Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel Capacity Analysis of a Comprehensive Absorbing Receiver for Molecular Communication via Diffusion

As a promising communication paradigm, Molecular Communication (MC) has been developing for more than ten years. During this period, with the continually development and mature of molecular channel, more and more factors from the actual molecular environment are taken into account to model the molecular channel more precisely. Due to the fact that chemical reactions are common and crucial in molecular communication environment, this paper investigates the impacts of various reaction rates on the channel modeling and channel capacity of MC. First, a detailed molecular system model is presented with a first-order degradation reaction in the channel and a second-order reversible reaction at the receiver side. Because of the Inter-Symbol Interference (ISI) induced by the stochastic nature of molecule motions, we propose an approximation method with low complexity to describe the received molecule signal with an arbitrary length of ISI. Based on the noisy molecular signal, closed-form expressions for probabilities of detection and false alarm are derived. The performance of channel capacity is evaluated concentrating on different circumstances with decoupled reaction rates and two prevalent modulation techniques (BCSK and MoSK) are implemented to ensure the accuracy and scientific of the conclusion. Numerical results show that the channel capacity could be enhanced significantly by selecting proper messenger molecules with desirable reaction rates in specific molecular channel.
An Analysis of Acoustic Features in Reading Speech from Chinese Patients with Depression

This paper investigates acoustic features of depression patients in voice quality and formants, from the perspective of experimental phonetics. The analysis on voice quality based on large samples shows that jitter, shimmer and HNR can distinguish the patients with different degrees of depression, while F0, standard deviation of F0 and HNR can distinguish depression patients from non-patients. These features indicate that the voice of patients tends to be hoarse and rough, with a lower pitch falling into a narrower range. The analysis on formants shows that depression patients tend to centralize monophthongs and simplify diphthongs, reflected by a lower opening degree and slower movement of tongue. Moreover, the patients tend to show a lower spectrum energy than healthy people. Finally, our analysis results suggest that these acoustic features can be used as objective markers for recognition of depression.
Axial Resolution Enhacement of Light-Sheet Microscopy via two Light-Sheets

Light-sheet fluorescence microscopy has many advantages including high-speed, noninvasive and low photobleaching and photodamage. The light-sheet thickness of light-sheet microscopy is used to determine the axial resolution. However, the light-sheet thickness is limited by the light diffraction. In order to beyond this limit, inhere, we introduce a novel way based on the use of two light-sheets to achieve the enhancement of the axial resolution of light-sheet microscopy. Two images are captured by using both Gaussian light-sheet and negative light-sheet beams. From these two images, a new relationship between them is built to achieve the axial resolution image higher than the image of Gaussian light-sheet. Experimental result is performed, indicating that the effectiveness of the proposed method is better than traditional light-sheet microscopy.
Natural Attenuation of Oily Sludge Used for the Maintenance of an Unpaved Road in Arauca (Colombia)

ABSTRACT IN this study the biodegradation and leaching of total petroleum hydrocarbons (TPHs) during the natural attenuation (NA) processes of the oily sludge used for the maintenance of unpaved roads were evaluated. The study was conducted on a road located in Arauca (Colombia) and simultaneously in a laboratory setting where rain conditions were simulated by using columns. In the in situ study, three depths (5, 50, and 80 cm) were assessed. The degradation of TPH was evaluated by monitoring physicochemical and microbiological conditions in situ and in the columns during 538 and 119 days, respectively. The pH levels observed during the study were relatively constant and ranged between the optimum values for biodegradation (6.2 ± 0.9). Water content in situ was low (< 14%) and observed concentrations of nitrogen (as ammonia and nitrate) were < 30 and < 10 mg/kgdw, respectively, indicating that no mass balance was maintained, both possible factors limiting intrinsic biodegradation. During the in situ study a 95% TPH degradation was observed at 5 cm depth, whereas no degradation was evident at 50 and 80 cm. In the column experiments, TPH concentration in the leachate was < 1 mg/L, indicating that the leaching process did not play a key role in this study.
Staircase Negotiation Learning for Articulated Tracked Robots with Varying Degrees of Freedom

Tracked robots capable of negotiating 3D terrains require delicate control, most often tailored to a specific platform or setting. For staircase traversal in particular, autonomous robot behaviours are difficult to obtain due to the increased risk of accident and stochasticity. Based on a previously developed reinforcement learning based framework that allows learning staircase ascent for an articulated tracked robot, in this work we extend our work to allow also staircase descent and further investigate the role of a manipulating arm in the stability and smoothness of the traversal. By relying on a precise simulation of geometry and kinematics of a real robot, we demonstrate prototype policies for staircase ascent and descent, optionally under the influence of an integrated active arm and different penalty criteria. The obtained results are qualitatively and quantitatively compared and show that the robot can learn plausible behaviors effectively, when guided by appropriate reward and penalty criteria.
Controller Hardware-in-the-Loop Testing of an IEC 61850 GOOSE Based Control for Seamless Transition of a Microgrid Between Island and Grid-Connected Modes

This article demonstrates development of a controller hardware-in-the-loop test system for validating microgrid control schemes. The investigation also proposes and analyzes the performance of an IEC 61850 generic object-oriented substation event protocol (GOOSE) based control strategy for managing the transition of a microgrid test model from grid-connected mode to islanded mode. The test model contains a single inverter-interfaced distributed energy resource (DER). The inverter controls allow the DER to regulate its output during grid-connected operation and regulate the microgrid voltage and frequency when separated from the Area electric power system. The test model, inverter, and microgrid controllers are simulated using RTDS Technologies’ real-time digital simulator. A digital protection relay is used in the hardware loop to provide I/O to the simulated controllers via GOOSE messaging. The results of the investigation illustrate the benefits of IEC 61850 standard for integration of controller hardware in a microgrid control scheme as well as the suitability of GOOSE messaging for seamless transitioning between microgrid operating modes.
Misconfiguration Discovery with Principal Component Analysis for Cloud-Native Services

Cloud applications and services have significantly increased the importance of system and service configuration activities. These activities include updating (i) these services, (ii) their dependencies on third parties, (iii) their configurations, (iv) the configuration of the execution environment, (v) network configurations. The high frequency of updates results in significant configuration complexity that can lead to failures or performance drops. To mitigate these risks, service providers extensively rely on testing techniques, such as metamorphic testing, to detect these failures before moving to production. However, the development and maintenance of these tests are costly, especially the oracle, which must determine whether a system’s performance remains within acceptable boundaries. This paper explores the use of a learning method called Principal Component Analysis (PCA) to learn about acceptable performance metrics on cloudnative services and identify a metamorphic relationship between the nominal service behavior and the value of these metrics. We investigate the following research question: Is it possible to combine the metamorphic testing technique with learning methods on service monitoring data to detect error-prone reconfigurations before moving to production? We remove the developers’ burden to define a specific oracle in detecting these configuration issues. For validation, we applied this proposal on a distributed media streaming application whose authentication was managed by an external identity and access management services. This application illustrates both the heterogeneity of the technologies used to build this type of service and its large configuration space. Our proposal demonstrated the ability to identify error-prone reconfigurations using PCA.
Design of Diving System Controller for an Autonomous Underwater Vehicle

A diving system control problem for an autonomous underwater vehicle (AUV) is considered. Linearized decoupled model is used to represent the kinetics/dynamics of the system for diving control. A single sensor is used to estimate the unmeasured state through diving system control structure. Two controller design schemes are defined and used for steady-state tracking and noise reduction. A proportional derivative (PD) controller and pole placement with estimator approach are adopted to synthesize the control law. The selection of the desired closed-loop pole and initial condition are discussed. Performance of the controller based on the desired specification and steady-state tracking is examined using simulation and its results are discussed.
Improved Combiner Design for Broadband Outphasing Power Amplifier

In this paper, an outphasing power amplifier(PA) with novel combiner is proposed. By replacing quarter wavelength impedance in classic structure with series microstrip lines with stop-band suppression, the proposed circuit achieved wide bandwidth while ensuring acceptable efficiency and linearity. Analytical and ADS simulation are presented in this work, an outphasing PA operating at center frequency 3.1GHz with 200MHz bandwidth is realized. The simulated results show excellent agreement in all cases considered.
Two-Staged Self-Attention Based Neural Model For Lung Cancer Recognition

Our work is devoted to the neoplasms presence recognition problem in the context of lung computer tomography photographs analysis. This problem is urgent due to the high lung cancer mortality rate. We propose a monochrome lungs tomography photographs analysis engine which could be useful for online medical consultation services. Our approach uses two-staged a self-attention based architecture and demonstrates results of 0.99F1 score. The presented results are obtained on open dataset of 10052 images.
Safe Prediction of Diabetes Mellitus Using Weighted Conglomeration of Mining Schemes

Diabetes mellitus (DM) is a chronic disease, from which most people are suffering in the present day. In this work, an attempt has been made to propose an ensemble of numerous techniques for the diagnosis and analysis of diabetic Mellitus data. The prime focus has been given to a safe prediction. One of the reasons for focusing on this ailment is due to its increasing rate of co-existence and occurrence over the world. This ailment is causing approximately one and a half million casualties every year. This work is meant to mitigate the challenge of early prediction of DM. For this, four different data mining techniques have been utilized namely lazy K-star, multi-layer perceptron (MLP), logistic regression and random forest. Using these four techniques a conglomerate algorithm was proposed which gives the final predicted label. If a patient test data is assigned a positive DM label by more than three classifiers then only it is assigned with the final label as positive DM, otherwise, it is treated as a negative. Satisfactory results in terms of the overall rate of accuracy have been obtained through this ensemble approach. Here, accuracy refers to the correct classification and prediction of DM through the proposed scheme. The proposed algorithm obtained an overall accuracy of 98.25%.
Compressive sensing based watermarking as a security layer for computational imaging applications

The evolution of digital imaging since its early days has mostly been driven by hardware advancements. Computational imaging is a recent alternative to traditional imaging, that focuses on the use of digital processing algorithms.One notable example is the single pixel imaging and its recent developments in the area of lensless imaging. It uses image acquisition based on the compressive sensing (CS) theory, resulting in measurement data that use less storage space. However, it requires more computational resources in order to reconstruct the image and make it available for the end user.The CS theory also opens new research opportunities regarding the security of digital image processing, storage and distribution. This paper presents a privacy enabling architecture for a real-life scenario of image acquisition using the single pixel camera. A reversible watermark is inserted to the measurement data before storage and distribution.
Different Types of Phishing Attacks and Detection Techniques: A Review

In this paper a review has been taken on different types of phishing attacks and detection techniques. Most of the cyber attacks are spreading through users weaknesses, which makes user weakest elements in security chain. Mostly this phishing targets the vulnerability which is already in the system due to human factor. Phishing is the huge problem and there is no single solution for mitigating the vulnerability completely so one has to use number of techniques to mitigate the attack. Here we presented various categories of mitigation technique for the phishing website like detection, correction, prevention and offensive defense.
Single-Input Multiple-Output Control for Multi-Goal Orchestration

In this paper, we propose a QoS-aware Single Input Multiple Output (SIMO) controller that combines performance and cost goals while aiming to maintain system stability. To enhance robustness, as targeted by inspiring control concepts, we use system identification models and analytical tuning techniques for Proportional-Integral-Derivative (PID) controllers. Our resulting SIMO PI controller performs well when tracking reference values that may change over time and when conciliating conflicting goals according to the user’s preference. In contrast, a naïve use of independent controllers may lead to opposing decisions and instabilities, as the controllers work against each other. We examine the use of the controller to orchestrate processing pods in a Kubernetes cluster for an IoT sensor analysis application (power consumption disaggregation). Nevertheless, the lessons learned in the design of the controller apply to other use cases, including batch and interactive workloads.
Development of a power converter control system for increasing system stability after an emergency in distributed networks

Thanks to the development of technologies and their price reduction, there are more opportunities for integrating renewable energy sources and creating networks with distributed generation. In these networks, generating units are represented by small synchronous generators, photovoltaic units, or wind turbines. To control and efficiently integrate them into existing networks, power electronics converters are used. All this affects the decrease in the inertia of the system as a whole, therefore, the speed and nature of transients as a result of, for example, short circuits in the network is significantly changing. Thus, in the event of a power shortage due to loss of generation or loss of communication with the system, a significant decrease in the frequency is possible in very short time intervals due to the small values of the system inertia. In addition, there is an acute question for the board, operational reliability and stability of networks with distributed generation in isolated operating modes, since there is a problem of coordination of each of the inverters and their correct configuration.
Speed Sensorless Finite Set Model Predictive Speed Control of Induction Motor

This paper presents Speed Sensor-less Finite set model predictive speed control of three-phase induction motor. In this method, it is assumed that a finite set of switching states is obtained in three-phase inverter only. The motor speed is estimated by using voltage and current sensor using an induction motor model. In this predictive speed controller, there is no PI controller for torque reference generation. This paper explores the control technique with different reference speeds and gives better performance than the conventional speed controller. The current constraint is also considered in this paper to limit the stator current during the transient. The weighting factor in optimization determines the performance of controller. The results of simulation have been presented.
Research of a Self-adaptive Mixed-Variable Multi-objective Ant Colony Optimization Algorithm

Multi-objective optimization (MOO) problems are often encountered in engineering calculations. Commonly used MOO algorithms include classical algorithms and modern heuristic algorithms. Modern heuristic algorithms can find the Pareto solution set, and are used most widely at present. This paper improves the ACOMV algorithm which is only suitable for solving mixed-variable single-objective optimization (SOO) problems, and proposes a MOACOMV algorithm suitable for solving mixed-variable MOO problems. And aiming at the dependence of MOACOMV algorithm performance on parameter setting, a SAMOACOMV algorithm using self-adaptive parameter setting scheme is proposed. Furthermore, the paper also designs one mixed-variable MOO benchmark for purpose to test and compare the performance of SAMOACOMV algorithm. The experiments indicate that SAMOACOMV algorithm has excellent comprehensive performance and is an ideal choice for solving mixed variable MOO problems.
Improving the EMI Robustness of Feedback-based Time-Encoding Readout Architectures for Resistive Sensor Interfaces

Time-encoding architectures enable compact, technology-scalable, thus low-cost sensor readout solutions, which are important features in many applications. As safety is a major requirement in critical applications like the automotive industry, this implies absolute robustness for the electronics. In order to achieve EMI robustness for sensor interface chips, low-pass filtering needs to be added at the sensor node, especially when external sensors are used. However, when using closed-loop time-encoding sensor readout architectures, the sensor element is inside the feedback of the interface. This limits the amount of low-pass filtering that can be applied, without jeopardizing the correct operation and stability of the system. This paper describes a novel circuit solution for closed-loop sensor readout that decouples the sensor excitation mechanism and the feedback path. This ensures a better EMC immunity thanks to the larger low-pass filtering, without compromising the readout operation of the interface. The final goal is that time-based sensor interface can achieve the same level of EMC performance as traditional amplitude-based readout architectures but in a more compact, scalable and low-cost solution.
A review on analysis of sleep EEG signals

Sleep EEG signal investigation is such something critical for illness examination and brain-computer examination. By the Usage of EEG spectral analysis method, we can explore the concealed wealth and consequently the exercises of the brain. Spectral examination of sleep EEG signal gives intense knowledge into the features of various phases of sleep which can be used to separate among typical and abnormal conditions. Present-day brain research is personally connected to the possibility to log the EEG and its significant study. This paper portrays the dissecting of human sleep EEG signals using Wavelet Transform and other methods, also conclude which is better to extract the features from sleep EEG signal. Discrete Wavelet Transform provides portrayals of the signs in the timerecurrence plane offers the data with respect to the time restriction of the spectral segments at various phases of sleep in individual and it is fast to decompose the original signal into small signal frequency, additionally talks about the clinical connection related to sleep EEG signals in detail.
Improved Trust Model based on Centrality Measures and Recommendation in Social Network

In this article, a model is developed to improve trust value in the relations that represents social networks by utilizing centrality measures interred with all participants in network-based and recommendations, both on connection or trust. Various central metrics were discussed and implemented to intend to trust. Algorithms are provided to facilitate their calculations. The referral neighbor that has a guaranteed trust boundary is chosen. Trust Value based on Interaction and Recommendations using Centrality Metric (TVIRCM) method is proposed and implemented in this study to improve trust value in the social network when the link between any two nodes represent the unique indication about trust whereas, there are no other standards for maintaining trust. Trust based on interaction refers to trust calculations based on real links observations and exploits the centrality metric. Trust based on recommendation refers to trust calculation based on trust participants of a remote neighbor about other participants.The developed approach is utilized in a trust observation phase as a trust-based interaction (i.e. assigned high and low centrality metrics), then the next phase is based on the proposed recommendation (i.e. the remote neighbor may have certain trust value) and the last phase is the trust calculation phase which based on combining direct and indirect trust.
Integrating Quantum Computing into Workflow Modeling and Execution

Quantum computing has the potential to significantly impact many application domains, as several quantum algorithms are promising to solve problems more efficiently than possible on classical computers. However, various complex pre- and post-processing tasks have to be performed when executing a quantum circuit, which require immense mathematical and technical knowledge. For example, calculations on today’s quantum computers are noisy and require an error mitigation task after the execution. Hence, integrating classical applications with quantum circuits is a difficult challenge. In this paper, we introduce a modeling extension for imperative workflow languages to enable the integration of quantum computations and ease the orchestration of classical applications and quantum circuits. Further, we show how the extension can be mapped to native modeling constructs of extended workflow languages to retain the portability of the workflows. We validate the practical feasibility of our approach by applying our proposed extension to BPMN and introduce Quantum4BPMN.