Miracle-dz/UniFAR-FTUs huggingface dataset:
Discrete-Time Kalman Filter Design for Linear Infinite-Dimensional Systems

As the optimal linear filter and estimator, the Kalman filter has been extensively utilized for state estimation and prediction in the realm of lumped parameter systems. However, the dynamics of complex industrial systems often vary in both spatial and temporal domains, which take the forms of partial differential equations (PDEs) and/or delay equations. State estimation for these systems is quite challenging due to the mathematical complexity. This work addresses discrete-time Kalman filter design and realization for linear distributed parameter systems. In particular, the structural- and energy-preserving Crank–Nicolson framework is applied for model time discretization without spatial approximation or model order reduction. In order to ensure the time instance consistency in Kalman filter design, a new discrete model configuration is derived. To verify the feasibility of the proposed design, two widely-used PDEs models are considered, i.e., a pipeline hydraulic model and a 1D boundary damped wave equation.
Detecting Successful Attacks from IDS Alerts Based On Emulation of Remote Shellcodes

Server administrators and security operation center analysts receive alerts from an intrusion detection system and check whether attacks have succeeded. However, it is difficult to handle them quickly because a tremendous number of alerts is generated in a short period of time. We propose a method to identify important alerts that lead to security incidents automatically. The key idea is to determine the success or failure of an attack based on traffic logs and the network behaviors observed during shellcode emulation. We evaluated the proposed method in terms of accuracy and performance and found that it can handle more than 60% of remote shellcodes and cope with practical attack cases.
Mitigating the Effect of Noise Uncertainty on the Online State-of-Charge Estimation of Li-Ion Battery Cells

Real-time battery state-of-charge (SOC) estimation is critical in many applications. The extended Kalman filter (EKF) has been successfully deployed in SOC estimation allowing real-time SOC monitoring. However, modeling inaccuracies, measurement faults, and wrong initialization can cause the estimation algorithm to diverge. The precise knowledge of statistical information about process and measurements noise is crucial for accurate system modeling and estimation. This paper presents a novel SOC estimation approach based on maximum-likelihood estimation (MLE). The process and measurements models are transformed to an error state propagation system where the innovation covariance is utilized to maximize the likelihood of the multivariate innovation distribution with respect to process and measurement covariances. The MLE formulation allows the estimation of the process and measurement noise covariance magnitudes, which are used to obtain an optimal SOC estimate. The proposed method is validated experimentally using a number of Li-ion battery cells under various testing conditions. The estimation performance is compared with that of the conventional EKF technique as well as previously published results based on autocovariance least-squares measurements noise estimation. The results indicate an enhanced performance for the new algorithm over the traditional EKF across all conducted tests.
A Methodology for Developing Normalized Formative Indices Using Messy Data

The global expansion of information technology is providing MIS researchers greater accessibility to secondary data sources than ever before. Unfortunately, practically all of the variables in this era of Big Data are non-normally distributed and many have large proportions of missing values. In many cases, extreme distributional problems such as inflated frequencies (e.g., stacks of zeroes) inhibit statistical analyses for data mining or theory testing purposes. Because of the massive amounts of available ‘messy’ data, practitioners and researchers need methods for prioritizing and analyzing their archival datasets. Thus, we propose a normalized formative index development methodology that progresses through five stages: content specification, data collection, data reduction, technical validation and norming. Each stage of the proposed methodology is validated by its successful use in multidisciplinary research on formative index construction.
On Data Center Demand Response: A Cloud Federation Approach

The significantly high energy consumption of data centers constitutes a major load on the smart power grid. Data center demand response is a promising solution to incentivize the cloud providers to adapt their consumption to the power grid conditions. These policies not only mitigate the operational stability issues of the smart grid but also potentially decrease the electricity bills of cloud providers. Cloud providers can improve their contribution and reduce their energy cost by collaboratively managing their workload. Through cooperation in the form of cloud federations, providers can spatially migrate their workload to better utilize the benefits provided by demand response schemes over multiple locations. To this end, this work considers an interaction system between the independent cloud providers and the corresponding smart grid utilities in the context of a demand response program. Leveraging the cooperative game theory, this paper presents a federation formation among the cloud providers in the presence of a location-dependent demand response program. A distributed algorithm that is coupled with an optimal workload allocation problem is applied. The effect of the federation’s formation on the clouds’ profits and on the smart grid performance is analyzed through simulation. Simulation results show that cooperation increases the clouds’ profits as well as the smart grid performance compared to the noncooperative case.
Correct-by-Construction Parallelization of Hard Real-Time Avionics Applications on Off-the-Shelf Predictable Hardware

We present the first end-to-end modeling and compilation flow to parallelize hard real-time control applications while fully guaranteeing the respect of real-time requirements on off-the-shelf hardware. It scales to thousands of dataflow nodes and has been validated on two production avionics applications. Unlike classical optimizing compilation, it takes as input non-functional requirements (real time, resource limits). To enforce these requirements, the compiler follows a static resource allocation strategy, from coarse-grain tasks communicating over an interconnection network all the way to individual variables and memory accesses. It controls timing interferences resulting from mapping decisions in a precise, safe, and scalable way.
Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks

Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time.
Secure Online Data Sharing in Cloud by Private Transmit Cryptosystem Using Aggregate Keys

Cloud has become a crucial part of our day to day life because of its easy, effortless and straightforward nature of data storing and sharing. One of the important concerns for many users is data storing, we mustn’t forget about data sharing. A convenient way of online data sharing is to look at its pros for simple access while preserving security are cons for any user. Thus, a better way of ensuring user’s data is to implement data integrity with the KAC scheme. This scheme provides an efficient sharing method of decrypting multiple sets of data with the single key. It’s one among many ways of quick and effective data retrieval in case of data loss or data alteration on the cloud. It also uses the broadcast algorithm to distribute data for a specific set of users. This scheme uses basic HMAC, one of the secure hash functions for the stability of data integrity. Therefore provides a protected environment where a user can share the bulk of data through integrity. Another advantage of using this scheme could reduce the burden of computation over the cloud.
Mutual Coupling Reduction of a Wideband Circularly Polarized Microstrip MIMO Antenna

Mutual coupling (MC) between elements in an antenna array could significantly affect antenna performance, but this effect has rarely been reported for circularly polarized (CP) antennas. Therefore, this paper investigates a wideband CP multiple-input-multiple-output (MIMO) antenna operating between 1.8 and 2.6 GHz. A line patch was introduced between closely placed radiating elements (<inline-formula> <tex-math notation="LaTeX">$0.3\lambda $ </tex-math></inline-formula>) to produce a high isolation between elements in the proposed antenna. The designed MIMO antenna has a wide impedance bandwidth, a wide axial ratio (AR) bandwidth, a very low MC (<inline-formula> <tex-math notation="LaTeX">$\text{S}_{{21}} < -25$ </tex-math></inline-formula> dB), a low envelop correlation coefficient (ECC < 0.01), a high diversity gain (DG ~ 10 dB), and a realized gain of above 2 dB over the entire frequencies. The prototype of the proposed antenna geometry was fabricated and measured. A good agreement between the simulated and measured results was observed.
Outage and ASE Analyses for Power Controlled D2D Communication

In fifth-generation technology, device-to-device (D2D) communication underlaid with a cellular network provides a feasible solution for proximity-based wireless applications and services. D2D communication allows a direct connection between two devices without traversing through the base station. It can potentially reduce the data traffic in the cellular networks with improved spectral and energy efficiency. However, interference management is a primary concern when D2D links utilize the same resources occupied by cellular users. In this paper, we propose a location-based power control approach to mitigate the severe interference components for underlaying D2D communication cellular network for multicell scenario. Moreover, outage analysis for the cellular user as well as D2D user is carried out by using the tools from stochastic geometry. Outage performance is further improved by employing a cooperation approach that also enables D2D user’s power saving. Further, area spectral efficiency (ASE) analysis is performed using the proposed power control and cooperation approach for the overall system model. We obtain the optimal D2D user density to maximize the ASE. Finally, the numerical results are presented to validate the analytic observations of the proposed framework.
Dahlin-Based Fast and Robust Current Control of a PMSM in Case of Low Carrier Ratio

The low ratio of sampling frequency to electrical frequency (carrier ratio) is a challenging issue for applications, such as high-speed or high-power drives. In fact, high-speed drives have high fundamental frequency and high-power drives have low switching (sampling) frequency, leading to the same control challenges of having high dynamic and stability with a limited number of control actions in one period. Parameters mismatch occurs due to the converter non-linearities, deadtimes, temperature change, and saturation; hence, providing robust control is a key challenge. This paper starts reviewing the problems of controlling machines with a few switching instances per fundamental period and an analysis of the state-of-the-art current control strategies for permanent magnet synchronous motors (PMSMs). Three different control schemes are analyzed. The discrete-time PI current controller is providing good robustness, while the dynamic performance may not be sufficient for some applications. Second, the deadbeat control offers very fast dynamic performance but is weak under parameter mismatch. The novel application of the Dahlin controller offers a tradeoff between performance and stability. The control algorithms are evaluated based on the stability, robustness, and dynamic and steady-state performances. Their performance and sensitivity to parameter variation are analyzed through simulations, and the experimental measurements are proving the results.
A NEW SLIDING MODE CONTROLLER IMPLEMENTATION ON AN AUTONOMOUS QUADCOPTER SYSTEM

This paper introduces Sliding Mode controller, a non-linear controller, for the implementation of an autonomous quadrotor platform, a non-linear system. The Sliding Mode controller was applied to a PixHawk Flight Controller using the Ardupilot firmware. The simulation testing using SITL shows the effectiveness of the controller before flight. The results imply the improvement when using Sliding Mode Control in comparison to PID controller. The results show that there is a reduction in attitude error when using Sliding Mode Control in comparison with PID control in all simulation and actual hardware results. The robustness of Sliding Mode Control was also tested by adding parameter uncertainties and disturbances to the system. In this study, the root-mean-square error obtained in the Sliding Mode Control is 1.546580%, 0.634243%, and 13.466256% for the roll, pitch, and yaw movements respectively, and the root-mean-square error obtained in the PID control is 2.588324%, 4.553838%, and 18.860183% for the roll, pitch, and yaw movements respectively. This shows that the quadrotor using Sliding Mode Control is less prone to attitude errors.
Weber Local Descriptors With Variable Curvature Gabor Filter for Finger Vein Recognition

Since captured finger vein images are usually of low quality, it is challenging to extract reliable finger vein features directly from the original finger vein images. Most current methods utilize texture change information and rich line features to extract features from finger vein images while neglecting the curvature of the finger veins. In this paper, a Weber local descriptor (WLD) with variable curvature Gabor filters is proposed for finger vein recognition. First, the differential excitation operator in the original WLD is improved by adding directional information, thereby allowing the local texture changes in an image to be better characterized and enhancing the differences between heterogeneous finger veins. Then, variable curvature Gabor filters are introduced to extract finger vein features that can simultaneously reflect the directional information and the curvature of the finger veins. In fact, two response values from the proposed Gabor filters are employed as features for each pixel; this approach is equivalent to defining intervals for the line features, rather than single values, and makes the results more robust to the rotation. The extensive experiments on the SDUMLA-FV and PolyU databases demonstrate that the proposed method can effectively improve the performance of finger vein recognition and show good robustness to translation, rotation, and illumination.
Demo Abstract: Monitoring Virtualized Telco Services for Multisided Platforms with SQL-like Query

The Telco ecosystem transformation towards cloud-native network services enables constructing an integrative platform business model in the form of a Multi-Sided Platform (MSP) leveraging microservice-based Virtualized Network Function architecture. In particular, MSP based architectures enable a multi-organizational ecosystem with increased automation possibilities for carrier-grade services creation and operations. We present a microservice-based monitoring system for virtualized Telco services based on OpenAirInterface (OAI) with an SQL-like query manager for metrics. We demonstrate two monitoring scenarios: (1) Average receiving (rx) PDU in bytes at MAC layer from the targeted user equipment (UE). (2) Finding the UE who consumes the most Physical Resource Blocks (PRB) within a specific time interval for the uplink and downlink transmission.
Artifacts Removal in Melanoma Using Various Preprocessing Filters

Preprocessing plays an important role for artifacts removal and quality improvement. In this paper, noise and hair removal techniques for melanoma in dermoscopic images is proposed. Here the performance of four filters for noise removal namely Wiener, Mean, Median, and Gaussian Filters are studied. Of these the performance of Gaussian filter is proved to be best. In addition Morphological Filter is used for hair removal. The noise and hair removal filtering processes help to enhance the quality of the image and thus aid to improve the segmentation results. The performance of the preprocessing filters is compared using quantifying parameters like MSE, PSNR, and SSIM.
Correlation Feature Selection (CFS) and Probabilistic Neural Network (PNN) for Diabetes Disease Prediction

The healthcare sector is a broad area with the abundance of patient information, which creates enormously large records day by day. Though the scientific industry is rich in information but it is poor in knowledge. Diabetics are considered as a primary health issue of the world. As per the WHO 2014 survey According to WHO 2014 report, over 422 million people are affected from the diabetics globally. In the minimization of massive investigations implied on the patients, the data mining uses many mechanisms and strategies to diagnose the diabetic problem. The main objective of this proposal is to introduce assemble Data Mining based Diabetes Disease Prediction System which provides a detailed analysis of diabetics using the database of diabetics patient. The formulated work comprises of two stages such as feature selection ad prediction methods which are made known to maximize the outputs of diabetes disease prediction. Initially Correlation Feature Selection (CFS) is formulated to identify the salient features for the diabetic repository. The identified features are fed into the classifier named Probabilistic Neural Network (PNN) classifier. As the diabetic of the patient is classified using PNN meanwhile the accuracy can be fine – tuned when using the identified features. Depending on the category of data, the diabetic information is gathered from the learning repository. The outputs are correlated with the current algorithms namely Back Propagation Neural Network (BPNN), Multilayer Perceptron, Neural Network (MLPNN) were used to fetch the outputs.
Research on Index Mechanism of HBase Based on Coprocessor for Sensor Data

In order to provide effective management of big data, almost two hundred different NoSQL stores have been developed, among which HBase is one of the best known. When performing data queries, the native HBase supports primary key indexes well. For non-primary key data, only the full table scan can be used, which greatly reduces the multi-condition query speed of HBase. Some additional indexing techniques have been presented to support querying on non-key indexing for HBase. For large-scale sensor data, this research field still requires effective design paradigm, in-depth experimentations and practical implementations. Therefore, we propose and implement a secondary memory index mechanism of HBase based on coprocessor for sensor data. The index mechanism can be automatically updated according to the change of the HBase table. Meanwhile, the index mechanism is persisted and maintained in memory, which can greatly improve the retrieval speed of the index data. By using real sensor datasets with different distribution patterns, experimental results show that the condition retrieval speed of the mechanism is greatly improved compared with the original HBase. In addition, compared with the secondary index mechanism based on Solr and Hibase, the performance of our proposed solution is also improved.
Using Topic Modeling to Extract Pre-Service Teachers’ Understandings of Computational Thinking From Their Coding Reflections

Contribution: This paper employs the automatic scoring of short essays as a novel way to determine pre-service teachers’ knowledge of and attitudes toward computational thinking (CT) from their written reflections. Implications about designing CT courses for pre-service teachers are discussed. Background: CT is an essential 21st-century competency that supports the development of problem-solving skills. Inspired by computing science problem-solving practices, CT should transcend disciplines, but few universities or colleges include CT courses or CT content in their core courses. It is also difficult to know what pre-service teachers think about CT and their role in promoting it. Research Questions: Do pre-service teachers’ coding reflections reveal any important information about their knowledge of, skills in, and attitudes toward CT? Methodology: Traditional qualitative techniques based on human raters are impractical in analyzing hundreds of essays. Topic modeling, an unsupervised machine learning modeling technique, was employed to extract topical features from participants’ reflections. In one section of an undergraduate Introduction to Educational Technology course offered at a large university in Western Canada, n = 139 pre-service teachers wrote a short reflection on their experience following a 20 h Accelerated Intro to Computer Science Code.org course. Topics were identified by analyzing contextual trends in participants’ written reflections. Findings: Results showed that pre-service teachers’ reflections included CT concepts, practices, and perspectives. Specifically, participants connected the coding activity to prior knowledge and experiences.
Neural Networks with Matlab

Matlab has a suite of programs designed to build neural networks (the Neural Networks Toolbox). Additionally , there are demonstrations available through Matlab's help feature. In this lab, we will only work with three layer " feed forward " nets (these are the nets we discussed in class). There are three steps to using neural networks: Design, Training, and Testing (the same for modeling in general! ). – Define the number of nodes in each of the three layers (input, hidden, output). – Define what σ is for each of the nodes (usually all nodes in a layer will all use the same σ). These are called the transfer functions. – Tell Matlab what optimization (or training) routine to use. Generally, we will use either traingdx, which is gradient descent, or trainlm (for Levenburg-Marquardt, which is a combination of gradient descent and Newton's Method). • Training: Once the network has been designed, we " train " the network (by optimizing the error function). This process determines the " best " set of weights and biases for our data set. • Testing: We need to test our network to see if it has found a good balance between memorization (accuracy) and generalization.