The Necessary and Sufficient Conditions for a Universal Conservation of a State of a Unitary Quantum Circuit

By A. Peres and A. Stern's opinions a computational process evolves along a cyclic logic orbit defined by a computation. A. Stern thought that if we could design such a circuit, its operations can be extended to including nonconservative behavior associated with the external perturbations or internal quantum errors. A. Peres did not discuss how to make φM-1 ＝φo. A. Stern proposed only a necessary condition for a conservation of a state of a quantum circuit.In this paper we present a necessary and sufficient condition for a universal conservation of a state of a quantum circuit.We also find all operators which can allow the conservation.

Necessary and Sufficient Conditions of Separability for Multipartite Pure States

In this paper we present a necessary and sufficient condition of separability for multipartite pure states and variants of it. These conditions are very simple and calculable, and they do not require Schmidt decomposition （for two subsystems） or tracing out operations. We also give a necessary condition for a local unitary equivalence class for a bipartite system in terms of the determinant of the matrix of amplitudes.

Artificial Intelligence Driven Resiliency with Machine Learning and Deep Learning Components

The future of any business from banking,e-commerce,real estate,homeland security,healthcare,marketing,the stock market,manufacturing,education,retail to government organizations depends on the data and analytics capabilities that are built and scaled.The speed of change in technology in recent years has been a real challenge for all businesses.To manage that,a significant number of organizations are exploring the Big Data(BD)infrastructure that helps them to take advantage of new opportunities while saving costs.Timely transformation of information is also critical for the survivability of an organization.Having the right information at the right time will enhance not only the knowledge of stakeholders within an organization but also providing them with a tool to make the right decision at the right moment.It is no longer enough to rely on a sampling of information about the organizations'customers.The decision-makers need to get vital insights into the customers'actual behavior,which requires enormous volumes of data to be processed.We believe that Big Data infrastructure is the key to successful Artificial Intelligence(AI)deployments and accurate,unbiased real-time insights.Big data solutions have a direct impact and changing the way the organization needs to work with help from AI and its components ML and DL.In this article,we discuss these topics.

从计算机科学理论审视意识和通用人工智能

1.Introduction We have defined the Conscious Turing Machine(CTM)for the purpose of investigating a theoretical computer science(TCS)approach to consciousness[1].For this,we have hewn to the TCS demand for simplicity and understandability.The CTM is consequently and intentionally a simple machine.It is not a model of the brain,though its design has greatly benefited—and continues to benefit—from neuroscience and psychology.

Preparation of active carbons from corn stalk for butanol vapor adsorption

Active carbons(ACs) were prepared through chemical activation of biochar from whole corn stalk(WCS)and corn stalk pith(CSP) at varying temperatures using potassium hydroxide as the activating agent. ACs were characterized via pore structural analysis and scanning electron microscopy(SEM). These adsorbents were then assessed for their adsorption capacity for butanol vapor. It was found that WCS activated at900 °C for 1 h(WCS-900) had optimal butanol adsorption characteristics. The BET surface area and total pore volume of the WCS-900 were 2330 m2/g and 1.29 cm3/g, respectively. The dynamic adsorption capacity of butanol vapor was 410.0 mg/g, a 185.1% increase compared to charcoal-based commercial AC(143.8 mg/g).

Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components.Shunt active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents.However,CCVSI with traditional controllers have a limited transient and steady state performance.In this paper,we propose an adaptive dynamic programming(ADP) controller with online learning capability to improve transient response and harmonics.The proposed controller works alongside existing proportional integral(PI) controllers to efficiently track the reference currents in the d-q domain.It can generate adaptive control actions to compensate the PI controller.The proposed system was simulated under different nonlinear(three-phase full wave rectifier) load conditions.The performance of the proposed approach was compared with the traditional approach.We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison.The online learning based ADP controller not only reduced average total harmonic distortion by 18.41%,but also outperformed traditional PI controllers during transients.

Review of Key Management Mechanisms in Wireless Sensor Networks

Recent advancements in wireless communication and microchip techniques have accelerated the development of wireless sensor networks (WSN). Key management in WSN is a critical and challenging problem because of the inner characteristics of sensor networks: deployed in hostile environments, limited resource and ad hoc nature. This paper investigates the constraints and special requirements of key management in sensor network environment, and some basic evaluation metrics are introduced. The key pre-distribution scheme is thought as the most suitable solution for key management problem in wireless sensor networks. It can be classified into four classes: pure probabilistic key pre-distribution, polynomial-based, Blom's matrix-based, and deterministic key pre-distribution schemes. In each class of methods, the related research papers are discussed based on the basic evaluation metrics. Finally, the possible research directions in key management are discussed.

Classification Approach for Intrusion Detection in Vehicle Systems

Vehicular ad hoc networks (VANETs) enable wireless communication among Vehicles and Infrastructures. Connected vehicles are promising in Intelligent Transportation Systems (ITSs) and smart cities. The main ob-jective of VANET is to improve the safety, comfort, driving efficiency and waiting time on the road. VANET is unlike other ad hoc networks due to its unique characteristics and high mobility. However, it is vulnerable to various security attacks due to the lack of centralized infrastructure. This is a serious threat to the safety of road traffic. The Controller Area Network (CAN) is a bus communication protocol which defines a standard for reliable and efficient transmission between in-vehicle parts simultaneously. The message moves through CAN bus from one node to another node, but it does not have information about the source and destination address for authentication. Thus, the attacker can easily inject any message to lead to system faults. In this paper, we present machine learning techniques to cluster and classify the intrusions in VANET by KNN and SVM algorithms. The intrusion detection technique relies on the analysis of the offset ratio and time interval between the messages request and the response in the CAN.

Noise Reduction in Industry Based on Virtual Instrumentation

This paper discusses the reduction of background noise in an industrial environment to extend human-machine-interaction.In the Industry 4.0 era,the mass development of voice control(speech recognition)in various industrial applications is possible,especially as related to augmented reality(such as hands-free control via voice commands).As Industry 4.0 relies heavily on radiofrequency technologies,some brief insight into this problem is provided,including the Internet of things(IoT)and 5G deployment.This study was carried out in cooperation with the industrial partner Brose CZ spol.s.r.o.,where sound recordings were made to produce a dataset.The experimental environment comprised three workplaces with background noise above 100 dB,consisting of a laser/magnetic welder and a press.A virtual device was developed from a given dataset in order to test selected commands from a commercial speech recognizer from Microsoft.We tested a hybrid algorithm for noise reduction and its impact on voice command recognition efficiency.Using virtual devices,the study was carried out on large speakers with 20 participants(10 men and 10 women).The experiments included a large number of repetitions(100 times for each command under different noise conditions).Statistical results confirmed the efficiency of the tested algorithms.Laser welding environment efficiency was 27%before applied filtering,76%using the least mean square(LMS)algorithm,and 79%using LMS+independent component analysis(ICA).Magnetic welding environment efficiency was 24%before applied filtering,70%with LMS,and 75%with LMS+ICA.Press workplace environment efficiency showed no success before applied filtering,was 52%with LMS,and was 54%with LMS+ICA.

Non-Statistical Multi-Beamformer

In this paper, a multiple beamforming technique is presented by using a direct data domain least squares (D3LS) approach. Direct data domain approach is very suitable for real time applications since it utilizes only a single snapshot of data as opposed to statistical approaches where multiple measurements have to be taken and the covariance matrix has to be formed. It is also very effective especially in the case of blinking jammers where the statistical approaches will fail or needs to perform additional tasks to overcome it. It has been previously shown that the D3LS can successfully handle only one or two Signal of Interests (SOI). Here, we have developed a new technique where multiple SOI can be handled simultaneously. Numerical simulations have shown that the new approach can maximize the signals from the direction of the SOI at the same time minimizing the jammers. The new approach can be successfully applied in the satellite communications, Over the Horizon Radars (OTHR) as well as wireless communications to detect or track multiple targets simultaneously.

An Experimental Simulation of a Design Three-Port DC-DC Converter

Traditional DC-DC converter topologies interface two power terminals: a source and a load. The construction of diverse and flexible power management and distribution (PMAD) systems with such topologies is governed by a tight compromise between converter count, efficiency, and control complexity. The broader impact of the current research activity is the development of enhanced power converter systems suitable for a wide range of applications. Potential users of this technology include the designers of portable and stand-alone systems such as laptops, hand-held electronics, and communication repeater stations. High power topology options support the evolution of clean power technologies such as hybrid-electric vehicles (HEV’s) and solar vehicles. DC-DC converter is considered as an advanced environmental issue since it is a greenhouse emission eliminator. By utilizing the advancement of these renewable energy sources, we minimize the use of fossil fuel. Thus, we will have a cleaner and pollution free environment. In this paper, a three-port DC-DC converter is designed and discussed. The converter was built and tested at the energy research laboratory at Taibah University, Al Madinah, KSA.

MICROPROCESSOR BASED PHASELOCKED LOOP SPEED CONTROL SYSTEM FOR AC MOTOR

In this paper the authors present an analysis and the implementation of microprocessor-baseddigital phase-locked loop speed control system for an induction motor which is actuated by aSPWM-GTR inverter.The system is controlled by a 16-bit single chip microprocessor.A new type of frequency and phase detector is presented in detail,An adaptive method isadopted in speed controller.A three mode control scheme is used.These techniques are very use-ful to the improvement of the dynamic behavior of digital AC motor drive system.Experimental results show that the system is of good stability,high precision and good dynam-ic performance.

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition:The Possibilities of Next Generation 5G Data Networks

This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction.Modern vehicles are nowadays increasingly supporting voice commands,which are one of the pillars of autonomous and SMART vehicles.Robust speaker recognition for context-aware in-vehicle applications is limited to a certain extent by in-vehicle back-ground noise.This article presents the new concept of a hybrid system which is implemented as a virtual instrument.The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction.The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks,which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles.On average,the unfiltered voice commands were successfully recognized in 29.34%of all scenarios,while the LMS reached up to 71.81%,and LMS-ICA hybrid improved the performance further to 73.03%.

Towards Realistic Vibration Testing of Large Floor Batteries for Battery Electric Vehicles (BEV)

This contribution shows an analysis of vibration measurement on large floor-mounted traction batteries of Battery Electric Vehicles(BEV).The focus lies on the requirements for a realistic replication of the mechanical environments in a testing laboratory.Especially the analysis on global bending transfer functions and local corner bending coherence indicate that neither a fully stiff fixation of the battery nor a completely independent movement on the four corners yields a realistic and conservative test scenario.The contribution will further show what implication these findings have on future vibration&shock testing equipment for large traction batteries.Additionally,it will cover an outlook on how vibration behavior of highly integrated approaches(cell2car)changes the mechanical loads on the cells.

CNN-Based Forensic Method on Contrast Enhancement with JPEG Post-Processing

As one of the most popular digital image manipulations,contrast enhancement(CE)is frequently applied to improve the visual quality of the forged images and conceal traces of forgery,therefore it can provide evidence of tampering when verifying the authenticity of digital images.Contrast enhancement forensics techniques have always drawn significant attention for image forensics community,although most approaches have obtained effective detection results,existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format.The detection of forgery on contrast adjustments in the presence of JPEG post processing is still a challenging task.In this paper,we propose a new CE forensic method based on convolutional neural network(CNN),which is robust to JPEG compression.The proposed network relies on a Xception-based CNN with two preprocessing strategies.Firstly,unlike the conventional CNNs which accepts the original image as its input,we feed the CNN with the gray-level co-occurrence matrix(GLCM)of image which contains CE fingerprints,then the constrained convolutional layer is used to extract high-frequency details in GLCMs under JPEG compression,finally the output of the constrained convolutional layer becomes the input of Xception to extract multiple features for further classification.Experimental results show that the proposed detector achieves the best performance for CE forensics under JPEG post-processing compared with the existing methods.

Electrical Brain Stimulation to Treat Neurological Disorder

Neurological disorders with symptoms such as chronic pain,depression,and insomnia are widespread.Very weak electric fields applied through the skull can enhance or diminish neural activity and modulate brain waves in order to treat many of these common medical problems.This approach is to be contrasted with well-established pharmacological methods or more recent invasive electrical Deep Brain Stimulation(DBS)techniques that require surgery to insert electrodes deep into the brain.We claim that Non-Invasive Brain Stimulation(NIBS)will provide new treatment methods with much greater simplicity,lower cost,improved safety and in some cases,possibly greater effectiveness.This emerging use of NIBS is a branch of a new multidisciplinary field that we coined Neuro-systems Engineering[1].This field involves neuroscientists,psychologists,and electrical engineers.This emerging field relies on existing standards for the safe implementation of these novel treatment modalities[2].Methods of stimulating the brain are based on emerging electro-technologies such as transcranial Direct Current/Alternating Current(DC/AC)electric fields and pulsed magnetic fields.Application of functional and time-dependent brain imaging methods can be used to locate relevant brain regions and determine the most appropriate stimulation method.Application of tailored and individualized control can be combined with other therapy methods to effectively treat neurological disorders while minimizing or even eliminating the use of pharmaceuticals.In this paper,we are presenting our embodiment for a closed loop,feedback controlled,non-invasive application of electrical stimulation of the brain to enhance individual/group performance or to treat neurological disorders.

New Hybrid IoT LoRaWAN/IRC Sensors:SMARTWater Metering System

The massive development of internet of things(IoT)technologies is gaining momentum across all areas of their possible deployment—spanning from Industry 4.0 to eHealth,smart city,agriculture or waste management.This ongoing development is further pushed forward by the gradual deployment of 5G networks.With 5G capable smart devices,it will be possible to transfer more data with shorter latency thereby resulting in exciting new use cases such as Massive IoT.Massive-IoT(low-power wide area network-LPWAN)enables improved network coverage,long device operational lifetime and a high density of connections.Despite all the advantages of massive-IoT technology,there are certain cases where the original concept cannot be used.Among them are dangerous explosive environments or issues caused by subsurface deployment(operation during winter months or dense greenery).This article presents the concept of a hybrid solution of IoT LoRaWAN(long range wide area network)/IRC-VLC(infrared communication,visible light communication)technology,which combines advantages of both technologies according to the deployment scenario.

Modeling for Underground Cable Water Tree Growth Dynamics

Underground cable faults, whether transient or permanent, are traceable to in-sulation failure problems, most of which are water tree initiated. Insulation breakdown, which usually leads to costly power outages, may be prevented by taking pre-emptive actions. The most decisive pre-emptive action is one in which real-time tracking of water tree advancement within the cable insulation system is possible. Such pre-emptive actions, however, depend on accurate modeling of the phenomenon. Earlier water tree models are static in that they focused on the cable insulation property change at a time segment. Thus, they lack the properties needed for tracking water tree progress and for determining the onset of transient and permanent faults. This paper presents a new ap-proach to water tree modeling, focused on insulation degradation geometry in the form of parabolic expansion of water tree. We developed a dynamic model centered on the computation of the capacitance of a vented water tree as a function of time. The dynamic model accounts for the time-dependence of the radial growth of the water tree to track insulation degradation. The model was tested in predicting cross-linked polyethylene (XLPE) cable’s insulation lifespan. The result was found to be within the range of the recorded lifespan of field aged cables in the literature. Also, performance comparison with an earlier analytical model validated with COMSOL Hyperphysics software shows a signif-icant correlation between them.

A Neural Network Algorithm to Detect Sulphur Dioxide Using IASI Measurements

The remote sensing of volcanic sulphur dioxide (SO2) is important because it is used as a proxy for volcanic ash, which is dangerous to aviation and is generally more difficult to discriminate. This paper presents an Artificial Neural Network (ANN) algorithm that recognizes volcanic SO2 in the atmosphere using hyperspectral remotely sensed data from the IASI instrument aboard the Metop-A satellite. The importance of this approach lies in exploiting all thermal infrared spectral information of IASI and its application to near real-time volcanic monitoring in a fast manner. In this paper, the ANN algorithm is demonstrated on data of the Eyjafjallajokull volcanic eruption (Iceland) during the months of April and May 2010, and on the Grímsvotn eruption occurring during May 2011. The algorithm consists of a two output neural network classifier trained with a time series consisting of some hyperspectral eruption datasets collected during 14 April to 14 May 2010 and a few from 22 to 26 May 2011. The inputs were all channels (441) in the IASI v3 band and the target outputs (truth) were the corresponding retrievals of SO2 amount obtained with an optimal estimation method. The validation results for the Eyjafjallajokull independent data-sets had an overall accuracy of 100% and no commission errors, therefore demonstrating the feasibility of estimating the presence of SO2 using a neural network approach also a in cloudy sky conditions. Although the validation of the neural network classifier on datasets from the Grímsvotn eruption had no commission errors, the overall accuracies were lower due to the presence of omission errors. Statistical analysis revealed that those false negatives lie near the detection threshold for discriminating pixels affected by SO2. This demonstrated that the accuracy in classification is strictly related to the sensitivity of the model. The lower accuracy obtained in detecting SO2 for Grímsvotn validation dates might also be caused by less statistical knowledge of such an eruption during the training phase.

Surface Morphology of Reactive Powder Concrete Containing Soil

Recent studies have revealed that concrete can be used as a media to contain As （arsenic） removed from drinking water. Concrete, which is a composite material, has been effective in solidifying hazardous wastes and contaminated soils. A research project was conducted to study the effects of uncontaminated soil and arsenic contaminated soil on the microstructure of concrete to qualitatively define the mechanisms of the encapsulation of soils containing inorganic material such as arsenic by application of solidification/stabilization technique. This research paper focused on studying the surface morphology of RPC （reactive powder concrete） containing soil.