Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing the Origins and Applications of Atypical Capacitive Cell Phenomena in Microfluidic Impedance Sensors

This paper investigates the origins of an uncharacteristic capacitive phenomenon in cells that is highly frequency dependent and has remained unreported until now. This behavior was observed to be consistent in >10000 individual red blood cells probed using a microfluidic impedance sensor integrated with coplanar microelectrode structures and also repeatable across different devices. To better understand the reasons for this phenomenon, the system was extensively modelled using COMSOL Multiphysics and in our findings, we report a model that greatly agrees with our experimental observations. We also describe preliminary efforts in using this novel capacitive phenomenon to overcome long-standing limitations of traditional microfluidic devices in accurate cell position sensing and obtaining vital electrical parameters of cells.
Mutation Analysis for Coq

Mutation analysis, which introduces artificial defects into software systems, is the basis of mutation testing, a technique widely applied to evaluate and enhance the quality of test suites. However, despite the deep analogy between tests and formal proofs, mutation analysis has seldom been considered in the context of deductive verification. We propose mutation proving, a technique for analyzing verification projects that use proof assistants. We implemented our technique for the Coq proof assistant in a tool dubbed mCoq. mCoq applies a set of mutation operators to Coq definitions of functions and datatypes, inspired by operators previously proposed for functional programming languages. mCoq then checks proofs of lemmas affected by operator application. To make our technique feasible in practice, we implemented several optimizations in mCoq such as parallel proof checking. We applied mCoq to several medium and large scale Coq projects, and recorded whether proofs passed or failed when applying different mutation operators. We then qualitatively analyzed the mutants, finding many instances of incomplete specifications. For our evaluation, we made several improvements to serialization of Coq files and even discovered a notable bug in Coq itself, all acknowledged by developers. We believe mCoq can be useful both to proof engineers for improving the quality of their verification projects and to researchers for evaluating proof engineering techniques.
Constrained Distance-Based Clustering for Satellite Image Time-Series

The advent of high-resolution instruments for time-series sampling poses added complexity for the formal definition of thematic classes in the remote sensing domain—required by supervised methods—while unsupervised methods ignore expert knowledge and intuition. Constrained clustering is becoming an increasingly popular approach in data mining because it offers a solution to these problems; however, its application in remote sensing is relatively unknown. This article addresses this divide by adapting publicly available constrained clustering implementations to use the dynamic time warping (DTW) dissimilarity measure, which is sometimes used for time-series analysis. A comparative study is presented, in which their performance is evaluated (using both DTW and Euclidean distances). It is found that adding constraints to the clustering problem results in an increase in accuracy when compared to unconstrained clustering. The output of such algorithms are homogeneous in spatially defined regions. Declarative approaches and $k$-means-based algorithms are simple to apply, requiring little or no choice of parameter values. Spectral methods, however, require careful tuning, which is unrealistic in a semisupervised setting, although they offer the highest accuracy. These conclusions were drawn from two applications: crop clustering using 11 multispectral Landsat images nonuniformly sampled over a period of eight months in 2007; and tree-cut detection using 10 NDVI Sentinel-2 images nonuniformly sampled between 2016 and 2018.
Food Talks: Visual and Interaction Principles for Representing Environmental and Nutritional Food Information in Augmented Reality

This user-centered design research project aimed to investigate visual and interaction principles for augmented reality (AR) in the context of environmental and nutritional food labelling. Nutritional information on existing food labels is often misunderstood and environmental information is seldom depicted, despite consumer demand. The project explored the potential of AR in this context with a two-phase process. Phase 1 aimed to engage large audiences in public spaces using a stand-alone AR device showing environmental information only. This allowed design strategies to be tested. Phase 2 addressed personalised information, combining nutritional and environmental data with smartphone AR. Here we integrated design learnings from Phase 1 and then focused on assessing the benefits of AR. A between-subjects study with 84 participants compared two-versions of the smartphone application; one version showed the information with AR and the other showed the same information with a static page. Results showed that participants using the AR version learned more about food products than those using the static version. In addition, the AR version matched the high scores of the static version with regards to usability (SUS score of 86) and aesthetics (VisAWI score of 5.9), despite technical limitations of AR. This work reveals that AR can be a credible medium in the food industry and provides visual and interaction design learnings to inform designers in the industry.
Reconfigurable Filter for Bandpass-to-Absorptive Bandstop Responses

A frequency tunable filter with a bandpass-to-absorptive bandstop reconfigurable characteristic is studied, designed and validated theoretically and experimentally. To control the filter bandwidth and impedance matching performance, variable capacitors are used in each external and internal coupling structure, and, by using equivalent circuits, the required frequency responses of two different modes are obtained. Two single-pole double-throw switches embedded in the transmission lines running from the source to the load accommodate external coupling structures to control the operational mode of the proposed filter. All commercially available electronics such as switches and varactor diodes are placed on the microstrip line layer, and the frequency-tunable substrate integrated waveguide (SIW) resonators are coupled to the transmission line through coupling slots. In the absorptive bandstop mode, the stopband is tuned from 1.83 GHz to 2.49 GHz, while the passband can be tuned from 1.86 GHz to 3.3 GHz in the bandpass mode. In both modes, more than 10 dB in return loss over the frequency tuning range of interest has been achieved. Theoretical, simulated, and measured results are in good agreement, which validate the proposed filter structure.
Exploring Network Embedding for Efficient Message Routing in Opportunistic Mobile Social Networks

With the advancement in communication technologies and the widespread availability of mobile devices, the opportunistic mobile social networks (OMSNs) are gaining momentum in supporting spontaneous communication and interaction among end-users who opportunistically contact each other. However, existing research on message routing in OMSNs face major challenges on achieving a high routing efficiency and low latency for social information request. This paper proposes a personalised message routing (PMR) framework that leverages an inductive network embedding model and an attention-based mechanism to facilitate efficient message routing in opportunistic networks. Specifically, the network embedding model encompasses a higher-order proximity profiling algorithm in order to embed both the content-based and structure-based network features beyond immediate friends into low dimensional representations. Further, we present an attentional neural network model to learn user-friend preferences, for the purpose of capturing the diversified interests among connected users and to determine the most informative friends during the message dissemination process. The performance of our proposed framework is evaluated through simulations on three real-world mobile network trace datasets and the experimental results show that the proposed PMR framework considerably and consistently outperforms the state-of-the-art message routing methods.
A Data-Trained, Affine-Linear Intra-Picture Prediction in the Frequency Domain

This paper presents a data-driven training of affine- linear predictors which perform intra-picture prediction for video coding. The trained predictors use a single line of reconstructed boundary samples as input like the conventional intra prediction modes. For large blocks, the presented predictors initially transform the input samples via Discrete Cosine Transform. This allows to omit high frequency coefficients and consequently reduce the input dimension. The output is the result of a single matrix-vector multiplication and offset addition. Here, the predictors only construct certain coefficients in the frequency domain. The final prediction signal is then obtained by inverse transform. The coefficients of the prediction modes need to be stored in advance, requiring 0.273 MB of memory. The training employs a recursive block partitioning, where the loss function targets to approximate the bit-rate of the DCT-transformed block residuals. The obtained predictors are incorporated into the Versatile Video Coding Test Model 4. The authors report All- Intra bit-rate savings ranging from 0.7% to 2.0% across different resolutions in terms of the Bjøntegaard-Delta bit rate (BD-rate).
Power Quality Improvement of a Self-Excited Induction Generator Using NFPI Controller Based Hybrid STATCOM System

In this paper a control scheme to improve power quality as well as voltage synchronization of self-excited induction generator (SEIG) driven by wind turbine feeding power to a remote area has been proposed. In practice, Neuro-Fuzzy PI (NFPI) controlled hybrid static compensator (STATCOM)is used to control voltage and frequency regulation of SEIG. The NFPI controlled STATCOM is used to balance the reactive power requirement of SEIG under load perturbation and tries to make terminal voltage constant. Integration of STATCOM with BESS is done to make a hybridized STATCOM system to enhance the active power capability of traditional STATCOM. This helps to offset the deviation resulted in both active and reactive power thus enhancing active power capability, reliability and system stabilization. The dynamic model of Hybrid STATCOM using stationary two-axis theory is developed for analyzing system behavior. Simulated results show improved power quality and performance of the system under variable loading condition in stand-alone application.
A Multi Modal People Tracker for Real Time Human Robot Interaction

Tracking people in the surroundings of interactive service robots is a topic of high interest. Even if image based detectors using deep learning techniques have improved the detection rate and accuracy a lot, for robotic applications it is necessary to integrate those detections over time and over the limited ranges of individual sensors into a global model. That data fusion enables a continuous state estimation of people and helps reducing the false decisions taken by individual detectors and increasing the overall range. In this paper, we present a tracking framework with a new distance measure for data association and a proper consideration of individual sensors’ accuracies. By means of that, we could deal with high false detection rates of laser-based leg detectors without introducing further heuristics like a background model. The proposed system is compared to other tracking approaches from the state of the art. Furthermore, we present a novel manually annotated benchmark dataset for multi sensor person tracking from a moving robot platform in a guide scenario, which will be made publicly available.
Hand-held non-invasive NIR device for early stage breast cancer pre-screening

We present a hand-held, low-cost, non-invasive and non-ionizing near-infrared (NIR) light-based pre-screening devices for early stage breast cancer. The pre-screening of breast cancer in women at risk can improve the prognosis of patients, and such a solution should complement the existing diagnostic approaches. Our design is based on the principle of amplitude cancellation of two anti-phase NIR light-emitting diodes (LEDs) operated at a wavelength of 840nm placed at a fixed distance from a photodetector in the middle. The device has been tested on breast phantom made of Acetal Resin Rod. The design functionality has been achieved from implemented prototype and has been verified by several experiments resulting in detection of a non-homogenous object inside the phantom. This proposed design can be further improved by integration with efficient probe-based algorithm, which will show the presence of abnormalities in the human breast in an automated procedure. This device can provide a practical, low-cost solution for massive breast-cancer pre-screening campaigns, especially in developing countries.
A Standardized ICS Network Data Processing Flow With Generative Model in Anomaly Detection

Industrial control systems (ICS) now usually connect to Wireless Sensor Networks and the Internet, exposing them to security threats resulting from cyber-attacks. However, detecting such attacks is non-trivial task. The high-dimensional network data pose significant challenges on security anomaly detection. In this work, we propose a network flow data processing method, which can make the complex network data more standardized and unified to assist security anomaly detection. Then, data generation method is applied to collect enough training data. We also propose a evaluation method for generated data. Finally, the bidirectional recurrent neural networks with attention mechanism is proposed to extract the latent feature, and give an explainable results in identifying the dominant attributes. Empirical results show our method outperforms the state-of-the-art models.
Improving Packet Flow Counting With Fingerprint Counting

In many applications, there is a need to estimate the frequency of elements. For example, in networking to know the number of packets of each flow. This poses a challenge as the number of flows and packets per second can be very large and therefore an exact count would require a large amount of fast memory. In those cases, an alternative is to use data structures, commonly referred to as sketches, that provide an estimate of the frequency of elements using a much smaller amount of memory. For example, the Count Min Sketch (CMS) hashes each element to a few counters and returns as estimate the minimum value among them. The CMS in general overestimates the frequency of an element as other elements may also map to the same counters and increment them. In this letter, fingerprint counting, a scheme to reduce the counter overestimation is presented and evaluated. The main idea is to add a fingerprint to the counters and use it to check if consecutive increments to a counter belong to the same element. When they do not, the counters can be incremented by half a packet instead of a full packet thus reducing the overestimation. The evaluation results show that the proposed scheme is able to reduce the overestimation and improve the CMS accuracy. In more detail, the overestimation is reduced by more than 20% in many of the configurations tested reaching values over 50% in some cases. A scheme to encode the fingerprints in the counters that practically eliminates the additional memory required for the fingerprints is also presented. Therefore, the improvement in the accuracy is achieved with a negligible impact on the size of the memory needed to implement the CMS.
FPChecker: Detecting Floating-Point Exceptions in GPU Applications

Floating-point arithmetic is widely used in applications from several fields including scientific computing, machine learning, graphics, and finance. Many of these applications are rapidly adopting the use of GPUs to speedup computations. GPUs, however, have limited support to detect floating-point exceptions, which hinders the development of reliable applications in GPU-based systems. We present FPCHECKER, the first tool to automatically detect floating-point exceptions in GPU applications. FPCHECKER uses the clang/LLVM compiler to instrument GPU kernels and to detect exceptions at runtime. Once an exception is detected, it reports to the programmer the code location of the exception as well as other useful information. The programmer can then use this report to avoid the exception, e.g., by modifying the application algorithm or changing the input. We present the design of FPCHECKER, an evaluation of the overhead of the tool, and a real-world case scenario on which the tool is used to identify a hidden exception. The slowdown of FPCHECKER is moderate and the code is publicly available as open source.
Color Point Cloud Registration Based on Supervoxel Correspondence

With the development of RGBD sensors, the high-quality color point cloud can be obtained expediently. In this paper, we propose a novel registration method for 3D color point clouds from different views, which is a critical issue in many applications. Different from traditional feature-based methods, we design a hybrid feature representation with color moments of the point, which could be applied naturally for any color point cloud. And these features are extracted from point clouds based on the supervoxel segmentation. By jointly conducting these features for similarity measure, a weight parameter is dynamically adapted between the color and the spatial information. The registration algorithm is under a classic iterative framework for building the correspondence and estimating transformation parameters. In addition, we provide a mutual correspondence matching condition with hybrid features to build some more robust relationships for estimating transformation parameters. Experimental results demonstrate that our method can effectively reduce the number of point data for registration and achieve good matching results even in a poor initial condition.
How Much Modern Computational Graph Theory Can Contribute to the Weight of a Political Coalition

It is hard to overestimate the importance of a strong and integral political coalition. In simple cases, one can easily construct a coalition: just start from yourself and iteratively propose the alliance to the most influential political forces from those that do not contradict with the previously associated members. However, the more complex a society becomes (both in the number of political forces and in the number of questions they have principal opinions on), the more difficult it turns out to make coalitions both strong and solidary. From a mathematical point of view, the coalition problem can be formulated in terms of the well-known Maximum Weighted Clique Problem (MWCP). Recent decades resulted in a significant progress in the solution of large and complex MWCPs. In the present paper, the abstract coalition problem is written in the form of MWCP and attacked by two state-of-the-art solvers. The computational experiments show a significant gain in the weight of the resulting coalitions in comparison with the coalitions constructed by the human-intuitive greedy approach.
6DoF Pose Estimation with Object Cutout based on a Deep Autoencoder

The six degree-of-freedom (6DoF) pose estimation is an important task in Augmented Reality, especially for initializing or recovering from the failure of 3D tracking for the textureless object, since it still encounters the insufficient accuracy problems because of cluttered backgrounds, occasionally quick movements, and other factors. We propose a simple but effective method to cutout the interested textureless object in a single RGB image with clear contour, which can be employed to directly estimate 6Dof poses with relatively high precision, and then help to remove most of the disturbing edges of clutter background for further refinement of pose estimation. To achieve this task, we propose a novel convolutional neural network, similar to an autoencoder, to reconstruct arbitrary scenes containing the object of interest, and extract the object area. We evaluated our method on objects from the LINEMOD dataset, and the results show that our approach is superior to the baseline as well as some advanced methods.
A Fast Response Flexible Humidity Sensor based on PTFE Micropore Substrate

With the improvement of sensing technology, flexible or stretchable humidity sensors have become a new subject in research. However, most of the flexible humidity sensors reported showed a low response of hundreds of seconds. In this paper, a fast response flexible humidity sensor based on poly tetra fluoroethylene (PTFE) micropore substrate is reported. Interdigited electrodes were fabricated on the PTFE substrate with conductive silver paste by screen-printing process. And graphene oxide dispersion was dropped to develop the sensitive layer. The morphologies of PTFE substrate and GO layers were characterized by scanning electron microscopy (SEM). energy dispersive spectrometer (EDS) analysis showed the high oxidation degree of GO layers on the sensors. The impedance of humidity sensor was measured under different relative humidity (RH) levels. The results showed the impedance modulus decreasing as a function of humidity, with a low sensitivity of 1. 23kΩ/%RH at low RH levels and high sensitivity of 11kΩ/%RH at high RH levels. The impedance phase was also sensitive to humidity variation, which increased linearly with the sensitivity of 1.13°/%RH. The response and recovery time was measured as 5.5s and 12s, respectively, which increased to 9s and 16s with the micropores covered in comparison. The fast response and good sensitivity of the humidity sensor showed its potential applications in biomedical and wearable sensing.
A Novel Method to Estimate the Position of a Mobile Robot in Underfloor Environments Using RGB-D Point Clouds

This paper is focused on the design of a mobile robot whose objective is to apply thermal insulation spray in underfloor voids, to improve the energy efficiency of buildings. Solving robustly the mapping and localization problems is crucial to achieve a high degree of autonomy during the development of this task. Nevertheless, underfloor voids constitute specially challenging environments mainly owing to the extreme unevenness of the terrain and the changes the environment experiences as the insulation process is carried out. Taking these issues into account, this work presents the implementation of the localization module of the robot, which is equipped with a laser scanner and an RGB (Red, Green and Blue) camera. The data captured by both sensors is combined to build point clouds that describe the appearance of the environment. While the robot traverses the a priori unknown environment, several point clouds are built and an alignment between each pair of consecutive clouds is carried out. From this information, the current position of the robot is estimated with respect to the previous one. The method has been tested with several datasets captured in real underfloor environments (building crawl spaces) and under real operating conditions.