The article herein presents the method and algorithms for forming the feature space for the base of intellectualized system knowledge for the support system in the cyber threats and anomalies tasks. The system being elaborated might be used both autonomously by cyber threat services analysts and jointly with information protection complex systems. It is shown, that advised algorithms allow supplementing dynamically the knowledge base upon appearing the new threats, which permits to cut the time of their recognition and analysis, in particular, for cases of hard-to-explain features and reduce the false responses in threat recognizing systems, anomalies and attacks at informatization objects. It is stated herein, that collectively with the outcomes of previous authors investigations, the offered algorithms of forming the feature space for identifying cyber threats within decisions making support system are more effective. It is reached at the expense of the fact, that, comparing to existing decisions, the described decisions in the article, allow separate considering the task of threat recognition in the frame of the known classes, and if necessary supplementing feature space for the new threat types. It is demonstrated, that new threats features often initially are not identified within the frame of existing base of threat classes knowledge in the decision support system. As well the methods and advised algorithms allow fulfilling the time-efficient cyber threats classification for a definite informatization object.
A compact Sierpinski Carpet square fractal multiband antenna operating at 3.9 (WiMAX) /6.6 (Satellite TV) /8.1/10.7/11.8 GHz (X-band) is presented. The proposed Microstrip Patch Antenna (MSPA) consists of a Sierpinski Carpet square fractal radiator in which square slots are etched out and a tapered microstrip feed line. The Sierpinski Carpet square fractal patch modifies the current resonant path thereby making the antenna to operate at five useful bands. Impedance matching at these bands are solely achieved by using Sierpinski square slot and tapered feedline, thus eliminating the need of any external matching circuit. The dimensions of the compact antenna is 32 x 32 x 1,6 mm3 and exhibits S11<-10dB bandwidth of about 4.8% (4.01-3.82 GHz), 2.1% (6.62-6.48 GHz), 2.7% (8.24-8.02 GHz), 2.1% (10.77-10.54 GHz) and 21% (12.1-11.60 GHz) with the gain of 7.57/3.91/3.77/6.74/1.33 dB at the operating frequencies 3.9/6.6/8.1/10.7 and 11.8 GHz, respectively under simulation analysis carried out by using HFSS v.13.0.
New optimized 2x2 slotted array antenna is designed using HFSS to operate at 28.1 GHz, using RT6010 substrate with height of 1.6 mm, tangent loss of 0.0023 and dielectric constant of 10.2 and overall dimension of 12x12 mm2, for 5G mobile applications. The 2x2 slotted linear array antenna achieved a high gain of 18.3 dB at 28.1 GHz with 10 dB bandwidth of 1.39 GHz, and with 83.21% of size reduction.
This paper presents a printed dual band monopole antenna working below 250 MHz using meander line and an added stub. Meander line approach is used to reduce the size of the low frequency monopole. The proposed antenna is fed by microstrip line and printed on FR-4 substrate with an overall size of 290 x 83 mm2. The added stub tuned dual band operation at 114 MHz and 221 MHz with measured reflection coefficient of -19 dB at both bands. The antenna has omni-directional characteristics with efficiency greater than 90% and gain of 1.87, 1.7 dBi at both bands respectively. The antenna design is optimized through a detailed parametric study. This study includes varying stub, Meander, feed and ground dimensions. The antenna has been fabricated and measured where dual band operation in the MHz range is verified.
The paper introduces a topology mutation – the novel concept in Moving Target Defense (MTD). MTD is a new technique that represents a significant shift in cyber defense. Traditional cybersecurity techniques have primarily focused on the passive defense of static networks only. In MTD approach cyber attackers are confused by making the attack surface dynamic, and thus harder to probe and infiltrate. The emergence of Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technology has opened up new possibilities in network architecture management. The application of combined NFV and SDN technologies provides a unique platform for implementing MTD techniques for securing the network infrastructure by morphing the logical view of the network topology.
Waste management is a challenging problem for most of the countries. The current waste segregation and the collection method are not efficient and cost-effective. In this paper, a prototype is presented for smart waste management. It is also capable of waste segregation at the ground level and providing real-time data to the administrator. Impact and cost analysis of the deployment of smartbin is also presented considering one ward of Ahmedabad Municipal Corporation. It is clear from that deployment of this smartbin will save about 40% of the current expenditure for that ward.
In this paper a review on biometric person identification has been discussed using features from retinal fundus image. Retina recognition is claimed to be the best person identification method among the biometric recognition systems as the retina is practically impossible to forge. It is found to be most stable, reliable and most secure among all other biometric systems. Retina inherits the property of uniqueness and stability. The features used in the recognition process are either blood vessel features or non-blood vessel features. But the vascular pattern is the most prominent feature utilized by most of the researchers for retina based person identification. Processes involved in this authentication system include pre-processing, feature extraction and feature matching. Bifurcation and crossover points are widely used features among the blood vessel features. Non-blood vessel features include luminance, contrast, and corner points etc. This paper summarizes and compares the different retina based authentication system. Researchers have used publicly available databases such as DRIVE, STARE, VARIA, RIDB, ARIA, AFIO, DRIDB, and SiMES for testing their methods. Various quantitative measures such as accuracy, recognition rate, false rejection rate, false acceptance rate, and equal error rate are used to evaluate the performance of different algorithms. DRIVE database provides 100% recognition for most of the methods. Rest of the database the accuracy of recognition is more than 90%.
In the last few years, a great attention was paid to the deep learning Techniques used for image analysis because of their ability to use machine learning techniques to transform input data into high level presentation. For the sake of accurate diagnosis, the medical field has a steadily growing interest in such technology especially in the diagnosis of melanoma. These deep learning networks work through making coarse segmentation, conventional filters and pooling layers. However, this segmentation of the skin lesions results in image of lower resolution than the original skin image. In this paper, we present deep learning based approaches to solve the problems in skin lesion analysis using a dermoscopic image containing skin tumor. The proposed models are trained and evaluated on standard benchmark datasets from the International Skin Imaging Collaboration (ISIC) 2018 Challenge. The proposed method achieves an accuracy of 96.67% for the validation set .The experimental tests carried out on a clinical dataset show that the classification performance using deep learning-based features performs better than the state-of-the-art techniques.
In this paper, a new 11T SRAM cell using FinFET technology has been proposed, the basic component of the cell is the 6T SRAM cell with 4 NMOS access transistors to improve the stability and also makes it a dual port memory cell. The proposed cell uses a header scheme in which one extra PMOS transistor is used which is biased at different voltages to improve the read and write stability thus, helps in reducing the leakage power and active power. The cell shows improvement in RSNM (Read Static Noise Margin) with LP8T by 2.39x at sub-threshold voltage 2.68x with D6T SRAM cell, 5.5x with TG8T. The WSNM (Write Static Noise Margin) and HM (Hold Margin) of the SRAM cell at 0.9V is 306mV and 384mV. At sub-threshold operation also it shows improvement. The Leakage power reduced by 0.125x with LP8T, 0.022x with D6T SRAM cell, TG8T and SE8T. Also, impact of process variation on cell stability is discussed.
Usually, cellular networks are modeled by placing each tier (e.g macro, pico and relay nodes) deterministically on a grid. When calculating the metric performances such as coverage probability, these networks are idealized for not considering the interference. Overcoming such limitation by realistic models is much appreciated. This paper considered two- tier twohop cellular network, each tier is consisting of two-hop relay transmission, relay nodes are relaying the message to the users that are in the cell edge. In addition, the locations of the relays, base stations (BSs), and users nodes are modeled as a point process on the plane to study the two hop downlink performance. Then, we obtain a tractable model for the k-coverage probability for the heterogeneous network consisting of the two-tier network. Stochastic geometry and point process theory have deployed to investigate the proposed two-hop scheme. The obtained results demonstrate the effectiveness and analytical tractability to study the heterogeneous performance.
The paper presents a concept of a control system for a high-frequency three-phase PWM grid-tied converter (3x400 V / 50 Hz) that performs functions of a 10-kW DC power supply with voltage range of 600÷800 V and of a reactive power compensator. Simulation tests (in PLECS) allowed proper selection of semiconductor switches between fast IGBTs and silicon carbide MOSFETs. As the main criterion minimum amount of power losses in semiconductor devices was adopted. Switching frequency of at least 40 kHz was used with the aim of minimizing size of passive filters (chokes, capacitors) both on the AC side and on the DC side. Simulation results have been confirmed in experimental studies of the PWM converter, the power factor of which (inductive and capacitive) could be regulated in range from 0.7 to 1.0 with THDi of line currents below 5% and energy efficiency of approximately 98.5%. The control system was implemented in Texas Instruments TMS320F28377S microcontroller.
This work presents a theoretical study for the distribution of nanocomposite structure of plasmonic thin-film solar cells through the absorber layers. It can be reduced the material consumption and the cost of solar cell. Adding nanometallic fillers in the absorber layer has been improved optical, electrical characteristics and efficiency of traditional thin film solar cells (ITO /CdS/PbS/Al and SnO2/CdS/CdTe/Cu) models that using sub micro absorber layer. Also, this paper explains analysis of J-V, P-V and external quantum efficiency characteristics for nanocomposites thin film solar cell performance. Also, this paper presents the effect of increasing the concentration of nanofillers on the absorption, energy band gap and electron-hole generation rate of absorber layers and the effect of volume fraction on the energy conversion efficiency, fill factor, space charge region of the nanocomposites solar cells.
In this fast-changing environmental condition, the effect of fossil fuel in vehicle is a significant concern. Many sustainable sources are being studied to replace the exhausting fossil fuel in most of the countries. This paper surveys the types of electric vehicle’s energy sources and current scenario of the onroad electric vehicle and its technical challenges. It summarizes the number of state-of-the-art research progresses in bidirectional dcdc converters and its control strategies reported in last two decades. The performance of the various topologies of bidirectional dc-dc converters is also tabulated along with their references. Hence, this work will present a clear view on the development of state-of-the-art topologies in bidirectional dc-dc converters. This review paper will be a guide for the researchers for selecting suitable bidirectional traction dc-dc converters for electric vehicle and it gives the clear picture of this research field.
To overcome the detrimental influence of α impulse noise in power line communication and the trap of scarce prior information in traditional noise suppression schemes , a power iteration based fast independent component analysis (PowerICA) based noise suppression scheme is designed in this paper. Firstly, the pseudo-observation signal is constructed by weighted processing so that single-channel blind separation model is transformed into the multi-channel observed model. Then the proposed blind separation algorithm is used to separate noise and source signals. Finally, the effectiveness of the proposed algorithm is verified by experiment simulation. Experiment results show that the proposed algorithm has better separation effect, more stable separation and less implementation time than that of FastICA algorithm, which also improves the real-time performance of communication signal processing.
Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.
Operational Transresistance Amplifier (OTRA) has been a topic of great interest recently. OTRA has proved itself to be an appropriate device for the analog applications. As MOS scaling suffers from various problems, carbon nanotube field effect transistor (CNTFET) has came into light as one of the brightest alternative for FET (Field Effect Transistors) based devices. This work has introduced a new CNTFET based OTRA which is capable of realising inverse low pass filter using two OTRAs and few passive elements. CNTFET based OTRA has been designed and simulated at 10nm technology node. The working ability of the designed model has been conformed using HSPICE simulation. It is compared with conventional CMOS based OTRA. The comparative analysis has revealed improvement in various performance parameters. The paper also presents how change in number of carbon nanotube in CNTFETs in OTRA circuit affects the transresistance gain and input impedance. The optimized results are also discussed to improve transresistance gain and input impedance. The paper also dealt with the realisation of inverse low pass filter using proposed CNTFET based OTRA.
The emerging potentials in the electronics field, which facilitate the creation of complex projects with innovative functionalities, while maintaining low costs, are becoming even more appreciated by designers and engineers. In this manuscript, a telemetry system was designed and realized for monitoring main parameters of a racing vehicle. A STM32 Nucleo board acquires data from sensors installed on vehicle and transmits them to a base station. Acquired data are both stored on a SD card and wirelessly transmitted, for ensuring robustness/reliability of operation. The carried out tests confirm the truthfulness and compatibility of acquired data related to the vehicle parameters.
In cellular networks, cells are grouped more densely around highly populated areas to provide more capacity. Antennas are pointed in accordance with local terrain and clutter to reduce signal shadows and interference. Hardware parameters are easily set during installation but difficult to change thereafter. In a dynamic environment of population migration, there is need to continuously tune network parameters to adapt the network performance. Modern mobile equipment logs network usage patterns and statistics over time. This information can be used to tune soft parameters of the network. These parameters may include frequency channel assignment or reuse, and transmitter radiation power assignment to provide more capacity on demand. The paper proposes that by combining the frequency and power assignments, further optimisation in resource allocation can be achieved over a traditional frequency assignment. The solution considers the interference, traffic intensity and use of priority flags to bias some edges. An Edge Weight Power and Frequency Assignment Algorithm is presented to solve the resource allocation problem in cellular networks. The paper also analyses the performance improvements obtained over that of the Edge Weight Frequency Assignment Algorithm. The results show that the proposed algorithm improves the performance of the Edge Weight Frequency Assignment Algorithm depending on the initial structure of the graph.
Super-resolution image reconstruction utilizes two algorithms, where one is for single-frame image reconstruction, and the other is for multi-frame image reconstruction. Singleframe image reconstruction generally takes the first degradation and is followed by reconstruction, which essentially creates a problem of insufficient characterization. Multi-frame images provide additional information for image reconstruction relative to single frame images due to the slight differences between sequential frames. However, the existing super-resolution algorithm for multi-frame images do not take advantage of this key factor, either because of loose structure and complexity, or because the individual frames are restored poorly. This paper proposes a new SR reconstruction algorithm for images using Multi-grained Cascade Forest. Multi-frame image reconstruction is processed sequentially. Firstly, the image registration algorithm uses a convolutional neural network to register low-resolution image sequences, and then the images are reconstructed after registration by the Multi-grained Cascade Forest reconstruction algorithm. Finally, the reconstructed images are fused. The optimal algorithm is selected for each step to get the most out of the details and tightly connect the internal logic of each sequential step. This novel approach proposed in this paper, in which the depth of the cascade forest is procedurally generated for recovered images, rather than being a constant. After training each layer, the recovered image is automatically evaluated, and new layers are constructed for training until an optimal restored image is obtained. Experiments show that this method improves the quality of image reconstruction while preserving the details of the image.
The propagation of EM waves in soil is defined by permittivity and permeability which are in turn affected by the soil parameters such as soil moisture and texture. Therefore, a suitable Dielectric Model like MBSDM is required for the channel characterization of WUSN. Effect of soil parameters and environmental conditions on signal propagation is modelled using Superposition Model. The simulation of these stages is done in MATLAB for UG-UG, UG-AG and AG–UG scenarios. The system is further implemented on the ZYNQ ZC–702 hardware platform.
Utilization of drones is going to become predominated in cellular networks as aerial base stations in order to temporary cover areas where stationary base stations cannot serve the users. Detecting optimal location and efficient number of drone-Base Stations (DBSs) are the targets we tackle in this paper. Toward this goal, we first model the problem using mixed integer non-linear programming. The output of the proposed method is the number and the optimal location of DBSs in a two-dimension area, and the object is to maximize the number of covered users. In the second step, since the proposed method is not solvable using conventional methods, we use a proposed method to solve the optimization problem. Simulation results illustrate that the proposed method has achieved its goals.
An application specific integrated design using Quadrature Linear Discriminant Analysis is proposed for automatic detection of normal and epilepsy seizure signals from EEG recordings in epilepsy patients. Five statistical parameters are extracted to form the feature vector for training of the classifier. The statistical parameters are Standardised Moment, Co-efficient of Variance, Range, Root Mean Square Value and Energy. The Intellectual Property Core performs the process of filtering, segmentation, extraction of statistical features and classification of epilepsy seizure and normal signals. The design is implemented in Zynq 7000 Zc706 SoC with average accuracy of 99%, Specificity of 100%, F1 score of 0.99, Sensitivity of 98% and Precision of 100 % with error rate of 0.0013/hr., which is approximately zero false detection.
To avoid of manipulating search engines results by web spam, anti spam system use machine learning techniques to detect spam. However, if the learning set for the system is out of date the quality of classification falls rapidly. We present the web spam recognition system that periodically refreshes the learning set to create an adequate classifier. A new classifier is trained exclusively on data collected during the last period. We have proved that such strategy is better than an incrementation of the learning set. The system solves the starting–up issues of lacks in learning set by minimisation of learning examples and utilization of external data sets. The system was tested on real data from the spam traps and common known web services: Quora, Reddit, and Stack Overflow. The test performed among ten months shows stability of the system and improvement of the results up to 60 percent at the end of the examined period.
A novel dual mode logic (DML) model has a superior energy-performance compare to CMOS logic. The DML model has unique feature that allows switching between both modes of operation as per the real-time system requirements. The DML functions in two dissimilar modes (static and dynamic) of operation with its specific features, to selectively obtain either low-energy or high-performance. The sub-threshold region DML achieves minimum-energy. However, sub-threshold region consequence in performance is enormous. In this paper, the working of DML model in the moderate inversion region has been explored. The near-threshold region holds much of the energy saving of subthreshold designs, along with improved performance. Furthermore, robustness to supply voltage and sensitivity to the process temperature variations are presented. Monte carol analysis shows that the projected near-threshold region has minimum energy along with the moderate performance.
A detailed study about the suitable perturbation element shape and location for tunable BW dual mode microstrip filter which has circular ring resonator is presented. BW tuning is achieved by resonator geometry modification. The study explains the effect of a perturbation element on the stability of the center frequency during BW tuning. Different cases have been studied for two shapes of perturbation element; which one is a rectangular and the other is a radial. The treated cases discuss whether the perturbation element is located in the inner or in the outer circumference of the ring, and whether it is a patch or a notch. BW tuning simulation treated the case of FBW3dB increase for two and three times. The best case of perturbation element which has the best center frequency stability has been modeled, simulated, and fabricated at 2.4 GHz. Geometry modification of the filter took into account the RF MEMS modeling. The filter has an elliptic frequency response, and its FBW has been increased in five steps from 1.7% to 5%. The designed filters were evaluated experimentally and by simulation with very good agreement.
In the past it was usual to exert a huge effort in the design, simulation, and the real time implementation of the complicated electronic and communication systems, like GNSS receivers. The complexity of the system algorithms combined with the complexity of the available tools created a system that is difficult to track down for debugging or for redesign. So, the simulation and educational tools was different from the prototyping tools. In this paper the parallel search acquisition phase of a GPS receiver was simulated and implemented on FPGA using the same platform and through a graphical programming language. So this paper introduces the fruit of integrating the prototyping tools with the simulation tools as a single platform through which the complicated electronic systems can be simulated and prototyped.
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