Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensor Resistance Based Sensitivity Temperature Drift Tracking of Integrated 3D Hall Sensors

The magnetic sensitivity of voltage controlled Hall sensors changes up to $\pm 40\%$ in the temperature range from −40°C to 125°C, resulting in large measurement errors. To track the temperature induced sensitivity variation, state of the art systems use additional temperature sensors and/or on-chip calibration coils. This paper presents a method to track the magnetic sensitivity temperature drift of a Hall sensor based on its own resistance. The proposed method results in a tracking accuracy of $\pm$ 6% for Bx, By and $\pm$ 3% for Bz in the temperature range from −40°C to 125°C. Providing a magnetic sensitivity to bias current transfer function further enhances the tracking accuracy to $\leq 1\%$. It requires no additional integrated coils or temperature sensors.
Rooftop Solar PV Hosting Capacity Analysis in MV-LV Distribution Networks Using Quasi-Static Time-Series Simulation

The massive interconnection of rooftop solar photovoltaic (PV) generation to the distribution network constitutes a challenge for the operation and planning of distribution network. In this environment, the performance of the distribution network may be affected and a comprehensive analysis of the hosting capacity in the medium and low voltage network is necessary. A quasi-static time-series and short-circuit analysis considering scenarios of PV systems penetration is proposed to accurately capture the effects of resource intermittency and load dynamics. Six metrics are used: overvoltage, voltage unbalance, thermal loading, fault currents, reverse power flow, and network losses. To evaluate the performance of the method, a real distribution feeder is used, as well as characteristic data of the classes of consumers. Since the relevance of a metric in the impact of the operation depends on the particular characteristics of each case, it is convenient to quantify these different metrics.
Image Processing Model to Estimate Nutritional Values in Raw and Cooked Vegetables

The availability of high-calorie foods with contentious nutritional content has led to a worldwide increase in chronic disease. Therefore, monitoring of eating habits and practising healthy eating habits is recommended. Clinical diet assessment methods and mobile calorie tracking apps can be used to record daily food consumption but are often not user-friendly. Convenient image-based assessment models are currently available to recognise and estimate the nutritional value of foods directly from food images, but they do not consider how nutritional value changes after cooking. Consequently, VegeNet, a multi-output InceptionV3-based convolutional neural network model has been developed, which estimates the nutritional values of cooked and uncooked vegetables. The explicit use of the cooking state is the main contribution of this work. This deep learning model successfully classifies the food images at 97% accuracy and estimates the nutritional values at 15.30% mean relative error, making it suitable as a visual-based added food assessment solution. This can help users save time and avoid under-reporting problems.
A Review of Interval Valued Type 2 Fuzzy Rule-Based Classifiers

This paper presents a review of 21 past works on developing interval-valued type-2 fuzzy rule-based classifiers (IVT2FRBC) for various applications. Special attention is paid to two major topics: design and performance evaluation. The first topic involves decisions made pertaining to every relevant component of IVT2FRBC such as rule structure, number of input variables/features, type & number of membership functions for each variable, model construction method, model tuning, and so on. The second topic includes measures used to evaluate a classifier's performance, classifiers compared, and statistical tests carried out to describe the performance differences between/among classifiers. Based on the review, observations are made as to their soundness and usefulness.
CrossBal: Data and Control Plane Cooperation for Efficient and Scalable Network Load Balancing

Load balancing network traffic through multiple shortest-paths has become common practice to efficiently utilize the network infrastructure. Despite widespread adoption, Equal-Cost Multi-Path (ECMP) delivers performance far from optimal. Several load balancing solutions utilize Weighted-Cost Multi-Path (WCMP), splitting incoming traffic between links proportionally to link weights. However, implementing WCMP requires the controller to update match+action rules whenever the weights must be changed, introducing a delay before the appropriate traffic split can be applied. Additionally, weighted traffic splits are applied over network flows without regard to flow characteristics or needs. We propose CrossBal, a hybrid load balancing system based on Deep Reinforcement Learning (DRL) that focuses its efforts on high-impact elephant flows. The DRL agent is modeled to be able to efficiently utilize network links while minimizing the action space, allowing the agent to quickly learn how to load balance. Further, CrossBal can quickly react to network changes by monitoring and switching active routes directly in the data plane. Our evaluation shows that CrossBal can efficiently utilize network resources, using most available links, while also reducing link utilization imbalance. We also evaluate the elephant flow detection employed by CrossBal, showing how it can quickly identify elephant flows while efficiently utilizing switch resources.
A Predictive Point-of-Care Platform for Early Detection of Periodontal Disease

Periodontal disease, a major global health concern, often goes undiagnosed until advanced stages due to the lack of effective early screening methods. This paper introduces the development of a non-invasive, point-of-care (PoC) diagnostic platform for the early detection and monitoring of periodontal disease using saliva. The system consists of a reusable detector device and a biosensor chip with multiple electrodes for simultaneous analysis of key oral metabolites (lactate, pyruvate, succinate, and fumarate) from which diagnosis predictions can be made. The biosensor employs enzymatic reactions for selective metabolite detection, with results indicating near-linear responses across the physiological ranges. Despite the complexity of saliva, minimal cross-interference was observed, demonstrating the platform's potential for accurate early diagnosis. The adaptability of the design also promises possibilities for its use in detecting other oral and systemic conditions. Future work will focus on clinical validations and further development of the companion app for comprehensive disease monitoring and management.
Teardrops on My Face: Automatic Weeping Detection From Nonverbal Behavior

Human emotional tears are a powerful socio-emotional signal. Yet, they have received relatively little attention in empirical research compared to facial expressions or body posture. While humans are highly sensitive to others’ tears, to date, no automatic means exist for detecting spontaneous weeping. This article employed facial and postural features extracted using four pre-trained classifiers (FACET, Affdex, OpenFace, OpenPose) to train a Support Vector Machine (SVM) to distinguish spontaneous weepers from non-weepers. Results showed that weeping can be accurately inferred from nonverbal behavior. Importantly, this distinction can be made before the appearance of visible tears on the face. However, features from at least two classifiers need to be combined, with the best models blending three or four classifiers to achieve near-perfect performance (97% accuracy). We discuss how direct and indirect tear detection methods may help to yield important new insights into the antecedents and consequences of emotional tears and how affective computing could benefit from the ability to recognize and respond to this uniquely human signal.
Resource Allocation of Multi-User Workloads in Cloud and Edge Data-Centers Using Reinforcement Learning

Cloud and edge Data-center (DC) are designed to allocate computing resources dynamically to users based on the agreed Service Level Agreement (SLA). However, the ever-increasing demand for beyond 5G services necessitates an efficient workload management. A key challenge in this regard is auto-scaling, a dynamic process that adjusts computing resources to meet fluctuating system demands, optimizing resource utilization and cost efficiency. Traditional auto-scaling algorithms, which rely on fixed thresholds or control-theory, may face limitations in modern DC which are characterized by diverse, dynamic, and multi-user workloads. In this paper, we propose a Reinforcement Learning (RL)-based controller that extends the capacity of the state-of-the-art RL-based auto-scalers to the multi-user workload scenario. We compare the proposed RL agent against the well-known Proportional-Integral (PI) controller and a Threshold (THD)-based controller in a multi-user workload scenario in terms of created Cloud-native Network Functions (CNFs) and peak latency performed in a discrete event simulator.
Can LSE Reduce Noise in Sensing Applications?

This work reports an experimental investigation of Large Scale Excitation (LSE) in sensing applications. This technique aims at mitigating flicker noise by suppressing its originating, signal-dependent, phenomena. Differently from modulation/chopping, which is not effective in these situations (flicker noise is modulated just as the signal is), LSE aims at switching on/off the source of signal generation without leaving enough time for carrier trapping or mobility changing, so blocking random telegraph noise phenomena which build up 1/f noise. As 1/f noise appears both in transistors and thin-film resistors, which are both key elements in the front-end of various sensors types, this work, after providing a simplified model for the scenario, experimentally verifies LSE. Results confirm the possibility of consistent flicker noise reduction in MOS transistors, by as much as a factor 3.3, whereas no flicker noise reduction is observed on piezoresistors, hinting that the source of 1/f is here a different process.
Evaluation of Time-Series Imaging for Visual Analytics and Processing of an Indoor Air Quality Sensors Network

The Internet of Things (IoT) systems provide the technological support to perform automatic indoor air quality monitoring. However, most of the data IoT systems needs processing to obtain valuable information. In this work, we analyze the suitability of imaging time-series in terms of the amount of information based on images entropy, the accuracy to detect abnormal indoor air quality conditions by supervised classification with artificial intelligence (AI) methods, and its computational cost.
Phenomenological Range-Reflectivity Limits for Lidar Models and Feature Validation Approach

In sensor model development, the validation of individual model features is favorable. For lidar sensors, range limits depend on surface reflectivities. Two phenomenological feature models are presented for clear and adverse weather conditions. These models are derived from the lidar range equation. Maximum ranges for best environment conditions are based on data sheet parameters and attenuation on maximum range measurements. An application is presented using a physical point cloud lidar model. Moreover, an assessment of different feature models is needed, similar to unit tests. Therefore, resulting point clouds are compared with respect to the total number of corresponding points and the number of points with no correspondences for pair-wise cloud comparison. With a reference point cloud provided by the sensor manufacturer, feature validation becomes possible.
Reduced Graphene Oxide-Based Chemiresistive NO2sensor: Metal Oxide Nanoparticles Decoration Effect

We present the development of a stretchable platform-based nitrogen dioxide (NO2) gas sensor that operates at room temperature (RT). This study investigates the impact of incorporating metal oxide nanoparticles (NPs) into a chemiresistive NO2 gas sensor, utilizing reduced graphene oxide (rGO) as the primary sensing material. By introducing mesoporous ZnFe2O4 (ZFO) NPs at different concentrations into the rGO sensing layer, the performance of the gas sensor is significantly enhanced. The inclusion of ZFO NPs, which possess high-density defect sites, contributes to fast response and recovery times across a wide range of NO2concentrations (150 to 4000 ppb). Various transient parameters are analyzed to assess the effects of ZFO NPs integration. The rGO-ZFO-based gas sensor exhibits improved sensitivity and reproducibility. Moreover, these sensors, integrated into a stretchable platform and capable of RT operation, open possibilities for the development of advanced wearable NO2 gas sensors in the future.
Hierarchical Attention Feature Fusion-Based Network for Land Cover Change Detection With Homogeneous and Heterogeneous Remote Sensing Images

Deep learning techniques have become popular in land cover change detection (LCCD) with remote sensing images (RSIs). However, many existing networks mostly concentrate on learning deep features but without considering the effect of different features’ attention and fusion strategy on detection performance. In this article, a novel hierarchical attention feature fusion (HAFF)-based network for LCCD with RSIs is proposed. In the proposed HAFF-based network, novel multiscale convolution fusion filters (MCFFs) explore the global semantic feature of the interested targets from multiperspective ways. To achieve that objective, the proposed MCFFs are composed by a well-known position attention module (PAM) and a novel multiperspectives feature filter block (MFFB) with different kernel sizes. In addition, a compound loss function was proposed for balancing the impact from the features at different levels in terms of backpropagation error. Experiments conducted on six pairs of real RSIs, including three pairs of homogeneous images and three pairs of heterogeneous images, confirmed the superiority of the proposed HAFF network over other cognate methods. Moreover, the ablation experiments further confirmed the feasibility and superiority of the proposed MCFFs, whereas quantitative observations indicated that competitive improvements are achieved by the proposed MCFFs in terms of all the evaluation indicators. The code for the proposed approach will be available at https://github.com/ImgSciGroup/HAFF.
A Comparison Between Genetic Algorithm and Particle Swarm Optimization for Economic Dispatch in a Microgrid

Microgrids (MGs) and distributed energy resources (DERs) have gained popularity as a path to develop a more resilient, sustainable, and efficient energy infrastructure. Although photovoltaic (PV) systems and wind generators are common technologies in MGs, their variability and uncertainty pose reliability challenges. Battery energy storage systems (BESS) are integrated as backup solutions to address these issues. Energy management (EM) can perform economic dispatch (ED), which involves determining the most cost-effective combination of energy generation sources. In this paper, genetic algorithms (GA) and particle swarm optimization (PSO) were used to optimize the use of BESS in an MG, considering discharge cost factors that affect BESS lifetime. The results obtained reveal that both algorithms efficiently performed ED, however, PSO exhibited superior cost efficiency in its optimization outcomes and outpaced GA by 37.5% in terms of speed. Consequently, PSO showcased improved precision and swiftness when compared to GA in this task.
Glucose and Lactate Amperometric Sensors on a Flexible Printed Circuit for Low-Cost Sensing Applications

Printed circuit board (PCB) and flexible printed circuit (FPC) technologies are widely available, established and low-cost technologies. They can be exploited to realize various types of sensors that can easily be co-integrated with the required instrumentation, to realize cost-efficient, compact sensing devices. This paper presents the realization of flexible glucose and lactate enzymatic sensors on an FPC electrode array with pure soft gold electrode finish, designed and manufactured with commercial tools and vendors. The glucose sensor was characterized up to 30 mM and the lactate up to 1 mM and where capable of providing a measurable response as a function of analyte concentration with measured currents of up to 300 nA and 60 nA, respectively.
A Direct Piezoresistive Method to Transduce Electromechanical Motion in Self-Sensing Suspended NanoStructures

We present a facile method to transduce nanome-chanical vibrations induced by electrostatic actuation in sus-pended nanostructures using their intrinsic piezoresistivity. The capacitive method largely used for graphene nanomechanical res-onators is not very effective for materials with lower conductivity and also for low dimensional structures. A piezoresistive method, however, can address the transduction of a wide range of mate-rials and nanostructures. We, in this effort, have formulated a piezoresistive transduction method that involves simple circuitry and has a non-scaling electrical background independent of the mechanical motion, unlike capacitive transduction schemes where the electrical background scales with the mechanical vibrational amplitude. This facilitates an easy discernment of the electrome-chanical resonance for different drive voltages. Experimental measurements performed on an in-house fabricated reduced graphene oxide (rGO)-based suspended piezoresistive membrane reliably support the formulation of the principle of piezoresistive transduction advanced in this paper.
Effects of Various Head Movement Executions on Spatial Accuracy of Magnetoencephalography Systems

Head movements during magnetoencephalography (MEG) recordings lead to a smearing of the magnetic field distribution measured by the MEG sensors and thus to a significant degradation of the MEG spatial accuracy. This in turn is associated with inaccurate source localization. Head movement detection was applied to MEG recordings performed under the condition of different degrees of head movement (moderate, weak, quasi-no) and compared with individually constructed head-cast recordings. The center of the head moved on average around 4.8 mm for moderate motion and 3.5 mm for weak motion. Even without movement execution, the head center moved around 1.4 mm on average, which could be significantly minimized to less than 0.2 mm by using an individual head-cast. In addition, it was found that repositioning of the head within the MEG scanner could be realized with a mean deviation of the MEG sensor positions of around 1 mm with the use of an individual head-cast, while this deviation was around 4.7 mm without head-cast. Our work suggests that the use of head-casts allows maximization of the spatial precision of MEG systems and ensures the comparability of separately recorded sessions.
Leveraging Spatiotemporal Relations for Predicting Potential Link Failures

Being able to predict link failures in advance would be of great benefit to network operators. We use Machine Learning (ML) techniques to extract temporal and spatial relations from real network data and use them to predict link failures. We use Interior Gateway Protocol (IGP) configuration changes as a guide to achieve this. We predict link failures in the next five days based on data collected from the previous five days. We propose a modified Variational Auto Encoder (VAE) model to compress the higher dimensional dataset into a latent space that captures time-based relations in the data. We demonstrate that five days is the smallest look-back window of time required to get satisfactory prediction results. Using feature importance plots, we learned that the VAE model was able to capture intricate time-based dependencies in the error counter features to achieve good performance. In addition, using a Graph Convolutional Network (GCN), we were able to aggregate data from neighboring links to improve the model's performance. Neighbors up to two hops away carried relevant information in IGP metric settings and in traffic metric counter features. The relevance of the correlation of the features in time and space is confirmed using standard feature importance wrapper methods. Finally, by combining the VAE and GCN components, we were able to extract spatial and temporal features in conjunction, leading to further improvements. These ML approaches significantly improve existing manual methods of tracking metrics in time and space currently followed by the operator.
Implementation of the formation factor (F) to evaluate properties of cement-based materials from electrochemical techniques

Corrosion of reinforcing steel generated by chloride penetration is one of the main causes of deterioration of reinforced concrete structures. One of the best ways to control deterioration in structures due to this process is through quality control of the concrete before use. The capillary porosity and migration give parameters that could be related to material durability mechanisms. In this case, the Standard test method for density, absorption, and Voids in hardened concrete (ASTM C642:13), the Standard Test method for electrical indication of concrete’s Ability to Resist Chloride ion Penetration (ASTM C1202:19) and the Chloride migration coefficient from non-steady-state (NT BUILD 492:99) provide procedures for determining parameters to characterize the ability of concrete to resist chloride penetration. However, these tests are difficult, time-consuming, and expensive to perform. Therefore, in recent years the formation factor (F) has emerged as an alternative to develop tests that allow concrete to be evaluated in an easy, fast, and economic way. The formation factor (F) comes from Archie’s Law, and it is a dimensionless factor that allows us to relate the resistivity of a rock, with its porosity and with the resistivity of the fluid contained in it. This factor has been used successfully to characterize some properties of concrete. In that order of ideas, in this paper the capillary porosity, the chloride penetrability and the chloride migration coefficient of mortar mixtures with different percentages of addition of silica fume (SF) were characterized by the Formation Factor (F). For this purpose, Portland cement was partially mixed with Silica fume (SF) up to 15%, in four different mortar mixtures, and for the measurement of the Bulk resistivity of mortar samples (p), electrochemical impedance spectroscopy (EIS) was used. Additionally, tests of compressive strength, and Bulk density were carried out. A new experimental method based on electrochemical impedance spectroscopy (EIS) to characterize capillary porosity and chloride penetration in cement-based materials is proposed. Finally, this paper shows a strong correlation between the formation factor (F) and the capillary porosity, the chloride penetration and the compressive strength properties of silica fume mortars, which could be used as a parameter for quality control of cement-based materials. Further studies on this subject are highly recommended.
Plant Disease Detection Using an Electronic Nose

This paper presents experimental results on differentiating between healthy wheat plants and plants infected with Fusarium Head Blight (FHB) based on sensing the ambient gases in the plant environment using a gravimetric electronic nose enabled by a functionalized capacitive micromachined ultrasonic transducer (CMUT) array and machine learning (ML) algorithms. The CMUT sensor array is functionalized with organic/inorganic materials to capture disease-related volatile signals. The sensor data is processed and analyzed using ML algorithms for accurate plant classification. Experimental results demonstrate the effectiveness of the proposed approach in achieving high accuracy for plant disease detection at the end of the 11th day after plant inoculation.