Miracle-dz/UniFAR-FTUs huggingface dataset:
WiFi RSSI preprocessing library for Android

Fingerprinting is one of the most popular indoor positioning approach, which enables the usage of heuristic and data mining methods for position estimation. Many fingerprinting-based indoor positioning algorithms use the WiFi Received Signal Strength Indication data and Horus System showed that client side preprocessing could improve the stability, and accuracy of the positioning system. Although filtering techniques are well-studied and widely applied in data sciences, their applications in the field of indoor positioning is limited. Providing a general and flexible filtering library would facilitate the development of client applications for indoor positioning systems. This paper presents a Java library that allows the application of filtering methods on Android platform. Flexibility and extendability were concerned during the design of the library, which currently contains three different filtering methods, namely Horus, Static Time Window and Dynamic Time Window filters. An Android applications was developed to demonstrate the applicability of the filtering library in real life scenario.
Measuring the accuracy and learnability of tools in the struggle against misinformation in social media applications

Misinformation became pervasive on social media applications. The companies behind this kind of system have launched tools to avoid the problem, but some issues regarding the user behavior and proper software quality still need a forceful approach. First attempts to mitigate misinformation did not take into account user behavior and softwares requirements like learnability and accuracy, furthermore the characteristics of actors and artifacts from social media applications ecosystem has not been explored. This research aims to evaluate the usability of available tools made to combat the spread of misinformation and to verify the interrelationship between actors and artifacts from social media applications ecosystem for suggesting improvements on development of these tools.
Underwater image enhancement with encoding-decoding deep CNN networks

Image enhancement is an important topic in the field of machine vision and image processing. Complex underwater environment poses great challenge for machine vision due to the turbid water medium. Serious scattering and absorption make the underwater images have complicated noise distribution. This paper proposes an underwater image enhancement model based on Encoding-Decoding deep CNN networks. We employ the convolution layers as encoding while deconvolution layers as decoding. The model achieves the image enhancement in an end-to-end adaptive way rather than considering the physical environment. We provide several comparison experiments with different datasets. Experiments show that it outperforms state-of-the art underwater image restoration methods in underwater image defogging, denoising and color enhancement. Finally, the model is employed to handle the underwater images with the different levels of noise and shows good performance.
Hybrid Procedural Content Generation : A Proposal

Procedural content generation in games tends to target content that is abstract, dry and devoid of connection with the game’s meaning. This paper proposes merging user-driven content generation approaches with procedural content generation to create a new paradigm which we call Hybrid Procedural Content Generation. By replacing aspects of existing procedural generation techniques with humans, we can give rise to new kinds of game experiences.
High dynamic tone mapping algorithm based on wavelet domain image fusion

High dynamic range(HDR) images are widely used in our daily, but its display device is expensive, the displayed images also have higher quality, therefore the display of HDR image on traditional equipment is still a hot issue. In this paper, a tone mapping algorithm based on wavelet domain image fusion is proposed. First, three basic LDR images are obtained by three classical algorithms for HDR images of the same scene. Secondly, the three low dynamic range(LDR) images are separated by wavelet decomposition. Then, the low-frequency components and the high frequency components are seperatively weighted by different rules. Finally, the fusion image is obtained through the wavelet inverse transform, and the fused image is further enhanced by the guide image filter to obtain the final LDR image. The experimental results show that the proposed algorithm can keep the details of the brightest and darkest regions well, and the objective quality of images produced by the proposed algorithm is also better than other algorithms.
Finite Control Set Model Predictive Control to Guarantee Stability and Robustness for Surface-Mounted PM Synchronous Motors

This paper proposes a finite control set model predictive control (FCS-MPC) to guarantee the stability and robustness for surface-mounted permanent magnet synchronous motor (SPMSM) drives. Continuous-input-based control laws are first developed from a control-Lyapunov function in order to both stabilize the closed-loop system via feedback control laws and ensure the robustness via online adaptive laws. Because the asymptotic stability of the proposed control method is guaranteed by at least one discrete switching-state, the continuous-input-based control laws are converted into relevant constraints of the FCS-MPC optimization problem. To validate the advantages of the proposed FCS-MPC, comparative studies with the conventional FCS-MPC and space vector modulation based adaptive control are conducted on a prototype SPMSM testbed with a TI TMS320F28335 DSP. The effectiveness of the proposed FCS-MPC is verified by the comparative schemes with/without the additional constraints. Superiority of the proposed schemes such as zero steady-state error, fast speed-tracking capability, well-regulated stator currents, and low average switching frequency are experimentally validated under the step-changes of load torque and speed reference.
Study on optimized operation of distribution network when considering uncertainty

The reconstruction and the reactive power optimization of distribution network play an important role in optimizing the operation of distribution network, which play an important role in improving the quality of power supply and reducing network loss. Based on considering the uncertainties of distributed power supply, this paper establishes a multi-objective integrated optimization model of distribution network, optimizes multiple important measurement indexes of distribution networks, and uses the 69-node test system to verify the model at the same time. The result shows that reasonable distributed generation parallel and optimization can not only accommodate the new energy source but also improve the indexes of the system obviously, which verifies the feasibility and correctness of the proposed model.
Microbeamforming With Error Compensation

One of the main issues in the development of 2-D arrays is the high system complexity due to the requirement for a large number of elements. The 2-D array systems suffer from high system complexity. The microbeamforming (MBF) method has been proposed to reduce the system complexity; however, distortions of MBF approach such as focusing errors of postbeamforming process result in broadening the main lobe and increasing the sidelobe and grating-lobe levels, which together degrade the image quality. As the presteered radio frequency (RF) data can be estimated from MBF data at the digital back end, better postbeamforming can be performed and higher image quality can be achieved. In this paper, a compensation approach is proposed to estimate the presteered RF data from MBF data by utilizing additional headers and compensation factors. The compensation factors and headers are estimated at the probe front end and then applied to the back-end digital system to reconstruct the required presteered RF data. As the absolute values of the MBF errors are modeled as a single-sided Gaussian distribution, the theoretical mean square error with the proposed method is approximately 2.75 times lower than that of its counterpart without compensation; this implies better reconstruction of presteered RF data can be achieved with the proposed method. The simulation results showed that the main lobe is improved, and the sidelobe and grating-lobe levels in both the lateral and elevation directions were improved by 11.73 and 19.12 dB, respectively, while the peak signal-to-noise ratios improved by 6–9 dB with the proposed method. The contrast-to-noise ratios also are enhanced by 0.5 dB when using the proposed method. Analog circuits are presented to demonstrate that this novel compensation method can be realized in practice. The reduction of cables and analog-to-digital converters are about seven-fold compared to fully sampled 2-D array systems as 4 by 4 channels are grouped for the proposed method as well.
Personal Visualization for Learning

A BSTRACT Learners have personal data, such as grades, feedback and statistics on how they fair or compare with the class. But, data focusing on their personal learning is lacking, as it does not get updated regularly (being updated at the end of a taught session) and the displayed information is generally a single grade. Consequently, it is difﬁcult for students to use this information to adapt their behavior, and help them on their learning journey. Yet, there is a rich set of data that could be captured and help students learn better. What is required is dynamically, regularly updated personal data, that is displayed to students in a timely way. Such ‘personal data’ can be presented to the student through ‘personal visualizations’ that engender ‘personal learning’. In this paper we discuss our journey into developing learning systems and our resulting experience with learners. We present a vision, to integrate new technologies and visualization solutions, in order to encourage and develop personal learning that employs the visualization of personal learning data.
Design to Reduce Rotor Losses in Fault-Tolerant Permanent-Magnet Machines

This paper designs a new rotor topology of a fault-tolerant permanent-magnet (FTPM) machine with fractional-slot concentrated winding in order to reduce rotor losses, demagnetization risk, and temperature. The key is the appropriate flux barriers, which are designed in rotor yoke. First, the structure feature and losses of rotor are analyzed. An analytical expression is derived by the simplified permeance model to evaluate the armature reaction magnetic field of the newly designed FTPM machine. Second, both the newly designed and the original machines are optimized for a given set of specifications, and their performances are comparatively analyzed by using finite-element method. Finally, some experiments on the prototypes of both FTPM machines are carried out for validation.
Wireless indoor localisation for autonomous service robot with a single emitter

The ability to localise in an unknown environment allows for faster and less cost-prohibitive deployment of autonomous vehicles. In this research, we aim to demonstrate the possibility of using a single RF emitter node for location finding. An autonomous ground vehicle must be able to locate the signal emitted and navigate to it without having prior knowledge of the environment. Through the comparison of Received Signal Strength (RSS) at multiple directional antennae, approximations could be made as to the relative position of the emitter. A particle filter was added to improve the accuracy of these approximations over time. Simultaneous Localisation and Mapping (SLAM) was added to allow for A∗ path planning through the local environment in order to reach the emitter. A number of benchmark tests were created to prove system functionality and demonstrate that the system is able to find and navigate to a nearby emitter whilst avoiding obstacles.
Vertical Nanowire TFETs With Channel Diameter Down to 10 nm and Point SMIN of 35 mV/Decade

We present experimental data from vertical InAs/InGaAsSb/GaSb nanowire tunnel field-effect transistors with channel diameter scaled down to 10 nm and ability to reach a point subthreshold swing of 35 mV/decade at <inline-formula> <tex-math notation="LaTeX">${V}_{\text {DS}}= {0.05}$ </tex-math> </inline-formula> V. Furthermore, the impact of drain, channel, and source diameter scaling on the subthreshold swing and currents are studied. Impact of gate-overlap is more evident for devices with highly scaled source due to strong reduction of the current. Furthermore, small channel diameter makes these devices more susceptible to random telegraph signal noise.
A comparative study of genetic algorithm in sentiment analysis

Due to increase in competition, social media is playing a big role in increasing the potential customers for many organizations. Sentiment analysis is an important tool for analyzing the social media. It is a popular area in the domain of Natural Language Processing which deals in identifying the sentiments, opinion, emotion, aspect, view towards a piece of text which may refer to any object or an entity. It helps the organization to analyze their market strategy so that they can improve the product quality and customer service which will foster the sales revenue of the organization. Genetic algorithms are used popularly for sentiment analysis. They are majorly used in this field for optimization and search-related problems. Moreover, various tasks are involved in sentiment analysis. This survey provides a comparative study of the role of the Genetic algorithm in sentiment analysis on the basis of accuracy. It also provides a detail view of the tasks related to sentiment analysis.
iPUF: Interconnect PUF with Self-Masking Circuit for Performance Enhancement

A novel interconnect-based physical unclonable function, named iPUF, is presented in this paper. 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, that have become prominent in the advanced CMOS technology nodes. Initiated by a linear feedback shift register (LFSR), iPUF sequentially generates one bit signature at each clock cycle, making it more efficient compared to ring-oscillator PUF. For uniqueness enhancement, a novel self-masking scheme, trained by a given iPUF's own initial signature, is applied for signature generation. 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 self-mask. With 10% supply voltage, 0 - 100 degrees Celsius temperature variations, as well as one year of un-accelerated aging, iPUF's reliability values, are as high as 96.09%, 99.06%, and 99.63%, respectively. Finally, iPUF's robustness against various attacks is also proven.
High-level activity recognition based on analysis of spatio-temporal contexts

High-level activity recognition (HAR) has got widely used in healthcare, recommendation, activity sensitive applications etc. Because of the complexity, diversity and individuality of high-level activity, researchers are faced with the challenge of increasing recognition accuracy under realistic conditions. In daily life, high-level activity has a strong association with spatial location since people are used to doing an activity in a specific location. Furthermore, some high-level activities usually co-occur in temporal sequence, which is supposed to be considered in activity inference. In this paper, we propose an approach of high-level activity recognition based on analysis of spatio-temporal contexts. The approach extracts Region of Activity (ROA) from spatial locations and discovers Daily Activity Pattern (DAP) from temporal sequence of activities to support the design of classifiers of high-level activity. The experiments show that the proposed approach bring about promising performance improvements on activity recognition. The fusion of ROA and DAP gain an advantage over the existing work in accuracy.
Space vector pulse width modulation strategy for an even level inverter

The main aim of this paper is to develop a space vector based pulse width modulation (SVPWM) switching strategy for an even-level inverter. The concept of fractal theory is effectively utilized to define the switching vectors for this even-level inverter. No look up tables are used for implementing the scheme. The vectors needed to generate the reference space phasor are derived from the hexagonal coordinates using arithmetic expressions. The work is experimentally tested for a six-level inverter topology using open-end winding configuration of induction motor. Hardware results are presented to verify the design.
COME for No-Reference Video Quality Assessment

Nowadays, the issue of objective Video Quality Assessment (VQA) has been extensively studied. In this paper, we present an effective general-purpose VQA method named COnvolutional neural network and Multi-regression based Evaluation (COME). It requires no referred lossless video and is universal for non-specific types of distortion. A modified 2D convolutional neural network is introduced to learn the spatial features at frame level. At the same time, the motion information is extracted as temporal features at sequence level. And a multi-regression model is proposed to comprehensively assess the final video quality according to human’s psychological perception. The proposed method is tested on two commonly used databases with numerous kinds of distortions. The experimental results show that the proposed COME method is comparable with most popular full-reference VQA methods.
The use of PHM for a dynamic reliability assessment

The reliability of critical aircraft systems such as braking, actuation or cooling systems has a direct influence on the overall aircraft safety and availability. Thus, an unforeseen failure of such systems can have a huge financial impact due to unscheduled downtimes or even worse, can lead to a safety critical situation. A precise knowledge of these system's future reliability is therefore of great importance for any aircraft operator. The prevalent tools and processes used to conduct safety and reliability assessments have matured over the last decades. Nevertheless, they still form a rather static process, neglecting the true health state of an individual system. In this paper, it is proposed to utilize PHM methods to develop a dynamic reliability assessment method, which will assess the reliability of a given system at runtime (e.g. during flight), by monitoring and predicting the health status of safety critical components. It will be shown that this novel method provides several advantages compared to the classical reliability assessment process.
Multisensor Systems by Electrochemical Nanowire Assembly for the Analysis of Aqueous Solutions

The development of electrochemical multisensor systems is driven by the need for fast, miniature, inexpensive, analytical devices, and advanced interdisciplinary based on both chemometric and (nano)material approaches. A multicomponent analysis of complex mixtures in environmental and technological monitoring, biological samples, and cell culture requires chip-based multisensor systems with high-stability sensors. In this paper, we describe the development, characterization, and applications of chip-based nanoelectrochemical sensor arrays prepared by the directed electrochemical nanowire assembly (DENA) of noble metals and metal alloys to analyze aqueous solutions. A synergic action of the electrode transducer function and electrocatalytic activity of the nanostructured surface toward analytes is achieved in the assembled metal nanowire (NW) sensors. Various sensor nanomaterials (Pd, Ni, Au, and their multicomponent compositions) can be electrochemically assembled on a single chip without employing multiple cycles of photolithography process to realize multi-analyte sensing applications as well as spatial resolution of sensor analysis by this single-chip multisensor system. For multi-analyte electrochemical sensing, individual amperometric signals of two or more nanowires can be acquired, making use of the specific electrocatalytic surface properties of the individual nanowire sensors of the array toward analytes. To demonstrate the application of a new electrochemical multisensor platform, Pd-Au, Pd-Ni, Pd, and Au NW electrode arrays on a single chip were employed for the non-enzymatic analysis of hydrogen peroxide, glucose, and ethanol. The analytes are determined at low absolute values of the detection potentials with linear concentration ranges of 1.0 × 10−6 − 1.0 × 10−3 M (H2O2), 1.5 × 10−7 − 2.0 × 10−3 M (glucose), and 0.7 × 10−3 − 3.0 × 10−2 M (ethanol), detection limits of 2 × 10−7 M (H2O2), 4 × 10−8 M (glucose), and 5.2 × 10−4 M (ethanol), and sensitivities of 18 μA M−1 (H2O2), 178 μA M−1 (glucose), and 28 μA M−1 (ethanol), respectively. The sensors demonstrate a high level of stability due to the non-enzymatic detection mode. Based on the DENA-assembled nanowire electrodes of a compositional diversity, we propose a novel single-chip electrochemical multisensor platform, which is promising for acquiring complex analytical signals for advanced data processing with chemometric techniques aimed at the development of electronic tongue-type multisensor systems for flexible multi-analyte monitoring and healthcare applications.
Rule-based integration of smart services using the manufacturing service bus

Factories have to adapt permanently to changing situations in order to stay competitive. Premise to achieve this objective is up-to-date information on all levels of a factory and during the product life cycle, so that men and machine can optimize their activities according to their tasks. One approach to implement this economically is the massive application of sensors and information and communication technologies (ICT) leading to a Smart Factory. This process and related applications are summarized under the term of the forth industrial revolution (Industrie 4.0). It demands a flexible and easy-to-use integration of assets on the shop floor with ICT systems. The contribution of this paper is an Manufacturing Integration Assistant (MIALinx) that enables all these steps. The steps range from the integration to the sensing and analyzing of the sensor data to the execution of required actions. Furthermore, MIALinx provides an abstract rule based approach for users to model the behavior of the system. The presented system is based on concepts and technologies of the Internet of Things and service-oriented middleware. The main users targeted with our system are small and medium-sized enterprises that do not have the expertise or the investment possibilities to invest in completely new Industrie 4.0 systems but rather use their existing production assets and enrich them to achieve Industrie 4.0 capability.
A Bernstein-type Inequality for Some Mixing Processes and Dynamical Systems with an Application to Learning

We establish a Bernstein-type inequality for a class of stochastic processes that include the classical geometrically $\phi$-mixing processes, Rio's generalization of these processes, as well as many time-discrete dynamical systems. Modulo a logarithmic factor and some constants, our Bernstein-type inequality coincides with the classical Bernstein inequality for i.i. d. ~data. We further use this new Bernstein-type inequality to derive an oracle inequality for generic regularized empirical risk minimization algorithms and data generated by such processes. Applying this oracle inequality to support vector machines using the Gaussian kernels for both least squares and quantile regression, it turns out that the resulting learning rates match, up to some arbitrarily small extra term in the exponent, the optimal rates for i.i. d. ~processes.