices from  OPEC, an indirect relationship was discovered.

The primary challenge with big smart meter data is to gain actionable insights which ultimately enhance the sustainability of the power network. In this paper, we analyze 1 year of smart meter data from 326 households in Austin, TX. Via clustering, we find distinct daily usage patterns, illustrating heterogeneous consumption behavior which changes throughout the year. However, most consumers have at least 56% of days explained by 3 out of 24 identified load types. For each load type we estimate the value of PV and batteries under flat rate and Time-Of-Use electricity tariffs. We find that knowledge of a consumers most common load shapes can significantly improve estimates for PV viability compared to a control estimate based on population-wide averages. However, knowledge of the most common load shapes does not improve estimates of battery viability unless electricity prices are time-varying. This highlights that, in general, the information contained by load shape clusters is of high value when consumers face economic choices that depend on the timing of their consumption. This work builds on current knowledge by explicitly linking smart meter segmentation techniques to individual consumer suitability for different distributed energy technologies.

In this paper, we propose a novel approximate noise-whitening for multi-input multi-output (MIMO) detection with banded-inverse extension (BIE). If a noise covariance matrix origins from a Gauss-Markov process (GMP), its inverse is banded and the proposal is exact and yields no accuracy-losses. For general matrices to be inverted, the approximation-errors from inversion introduce a mismatched MIMO detection-model, which can cause performance-degradation. Hence, we develop an information-theoretic tool with generalized mutual information (GMI) to evaluate the impacts from approximation-errors on the final achievable rate. We show that the approximate noise-whitening method based on BIE not only minimizes the noise-distortion measured from the Kullback-Leibler (KL) divergence, but also asymptotically maximizes GMI of the MIMO system as signal-to-noise ratio (SNR) increases. Besides, the proposal also provides a unified framework for approximate matrix-inverse with an adjustable band-size that can tune the trade-off between complexity and accuracy.

Reducing near-field measurement times is an important challenge for future antenna measurement systems. We propose to incorporate knowledge about material parameters of the antenna measurement environment within the simulation model. To do so, a method-of-moments code with surface discretization is implemented as a side constraint to the near-field far-field transformation problem performed with the fast irregular antenna field transformation algorithm. Transformation and source reconstruction results of synthetic measurement data demonstrate the effectiveness of the proposed method.

The proliferation of Internet of Things (IoT) systems is having a profound impact across all aspects of life.  Recognising and identifying particular users is central to delivering the personalised experience that citizens want to experience, and that organisations wish to deliver.  This article presents a survey of human-computer interaction-based (HCI-based) and natural habits-based behavioral biometrics that can be acquired unobtrusively through smart devices or IoT sensors for user recognition purposes. Robust and usable user recognition is also a security requirement for emerging IoT ecosystems to protect them from adversaries. Typically, it can be specified as a fundamental building block for most types of human-to-things accountability principles and access-control methods. However, end-users are facing numerous security and usability challenges in using currently available knowledge- and token-based recognition (i.e., authentication and identification) schemes. To address the limitations of conventional recognition schemes, biometrics, naturally come as a first choice to supporting sophisticated user recognition solutions. We perform a comprehensive review of touch-stroke, swipe, touch signature, hand-movements, voice, gait and footstep behavioral biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioral biometrics  with a goal to identify their attributes and features for generating unique identification signatures. Finally, we present security, privacy, and usability evaluations that can strengthen the designing of robust and usable user recognition schemes for IoT applications.

It is well-known that probabilistic constellation shaping (PCS) provides a shaping-gain of 1.53dB asymptotically as signal-to-noie (SNR) increases. This is however, under ideal assumptions that the considered system operates in optimal sense and can achieve the Shannon capacity. While in practice the system can operate below the capacity, and in particular when block-error rate (BLER) and throughput are considered which are the most relevant merits, the benefits of PCS are unclear. In this paper, we propose a PCS-transceiver for the fifth-generation new-radio (5G-NR) that supports quadrature-amplitude-modulation (QAM) constellations shaped with PCS, namely, PCS-QAM. We put a special interest in the throughput achieved under a stringent BLER constraint, and validate the effectiveness of PCS with practical detection and decoding algorithms, in comparison to conventional uniform QAM constellations, namely, Uniform-QAM. We also analyze the properties of power-gain, entropy-loss, and peak-to-average power-ratio (PAPR), in connections to the PCS design. Further, we derive a necessary and sufficient condition for a PCS-QAM to outperform a Uniform-QAM in throughput, and prove that the normalized entropy-loss with the PCS-QAM must be less than the BLER obtained with the Uniform-QAM. Furthermore, we demonstrate that the PCS-transceiver is flexible in rate-adaption without affecting the encoder, and yields a better throughput-envelope by mitigating the SNR-gaps between two adjacent modulation and coding scheme (MCS) indexes in 5G-NR.

Jamming attacks significantly degrade the performance of wireless communication systems and can lead to significant overhead in terms of re-transmissions and increased power consumption. Although different jamming techniques are discussed in the literature, numerous open-source implementations have used expensive equipment in the range of thousands of dollars with the exception of a few. These implementations have also tended to be partial band, and do not cover the whole available bandwidth of the system under attack. In this work, we demonstrate that flexible, reliable, and low priced software-defined radio (SDR) jamming is feasible by designing and implementing different types of jammers against IEEE 802.11n networks. First, to demonstrate the optimal jamming waveform, we present an analytical bit error rate expression of the system under attack by employing two common jamming waveforms: Gaussian noise and digitally modulated. Then, we validate this analysis through simulations using the MATLAB WLAN toolbox. Afterwards, we implement JamRF, a toolkit that employs a low-cost SDR to implement numerous types of jammers to validate the analysis. Obtained results showed that, to jam the whole 2.4GHz spectrum, a stateful-reactive jammer employing random channel hopping jamming strategy, achieves a packet loss ratio above 90%.

In this paper, we propose the application of deep convolutional neural networks for the detection of radio signals in the broadband frequency spectrum. The RFROI–CNN (Radio Frequency Regions of Interests) approach uses a different way of proposing regions than in typical convolutional object detectors in images – instead of proposing regions based on the distinguishing features of specific radio transmissions, the radio signals are treated as masks that obscure the actual noise distribution, and a deep network is used to estimate the noise distribution in the broadband radio frequency spectrum. This approach allows the SNR of the signals to be maximised in signal processing and, consequently, detection characterised by higher values of quality indicators.

The number of road fatalities is continually increasing, and the age of autonomous cars on the road is swiftly approaching. Internet of things IoT, as well as the utilization of Big Data generated by IoT and M2M communication, offer a lot of potential for enhancing road safety. A Deep Learning technique without a training dataset that employs just a safety model and optimizes it sequentially through IoT learning over time might prevent a shortage of adequate Knowledge Bases while also boosting the ability to handle unforeseen circumstances. The article describes an Internet of Things (IoT) learning approach that uses in-vehicle sensors to prevent traffic accidents.

The increase in popularity of wireless networks in industrial, embedded, medical and public sectors has made them an appealing attack surface for attackers who exploit the vulnerabilities in network protocols to launch attacks such as Evil Twin, Man-in-the-middle, sniffing, etc., which may result in economic and non-economic losses. To protect wireless networks against such attacks, IEEE 802.11 keep updating the protocol standards with new and more secure versions. There has always been a direct correlation between attacks and the improvement of protocol standards. As the sophistication of attacks increases, protocol standards tend to move towards higher security, resulting in a significant rise in both latency and computational overhead, and severe degradation in the performance of low-latency applications such as Industrial Internet of Things (IIoT), automotive, robotics, etc. In this paper, we make the first attempt to highlight the importance of both latency and security in wireless networks from implementation and performance perspective. We make a review of existing IEEE 802.11 protocols in terms of security offered and overhead incurred to substantiate the fact that there is a need of a protocol which in addition to providing optimum security against attacks also maintains the latency and overhead. We also propose a secure and low-latency protocol known as Secure Authentication Protocol (SAP) which operates in two phases - registration and authentication, where the first phase is a one time process implemented using asymmetric cryptography and the second phase is implemented using symmetric cryptography. The protocol is structured in a way that it maintains the original structure of IEEE 802.11 protocols and performs both phases using fewer messages than existing protocols. By simulating the protocol using well-established OMNeT++ simulator, we proved that the proposed protocol incurs a low computation overhead, making it ideal for low-latency applications. We extensively verified the security properties of the proposed protocol using formal verification through widely-accepted Scyther tool. Finally, we perform a comparative analysis of SAP with existing IEEE 802.11 wireless network protocols to highlight the improvement.

Physics informed neural networks (PINNs) have been a well-known tool in solving forward and inverse partial differential equations (PDEs) problem. In particular, for the forward problem, only the initial/boundary conditions and the equation itself as the physical information are needed to obtain the predicted solution within the definition domain. However, some existing works show that the standard PINN method is insufficient for solving complex problems. For the relatively complex one-dimensional Euler equation problem, due to the limited accuracy of the standard PINN method, the algorithm is improved in two aspects in this study. First, a gradient-related weight function is introduced into the loss function and the framework of gradient-weighted physics-informed neural networks (gwPINNs) is proposed. In particular, the weight function is carefully designed. Second, the sequence-to-sequence (seq2seq) learning is applied in the training process to improve the precision. Then, four cases of Riemann problems are solved using the improved algorithm. The results show that the improved algorithm can obtain high accuracy in solving these problems, and verify the feasibility of solving one-dimensional Euler equation based on the improved PINN method.

While perturbation training is known to be effective in reducing fall risk, it is unclear whether the interval of perturbations affects motor response. We investigate postural responses that could vary with the interval of perturbations, probably leading to different contributions of relevant learning substrates. A total of 12 male volunteers with no neurological deficits (age: 33.33±3.12 S.D.) experienced two sequences of perturbations. Two sequences of perturbations were designed and administered in turn: the first sequence consisted of 24-time repeated perturbations with an interval of 5 seconds, while the second sequence consisted of ones with an interval of 2.5 seconds. A perturbation of a smaller magnitude was inserted into each sequence as a catch trial. Perturbations were given by a force plate moving in the anterior-posterior direction. The magnitude of the excursions of the center of pressure (COP) and ankle angle in response to perturbations with a longer interval is greater in comparison to that with a shorter interval (P < 0.05). A difference in responses to the perturbation following the catch trial appears in COP (P < 0.05), not in ankle angle (P > 0.05). These results suggest that while contraction of agonist muscles and co-contraction of antagonistic muscle pairs across the ankle joint for stability operate independently of each other, the refinement of the neuromotor system for a newly trained response can be modulated with stimulus intervals. The dependency of postural responses on the interval could imply that the strength of the learning effect varies with stimulus intervals.

Tensor decomposition has been demonstrated to be successful in a wide range of applications, from neuroscience and wireless communications to social networks. In an online setting, factorizing tensors derived from multidimensional data streams is however non-trivial due to several inherent problems of real-time stream processing. In recent years, many research efforts have been dedicated to developing online techniques for decomposing such tensors, resulting in significant advances in streaming tensor decomposition or tensor tracking. This topic is emerging and enriches the literature on tensor decomposition, particularly from the data stream analystics perspective. Thus, it is imperative to carry out an overview of tensor tracking to help researchers and practitioners understand its development and achievements, summarise the current trends and advances, and identify challenging problems. In this article, we provide a contemporary and comprehensive survey on different types of tensor tracking techniques. We particularly categorize the state-of-the-art methods into three main groups: streaming CP decompositions, streaming Tucker decompositions, and streaming decompositions under other tensor formats (i.e., tensor-train, t-SVD, and BTD). In each group, we further divide the existing algorithms into sub-categories based on their main optimization framework and model architectures. Finally, we present several research challenges, open problems, and potential directions of tensor tracking in the future.

Adept network management is key for supporting extremely heterogeneous applications with stringent quality of service (QoS) requirements; this is more so when envisioning the complex and ultra-dense 6G mobile heterogeneous network (HetNet). From both the environmental and economical perspectives, non-homogeneous QoS demands obstruct the minimization of the energy footprints and operational costs of the envisioned robust networks. As such, network intelligentization is expected to play an essential role in the realization of such sophisticated aims. The fusion of artificial intelligence (AI) and mobile networks will allow for the dynamic and automatic configuration of network functionalities. Machine learning (ML), one of the backbones of AI, will be instrumental in forecasting changes in network loads and resource utilization, estimating channel conditions, optimizing network slicing, and enhancing security and encryption. However, it is well known that ML tasks themselves incur massive computational burdens and energy costs. To overcome such obstacles, we propose a novel layer-based HetNet architecture which optimally distributes tasks associated with different ML approaches across network layers and entities; such a HetNet boasts multiple access schemes as well as device-to-device (D2D) communications to enhance energy efficiency via collaborative learning and communications.

While stroke survivors with moderate or mild impairment are typically able to open their hand at will, those with severe impairment are not. Abnormal synergies govern the arm and hand in stoke survivors with severe impairment, so hand opening, which is required to overcome the working synergy, is a task extremely difficult for them to achieve. It is universally accepted that alternative tracts including the cortico-reticulospinal tract (CRST), employed in the case that the corticospinal tract (CST) is damaged by stroke, brings about such abnormal synergies. Here we note that hand closing is enabled by alternative tracts as well as the CST, and raise a research question: does motor characteristics while closing the hand depend on the integrity of the CST? In this study, we evaluate the ability of 17 stroke survivors to flex and relax the metacarpophalangeal (MCP) joints and investigate whether motor characteristics can be distinguished based on CST integrity which is estimated using upper-extremity Fugl-Meyer (UEFM). UEFM scores have been perceived as an indirect indicator of CST integrity. We found that participants with the UEFM score above a certain value, who are assumed to use the CST, moves the MCP joints more smoothly (p<0.05) and activates the flexor to flex the joints faster (p<0.05), in comparison to participants with low UEFM scores, who are assumed to use alternative tracts. The results present evidence that use of alternative tracts (i.e. the CRST) results in a degradation in movement smoothness and slow activation of the MCP flexor.

In forensic analysis of any document, Ink mismatch detection provides a very important clue to see whether the document is corrupted or not. In hyperspectral imaging (HSI) ink mismatch detection is used to check whetherthe specific document is written with same pen/ink or different types of pens/inks have been used. In this paper, we show that aHSI of handwritten notes can differentiate between the ink sused to produce the document which are very similar in appearance. The HSI cube we have used for our research has 149 Bands. By using an unsupervised clustering algorithm, we have analyzed the spectral responses of the ink pixels to differentiate between the number of inks used in document. After the successful ink mismatch detection, we have used color labelling to classify the text written with different inks. This technique when applied to a RGB document, is not capable to differentiate between the number of inks used in the document. We hope that our findings and technique will help to examine HSI documents for forensic analysis.

This paper presents theoretical studies on Asymptotic Convergence Rate (ACR)  for finite dimensional optimization. Given the problem function (fitness function), ACR measures how fast an iterative optimization method converges to the global solution as the number of iterations increases to infinity. If less than one, it determines  fast exponential  convergence mode ( known as linear convergence in various contexts). The presented theory extends the previous studies on  Average Convergence Rate, a related convergence rate measure. The main focus is on the question how the change of problem function  may influence the value of ACR and what is the relation  between  convergence rate in the objective space and in the search space.  It is shown, in particular,  that the ACR is the maximum of two components, one of which does not depend on the problem function. This provides the lower bound for convergence rate and implies that some  algorithms  cannot converge exponentially fast for any nontrivial continuous optimization problem. Furthermore, among other results, it is shown how the convergence rate in the search space is related to the convergence rate in the objective space if the problem function is dominated by some polynomial. We  discuss various examples and  numerical simulations with use of (1+1) self-adaptive evolution strategy and other algorithms.