Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Grid Network With D2D Communication and Coherent PLC: Error Analysis

In this paper, we consider a hybrid smart grid communication network consisting of power line communication (PLC) at the transmitter side and the device-to-device (D2D) cellular communication on the receiver side which provides last mile connectivity. The PLC channel is assumed to experience Rayleigh fading and is corrupted by impulsive noise, and the D2D communication being a short range communication system, is assumed to experience Nakagami-$m$ fading with additive Gaussian noise. We propose a maximal-ratio combining (MRC) receiver for the considered hybrid smart grid communication system employing binary phase shift keying (BPSK) modulation at the transmitter and derive a closed-form symbol error probability (SEP) expression for the proposed receiver. The interference between the D2D links is assumed to be negligible for the tractability of the error probability analysis. Numerical results demonstrate the optimality of operating the D2D links at lower signal-to-noise ratio (SNR) values to overcome the effect of the impulsive PLC channel by utilizing diversity branches.
Stuck Pipe Incidents Prediction Based On Data Analysis

The process of oil and gas drilling is a complicated and uncertain field practice. Moreover, uncertainty and fuzziness of formation is also a threat to drilling safety. Due to inaccurate cognition of objective environment and decision-making errors of subjective consciousness, underground complex conditions and serious accidents are caused. Stuck pipe is one kind of downhole accident with high probability, complex handling difficulty and cost of manpower and material resources in drilling engineering. To deal with the stuck pipe accident, workers on the well-site are often required to judge the geological conditions of formation drilled, to adjust drilling parameters, drilling fluid performance and other methods to avoid and remove. Without fixed reference basis and determination methods, it is difficult to provide early warnings. Through the collection, while drilling in formation, such as geological lithology, designed well structure, real-time drilling fluid performance, rock physical properties of backflow cuttings, and drilling engineering parameter et,al. Artificial neural network learning model is used to predict the risk probability of stuck pipe. In this paper, face to formation collapse stuck pipe, after collected and analyzed the physical and chemical properties of backflow cuttings, drilling fluid system performance, well-site drilling data and other well-site drilling parameters according to the influencing factors of collapse and stuck pipe, combined with computer intelligent technology, and predicted collapse stuck pipe risk probability in advance. Through well-field data collected, the occurrence of stuck pipe is predicted successfully. Through accumulated database, the accuracy of prediction model is constantly improved and modified. Early warning mechanism, timely adjustment of drilling fluid additives, maintenance of drilling fluid system performance and improvement of drilling parameters for different formations, increase drilling speed and avoid unnecessary waiting time
Broadband Receiver Electronic Circuits for Fiber-Optical Communication Systems

The exponential growth of internet traffic drives datacenters to constantly improve their capacity. As the copper based network infrastructure is being replaced by fiber-optical interconnects, new industrial standards for higher datarates are required. Several research and industrial organizations are aiming towards 400 Gb Ethernet and beyond, which brings new challenges to the field of high-speed broadband electronic circuit design. Replacing OOK with higher M-ary modulation formats and using higher datarates increases network capacity but at the cost of power. With datacenters rapidly becoming significant energy consumers on the global scale, the energy efficiency of the optical interconnect transceivers takes a primary role in the development of novel systems. There are several additional challenges unique in the design of a broadband shortreach fiber-optical receiver system. The sensitivity of the receiver depends on the noise performance of the PD and the electronics. The overall system noise must be optimized for the specific application, modulation scheme, PD and VCSEL characteristics. The topology of the transimpedance amplifier affects the noise and frequency response of the PD, so the system must be optimized as a whole. Most state-of-the-art receivers are built on high-end semiconductor SiGe and InP technologies. However, there are still several design decisions to be made in order to get low noise, high energy efficiency and adequate bandwidth. In order to overcome the frequency limitations of the optoelectronic components, bandwidth enhancement and channel equalization techniques are used. In this work several different blocks of a receiver system are designed and characterized. A broadband, 50 GHz bandwidth CB-based TIA and a tunable gain equalizer are designed in a 130 nm SiGe BiCMOS process. An ultra-broadband traveling wave amplifier is presented, based on a 250 nm InP DHBT technology demonstrating a 207 GHz bandwidth. Two TIA front-end topologies with 133 GHz bandwidth, a CB and a CE with shunt-shunt feedback, based on a 130 nm InP DHBT technology are designed and compared.
Combining Deep Learning and Combinatorial Optimisation to Generate Four-Part Chorales

This thesis describes the implementation of a functional prototype consisting of a recurrent neural network and a branch-and-bound optimiser which together generate original music compliant to the rigid rule-set of the four-part chorale in the style of Johann Sebastian Bach. The exact nature of music generated by neural networks is troublesome to predict, and by extension to manipulate in any meaningful way between input and output. How well the results adhere to the more abstract aspects of music is hard to control, and often display little to no musical structure or significant creativity. Also, there are few avenues for interactivity and active intervention in the usage of such a "black-box" system. The paper explores means to mitigate this externally, and shows that combinatorial optimisation strategies can be employed post-generation to enforce musical constraints and adjust the output in such a way that it adheres to the conventions and expectations of western music theory. The constrained results display significant improvement in functional structure, melodic and harmonic progression and aesthetics compared to those generated by the neural network alone. Although the prototype is not capable of locating optimal transformations from the neural network output within reasonable time, a method for finding low cost chord combinations with valid structure can be used to generate results that demonstrate the applications of the system.
Interconnect-Based PUF With Signature Uniqueness Enhancement

Physical unclonable function (PUF) is an important security primitive, which generates unique signatures as fingerprints for each chip. This article first presents a novel interconnect-based PUF (iPUF). The proposed iPUF utilizes the manufacturing process variability of interconnect lines to introduce crosstalk variations for generating PUF signatures. By leveraging the variations of passive interconnects, iPUF minimizes the usage of active CMOS components, providing an increased resiliency against environmental variations and aging. Initiated by a linear feedback shift register (LFSR), iPUF sequentially generates 1-bit signature at each clock cycle, making it more efficient compared with ring-oscillator PUF. Second, two schemes for signature uniqueness enhancement of sequential PUFs are proposed. The self-masking scheme windows the sequential signature with an $m$ -bit mask trained by the PUF’s own initial sequential signature. Meanwhile, the bit-filtering scheme screens the randomness of each bit within the sequential signature by exploiting several sub-iPUFs and selects the bits with high randomness. To verify the performance of iPUF, Monte Carlo simulations of 500 samples, with variations following industrial data, are conducted in different operating corners. The uniqueness of the given sample set approaches 48.63% with a 10-bit mask. With ±10% supply voltage, $0~^{\circ }{C}$ –100 °C temperature variations, as well as one year of unaccelerated aging, iPUF’s reliability values, are as high as 96.09%, 99.06%, and 99.63%, respectively. For verification, 50 dies of iPUF chips are manufactured with a 55-nm technology node. Silicon results demonstrate that iPUF generates 1024-bit signatures with satisfied uniqueness (48.03%) while exhibiting good reliability (90.07%) under 120-mV voltage variations. Finally, iPUF’s robustness against various attacks is also proven.
A Survey of Internet of Things (IoT) for Geohazard Prevention: Applications, Technologies, and Challenges

Geologic hazards (geohazards) are naturally occurring or human-activity-induced geologic conditions capable of causing damage or loss of property and/or life. geohazards, such as landslides, surface subsidence, and earthquakes, can seriously affect and threaten life, property, or public safety. geohazards prevention is the application of geologic engineering principles and existing and emerging technologies to reduce, minimize, or prevent the effects of various geologic hazards. Monitoring and early warning are the most common strategies for geohazards prevention. With the development of the Internet of Things (IoT), an emerging new idea is to apply IoT technology to enhance the accuracy and efficiency of monitoring and early warning systems for geohazards prevention. This article aims to present a comprehensive survey of relevant research and technological developments of the IoT applied in geohazards prevention. It first surveys the applications of the IoT in the monitoring and early warning of seven types of common geohazards, including landslides, debris flow, rockfall, surface subsidence, surface collapse, surface cracks, and earthquakes, then investigates the key technologies in geohazards prevention when utilizing the IoT, and finally summarizes the challenges in IoT-based monitoring and early warning systems for geohazards prevention. Moreover, this article also highlights the future directions for employing the IoT for geohazards prevention.
Research on Fast Kernel Subspace Face Recognition Based on Deep Belief Network

Face recognition usually uses different features as input signals. There are many conventional techniques being used and these include technical and fundamental analysis. In this paper, the sample data is mapped from low-dimensional space to high-dimensional space by the kernel method, which makes the classification algorithm have the ability to deal with non-linear data and can solve the small sample problem. At the same time, deep belief network is used as feature transformation and classification to mine feature information of high-dimensional face data. The experimental results show that the optimal recognition rate of the proposed algorithm in a specific face database is up to 96%.
The Computational Advantages of Intrinsic Plasticity in Neural Networks

In this work, I study the relationship between a local, intrinsic update mechanism and a synaptic, error-based learning mechanism in ANNs. I present a local intrinsic rule that I developed, dubbed IP, that was inspired by the Infomax rule. Like Infomax, this IP rule works by controlling the gain and bias of a neuron to regulate its rate of fire. I discuss the biological plausibility of this rule and compare it to batch normalisation. This work demonstrates that local information maximisation can work in conjunction with synaptic learning rules to improve learning. I show that this IP rule makes deep networks more robust to increases in synaptic learning rates, and that it increases the average value for the slope of the activation functions. I also compare IP to batch normalisation and Infomax, whose family of solutions were shown to be the same. In addition, an alternative rule is developed that has many of the same properties as IP, but instead uses a weighted moving average to compute the desired values for the neuronal gain and bias rather than the Adamised update rules used by IP. This rule, dubbed WD, demonstrates universally superior performance when compared to both IP and standard networks. In particular, it shows faster learning and an increased robustness to increases in synaptic learning. The gradients of the activation function are compared to those in standard networks, and the WD method shows drastically larger gradients on average, suggesting that this intrinsic, information-theoretic rule solves the vanishing gradient problem. The WD method also outperforms Infomax and a weighted moving average version of batch normalisation. Supplementary analysis is done to reinforce the relationship between intrinsic plasticity and batch normalisation. Specifically, the IP method centers its activation over the median
Image Denoising Algorithm based on Improved NCSR Model

The Nonlocally Centralized Sparse Representation (NCSR) algorithm aims to reduce sparse coding noise and utilizes the Nonlocal Self-similarity of the image to improve the effectiveness and accuracy of the image denoising algorithm, which has achieved good denoising effect. However, in the local dictionary learning and sparse coefficient estimation, the algorithm directly uses the Euclidean distance of the noise image to measure the similarity, which will have a certain impact on the final denoising effect. Aiming at this deficiency, it is proposed to use the Gaussian Mixed Model (GMM) to train nonlocal self-similar image patch of the external clean image in the local dictionary learning, and use the learned GMM to guide the internal image clustering; when performing the sparse coefficient estimation, the structural similarity index(SSIM) is introduced into the Euclidean distance and the improved Euclidean distance is introduced into the weight calculation. The experimental results show that the improved algorithm proposed in this paper has improved the PSNR and the visual effect of the image.
User-Centered Gamification for E-Learning Systems: A Quantitative and Qualitative Analysis of its Application

Gamification applies game elements in non-game contexts to improve users’ experience. One of the contexts that have mostly taken advantage of gamification is the educational one. However, the students’ experience is unique, since it varies according to their profile. Therefore, the individualities of each student must be considered to improve the students’ experience. This paper aims to explore the gamification properties and analyze the results of the user-centered application. For this, we proposed a framework focused on the user-centered gamification in the educational context, taking into account personal, functional, psychological, temporal, playful, implementable and evaluative properties. After applying the framework in the e-learning system, the controlled experiment with 139 students revealed an increase in students’ interaction, engagement and satisfaction. Thus, our main contributions are the improvement of the students’ experience with the user-centered gamification and the definition of a framework to assist in its application. This paper proposes a user-centered gamification framework for educational context It has been applied in an e-learning system and a controlled experiment was conducted A total of 139 students enrolled for an online course, and half of them used the gamified version Results indicate an increase in the students’ interaction, engagement and satisfaction
SMART AND SECURE IOT AND AI INTEGRATİON FRAMEWORK FOR HOSPITAL ENVİRONMENT

This paper presents a smart and secure framework for hospital environment using Internet of Things (IoT) and Artificial Intelligence (AI). This system overcomes the drawbacks of the current system of hospital information such as inflexible modes of networking, fixed point of information and so on. Factors such as basic network environment construction, application framework, logic structure, and data security are considered in detail. This work also helps in overcoming the existing problems of treatment, diagnosis, patient monitoring, and maintenance of hospital records in electronic format effectively. It presents a profound positive impact on the current methods followed in the hospital environment.
Design of QAM-FBMC Waveforms Considering MMSE Receiver

Due to its high spectral confinement characteristics and spectral efficiency, QAM-FBMC is considered a candidate waveform to replace CP-OFDM. QAM-FBMC has inevitable non-orthogonality both in time and frequency, and the system and filter must be well-designed to minimize the interferences. However, existing QAM-FBMC studies utilize a matched filter as the receiver filter, which is not suitable for a non-orthogonal system. Therefore, in this letter, we design the prototype filters considering the MMSE criterion, and propose a system providing the highest SINR in QAM-FBMC which cannot avoid non-orthogonality. In addition, we confirm that the proposed filters show best performance at target SNR than the reference filters.
SQL-G: Efficient Graph Analytics by SQL

Querying graphs and conducting graph analytics become important in data processing since many real applications are dealing with massive graphs, such as online social networks, Semantic Web, knowledge graphs, etc. Over the years, many distributed graph processing systems have been developed to support graph analytics using various programming models, and many graph querying languages have been proposed. A natural question that arises is how to integrate graph data and traditional non-graph data in a distributed system for users to conduct analytics. There are two issues. One issue is related to expressiveness on how to specify graph analytics as well as data analytics by a querying language. The other issue is related to efficiency on how to process analytics in a distributed system. For the first issue, <italic>SQL</italic> is a best candidate, since <italic>SQL</italic> is a well-accepted language for data processing. We concentrate on <italic>SQL</italic> for graph analytics. Our early work shows that graph analytics can be supported by <italic>SQL</italic> in a way from “semiring + while” to “relational algebra + while” via the enhanced recursive <italic>SQL</italic> queries. In this article, we focus on the second issue on how to process such enhanced recursive <italic>SQL</italic> queries based on the <italic>GAS</italic> (<italic>Gather</italic>-<italic>Apply</italic>-<italic>Scatter</italic>) model under which efficient graph processing systems can be developed. To demonstrate the efficiency, we implemented a system by tightly coupling <italic>Spark</italic> <italic>SQL</italic> and <italic>GraphX</italic> on <italic>Spark</italic> which is one of the most popular in-memory data-flow processing platforms. First, we enhance <italic>Spark</italic> <italic>SQL</italic> by adding the capability of supporting the enhanced recursive <italic>SQL</italic> queries for graph analytics. In this regard, graph analytics can be processed using a distributed <italic>SQL</italic> engine alone. Second, we further propose new transformation rules to optimize/translate the operations for recursive <italic>SQL</italic> queries to the operations by <italic>GraphX</italic>. In this regard, graph analytics by <italic>SQL</italic> can be processed in a similar way as done by a distributed graph processing system using the APIs provided by the system. We conduct extensive performance studies to test graph analytics using large real graphs. We show that our approach can achieve similar or even higher efficiency, in comparison to the built-in graph algorithms in the existing graph processing systems.
A Neural Network Based Motor Bearing Fault Diagnosis Algorithm and its Implementation on Programmable Logic Controller

—This research aims to test the feasibility of Programmable Logic Controller implementation of an Artificial Neural Network based bearing fault diagnosis using vibration datasets. The main drawback of using a Programmable Logic Controller along with an Artificial Neural Network is that it does not support the parallel nature of neural networks. This drawback is not significant for relatively small applications like bearing diagnosis that involve very short execution time. In this paper, a three layer multilayer perceptron backpropagation neural network is trained using Levenberg-Marquardt training algorithm with vibration dataset consisting of four bearing status classes: normal, outer race way fault, inner race way fault and rolling element (ball) fault. Time-frequency domain and time domain input features were considered in this research. Both approaches have performed well during simulation phase. But the time-frequency feature extraction approach was observed to take too long scan cycle time to be implemented in real-time. This is due to the computationally intensive nature of Fast Fourier Transform algorithm involved during feature extraction. The time domain approach is proved to be feasible for Programmable Logic Controller implementation. The time domain input features used for neural network training were root mean square, variance, kurtosis and negative log likelihood values. The average performance obtained during simulation with 10-fold cross validation performance estimator was an error of 7.9 x10 -4 . The performance tests of Programmable Logic Controller implementation resulted in 100% bearing fault detection rate.
Patient Specific Coronary Stent Designing and its Computational Analysis using Ansys

The optimal patient-specific stent technique for minimizing/eliminating coronary artery blockage is an ambitious field of medical science and technology. Patient specific stents are the type of stents which are designed according to the dimensions of one particular individual, hence they vary from person to person. With the amelioration of computational simulations, it is quite useful as we can produce or manufacture a stent that is of a specific size, strength and more tenacious to suit the patient’s specific requirement, not only that but it is also a cost-effective method. It is very compatible and is a time tested structure with hemodynamic results in order to suit patients’s requirements based on their anatomical structure. In this study the coronary artery of a patient was constructed using Mimics and 3-Matic software (Materialise,NV), with reference to patient’s MRI angiography, subsequently with the help of the coronary artery measurements we formulated patient-specific coronary artery stents using Autocad-2016 and we used Finite Element anlysis to find the structural strength of the designed patient-specific coronary artery stent. With the help of ANSYS we constructed a few sample stents using different biocompatible materials and we used Finite Element Analysis method to find the most compatible/adaptable biomaterial, simultaneously we carried out the process of assessing the mechanical properties which are associated with restenosis. We created a simulation using Computational Fluid Dynamics (CFD) and we tested the accuracy of the stent in an artery with blood flow. The pre-operative computational simulations results are helpful in diminishing the chances of stent failure without performing actual Angioplasty which is both cost-effective as well as beneficial to reduce post-operative trauma faced by cardiac surgery patients.