Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Point-of-Care Assessment of Platelet Count-induced Changes in Whole Blood Coagulation with a Dielectric Microsensor

This paper reports on the assessment of changes in coagulation dynamics of human whole blood induced by depletion of platelet count using a microfluidic dielectric sensor. The sensor, termed ClotChip, is based on a three-dimensional, parallel-plate, capacitive sensing structure with sensing and floating electrodes integrated into a microfluidic channel. Whole blood samples with quantitative platelet defects are created ex vivo by mixing plasma samples having different platelet counts with red blood cells at a hematocrit level of 0.5. Using an impedance analyzer, the ClotChip readout is shown to be sensitive to changes in the coagulation process dynamics arising from the depleted platelet count. ClotChip performance is benchmarked against rotational thromboelastometry (ROTEM), a clinically important assay for whole blood coagulation. The ClotChip readout parameter, Δεr,max, exhibits a strong positive correlation (r = 0.75 P < 0.001, n = 28) with the ROTEM maximum clot firmness (MCF) parameter. We also demonstrate that the depletion of platelet count affects the time of coagulation onset captured by both the ClotChip and ROTEM assays. This work establishes the ClotChip potential as a point-of-care (POC) platform for rapid assessment of the platelet count-induced changes in coagulation dynamics using only 9µL of whole blood.
Low Power Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability Needs

Low-power wide area (LPWA) technologies are strongly recommended as the underlying networks for Internet of Things (IoT) applications. They offer attractive features, including wide-range coverage, long battery life, and low data rates. This paper reviews the current trends in this technology, with an emphasis on the services it provides and the challenges it faces. The industrial paradigms for LPWA implementation are presented. Compared with other work in the field, this paper focuses on the need for integration among different LPWA technologies and recommends the appropriate LPWA solutions for a wide range of IoT application and service use cases. Opportunities created by these technologies in the market are also analyzed. The latest research efforts to investigate and improve the operation of LPWA networks are also compared and classified to enable researchers to quickly get up to speed on the current status of this technology. Finally, challenges facing LPWA are identified and directions for future research are recommended.
Detection and Elimination of Spyware and Ransomware by Intercepting Kernel-Level System Routines

Spyware is the most complex, obfuscated, and targeted class of malware, which has grown dramatically in recent years. Spyware is designed for secret, long-term, and persistent missions. This paper provides a novel method for detection, tracking, and confronting the stealth and obfuscated spyware and ransomware, including keyloggers, screen recorders, and blockers. The proposed method of this paper is based on a dynamic behavioral analysis through deep and transparent hooking of kernel-level routines. We used linear regression, JRIP, and J48 decision tree algorithms as a classifier to recognize three classes of malware. This paper presents the main architectural plan of an anti-spyware application to track spyware footprints in order to detect and force terminate running processes, eliminate executable files, and restrict network communications. The efficiency of the proposed method was evaluated from the viewpoint of accuracy in detecting real-world samples of spyware by ROC curve analysis and from the viewpoint of success rate to confront effectively with active spyware. Our proposed method was able to recognize spyware with an accuracy of about 93% and an error rate near 7%. In addition, the proposed system can disinfect an operating system from infection by spyware with a hit rate of about 82%.
TOA Estimation of Chirp Signal in Dense Multipath Environment for Low-Cost Acoustic Ranging

In this paper, a novel time of arrival (TOA) estimation method is proposed based on an iterative cleaning process to extract the first path signal. The purpose is to address the challenge in dense multipath indoor environments that the power of the first path component is normally smaller than other multipath components, where the traditional match filtering (MF)-based TOA estimator causes huge errors. Along with parameter estimation, the proposed process is trying to detect and extract the first path component by eliminating the strongest multipath component using a band-elimination filter in fractional Fourier domain at each iterative procedure. To further improve the stability, a slack threshold and a strict threshold are introduced. Six simple and easily calculated termination criteria are proposed to monitor the iterative process. When the iterative “cleaning” process is done, the outputs include the enhanced first path component and its estimated parameters. Based on these outputs, an optimal reference signal for the MF estimator can be constructed, and a more accurate TOA estimation can be conveniently obtained. The results from numerical simulations and experimental investigations verified that, for acoustic chirp signal TOA estimation, the accuracy of the proposed method is superior to those obtained by the conventional MF estimators.
An Integration of Requirement Forecasting and Customer Segmentation Models towards Prescriptive Analytics For Electrical Devices Production

Material requirement planning is an essential role of a manufacturing business. Manufacturers need to find an effective way to manage material planning among the changes. This research is designed to create an integrated model of time series purchasing forecasting model and customer segmentation model in electrical equipment procurement for risk assessment and prescriptive model building. The methods used for forecasting are compared between Gradient Boosted Tree (GBT), Artificial Neural Network (ANN) and Decision Trees (DT) while the K-Means Clustering is selected to segment customers optimally. Henceforth, customers can be classified into three groups; Good, Moderate and Normal. The results of both methods are then used to generate a risk assessment matrix. Finally, the researcher analyze with the prescriptive analytics driven by the evolutionary optimization method to create a strategy and allocate parts which align to customer behaviour and according to the company policy.
Recurrent Neural Network Based Bitcoin Price Prediction by Twitter Sentiment Analysis

The sentiment in Twitter about Bitcoin have direct or indirect influence on overall market value of the Bitcoin. This research is concerned with predicting the volatile price of Bitcoin by analyzing the sentiment in Twitter and to find the relation between them. The tweets of Bitcoin collected from different news account sources are classified to positive or negative sentiments. The obtained percentage of positive and negative tweets are feed to RNN model along with historical price to predict the new price for next time frame. The accuracy for sentiment classification of tweets in two class positive and negative is found to be 81.39 % and the overall price prediction accuracy using RNN is found to be 77.62%.
UNiS: A User-space Non-intrusive Workflow-aware Virtual Network Function Scheduler

Network Function Virtualization (NFV) has gained a significant research interest in both academia and industry since its inception in the late 2012. One of the key research issues in NFV is the development of systems for building Virtual Network Functions (VNFs) capable of meeting the performance requirements of enterprise and telecommunication networks. New packet processing models leveraging kernel bypass I/O and poll-mode processing have gained popularity for building high performance VNFs because of their simple programming model and very low I/O overhead. However, a major drawback of such poll-mode processing is the inefficient use of CPU resources. Existing CPU schedulers are ill-suited for VNFs due to their inability to capture the actual processing cost of a poll-mode VNF, hence, cannot rightsize the CPU allocation. This is further exacerbated by their inability to consider VNF processing order when VNFs are chained to form Service Function Chains (SFCs). The state-of-the-art solutions proposed for VNF scheduling are intrusive, i.e., requiring the VNFs to be built with scheduler specific libraries or having carefully selected scheduling checkpoints. This highly restricts the VNFs that can properly work with such schedulers. In this paper, we present UNi S: a User-space Non-intrusive work-flow aware VNF Scheduler. Unlike existing approaches, UNi S does not require VNF modifications and treats the poll-mode VNFs as a black box, hence, is non-intrusive. UNi S is also workflow-aware, i.e., maintains SFC processing order while scheduling the VNFs. Testbed experiments show that UNiS is able to achieve a throughput within 90% (for synthetic traffic load) and 98% (for real data center traffic trace) of the achievable throughput using an intrusive co-operative scheduler.
Fuzzy Clustering for Misbehaviour Detection in VANET

Security in Vehicular Ad Hoc Networks (VANETs) has become a hot research area due to its life-saving characteristic. It mainly depends on the reliability of data exchanged by vehicles (sent, received, forwarded) that includes crucial information such as geographic position, speed, direction, etc. Indeed, several security threats might affect the reception of data itself by dropping, altering or delaying the exchanged messages. In this paper, we present a lightweight misbehavior detection schema for detecting and identifying selective forwarding attackers in VANETs for geographic routing protocol (GPSR). According to this attack, malicious vehicles behave like normal nodes but selectively drop messages. Identifying such attacks is challenging and sometimes impossible. Our designed schema adopts a fuzzy clustering mechanism called Fuzzy-MDS to differentiate malicious vehicles from legitimate vehicles. It initially establishes a preprocessing phase that allows retrieving a set of features from a trace file generated by running the simulation. Based on extracted features, Fuzzy-MDS uses FC-Means algorithm to get clusters of normal and attacker nodes. To enhance the efficiency of our detection schema, a decision threshold is tuned to well identify the misbehaving vehicles. Our preliminary simulation results and analysis show that an accuracy of 87% could be achieved with a low false positive rate under attack’s intensity of 25% of the total number of nodes.
Rewrite Rules for Debugging Student Programs in Programming Tutors

Data-driven intelligent tutoring systems learn to provide feedback based on past student behavior, reducing the effort required for their development. A major obstacle to applying data-driven methods in the programming domain is the lack of meaningful observable actions for describing the students problem-solving process. We propose rewrite rules as a language-independent formalization of programming actions in terms of code edits. We describe a method for automatically extracting rewrite rules from students program-writing traces, and a method for debugging new programs using these rules. We used these methods to automatically provide hints in a web application for learning programming. In-class evaluation showed that students receiving automatic feedback solved problems faster and submitted fewer incorrect programs. We believe that rewrite rules provide a good basis for further research into how humans write and debug programs.
Recognizing Facial Sketches by Generating Photorealistic Faces Guided by Descriptive Attributes

Recognizing or retrieve a face based on its sketch-photo similarity has important applications in law enforcement and public security. While many existing methods focus on recognizing facial sketch from image-based queries, this setting has the major limitations in practice, since the abstract sketch only captures sparse global structure of the human face. To address this issue, in this paper, we propose to bridge the gap between the sketch-photo pair by “translating” the abstract visual sketch into a photorealistic face with the help of descriptive attributes. Specifically, we propose an improved multi-modal conditional generative adversarial network (MMC-GAN) to jointly utilize the complementary information of visual sketches and semantic facial attributes to reduce the uncertainties of the facial image generation. A fusion network is introduced to better leverage the information from different modalities (visual sketch and semantic attributes). In order to improve the details of the generated facial images, we adopt a two-path generator structure in which the global feature and the local feature of human faces are learned in parallel. An identity-preserving constraint is further introduced to enhance the identity consistency between the sketches and facial images. Extensive experiments demonstrate that we can effectively manipulate the face image generation by varying the input facial attributes. Besides, the generated photorealistic face image is validated to improve the sketch-photo face recognition and retrieval.
Computational stereo-vision model of proto-object based saliency in three-dimensional space

Our visual system deals with a three-dimensional space but most of the current saliency models do not include depth information. We extend a proto-object based model to show how stereoscopic depth perception changes the ability to predict saliency on natural scenes. Results show the ability to mark depth discontinuities and a promising statistical improvement.
Optimal Frequency Controller Parameters of Wind Turbines Participating System Frequency Control

Control of variable speed wind turbines to participate system frequency control has become a new requirement for wind turbines in a high penetrated wind farm power system. This paper focuses on selection of the frequency controller parameters of the wind turbines to improve the frequency control effect. Frequency control method of the wind turbines is presented in this paper. Considering the system frequency response, the synthetic frequency response model is built, a multi-objective function of evaluating the effect of system frequency response is proposed, then genetic algorithm is used to determine the optimal frequency controller parameters. The performance of the frequency control effect with optimal frequency controller parameters of the wind turbines is validated in Matlab/Simulink.
Polymer Optical Fiber-Based Sensor System for Smart Walker Instrumentation and Health Assessment

This paper presents a polymer optical fiber (POF)-based sensor system for smart walker (SW) instrumentation. The system comprises an oximetry sensor working in reflectance mode, which is embedded in a 3D printed handle for the SW. In addition, there is also a POF-based smart textile that can be integrated in the users’ clothes and is placed on their chest. In this case, the smart textile can detect the displacement caused by the respiration, vibration due to heartbeat and bending induced in the gait. Results show that the proposed sensor system can detect the oxygen saturation, breathing rate, gait cadence, and heart rate with errors of about 1%. In addition, it is proposed an architecture for the SW in which the POF-based sensor system can be applied not only for remote healthcare applications, but also as sensor feedback in cloud robotics for some control paradigms for human–robot interaction. Furthermore, the proposed instrumentation presents the possibility of novel SW control, where the health conditions of the user are considered in the human–robot interaction.
MIMO Cable Guided Radar

Multiple Input Multiple Output (MIMO) cable guided radar presents a true paradigm shift in leaky coaxial cable sensing. Recent technology advancements in Software Defined Radio (SDR) allow this covert terrain following radar to provide reliable detection and pinpoint the location of intruders as they cross the perimeter. MIMO operation enables the use of end-to-end correlation (patents pending) using four times the data used by traditional leaky coaxial cable sensors. Utilizing contra-directionally coupled and co-directionally coupled responses from both ends of the cables, end-to-end correlation doubles the location accuracy for a given bandwidth and reduces sensitivity to RF interference (mutual and 3rd party) – a common problem with other leaky coaxial cable sensors. Since the noise created by nearby metallic fences is a form of multipath, end-to-end correlation suppresses this common source of nuisance alarms. In addition, end-to-end correlation eliminates the need for costly “graded” cables. The use of standard leaky coaxial cables like those made in large quantity for use in communications avoids much of the wasted cable associated with graded cables and it simplifies installation and repair of the cables. These cables are designed for direct burial in many types of mediums. A cable plough can be used in many types of soil as opposed to the traditional trench and backfill approach. The cost savings in using a plough can be dramatic. The co-directionally coupled responses are used to enhance the detection process as well as to communicate response data from one processor to the next. In addition to data, a novel “split-rail” power distribution over the sensor cables minimizes the need for costly infrastructure. Since the cables are in fact the sensor, the cable and processors creates a secure power and data network around the perimeter. Applications include; prisons, nuclear power plants, military sites, VIP residences and borders.
Time-Aware QoS Prediction for Cloud Service Recommendation Based on Matrix Factorization

Prediction of quality of service (QoS) is a critical area of research for cloud service recommendation. The disadvantage of QoS values is that they are directly related to time series of service status and network condition and thus instantly vary over time. The main contribution of this paper is to consider service invocation time as a dynamic factor in the collaborative filtering model and recommend high-quality services for target user. In particular, this paper proposes a time-aware matrix factorization (TMF) model that integrates QoS time series to provide two-phase QoS predictions for cloud service recommendation. The TMF model uses an adaptive matrix factorization model on a sparse QoS dataset to predict the missing QoS values. A temporal smoothing method is then developed and applied to the predicted result to perform the time-varying QoS prediction that accounts for the dependence of QoS values at different time intervals. The numerical experiments presented are conducted to validate the accuracy of the proposed method on a public QoS dataset.
Enhancing cost performance using Symbiotic Organism Search based algorithm in Cloud

In these last years, cloud computing is getting more and more popular due to the height development in telecommunication technologies. Which resulted the importance of the resource allocation topic in order to enhance the quality of service (QoS) given to the clients. However, provisioning of services at reasonable prices is the main challenge facing cloud computing today. This, led cloud providers to integrate a new scheduling techniques to overcome this issue. In this paper, we highlight in particular the problem of resource reservation cost. So that, we improve a metaheuristic approach based on Symbiotic Organism Search Algorithm (ISOS). It is a newly developed approach for solving numerical optimization problems. We specifically designed ISOS to minimize the cost of running tasks on virtual machines (VMs). Simulation results revealed that ISOS gave a better result compared to MAX-min, FCFS, PSO and MOGA methods.
Luenberger Observer Based Current Estimated Boost Converter for PV Maximum Power Extraction-A Current Sensorless Approach

A continuous change in the solar irradiance level and local atmospheric conditions, such as temperature and humidity, necessitate the use of maximum power point tracking (MPPT) technique of a photovoltaic (PV) system. MPPT ensures to harvest maximum power. Generally, voltage and current sensors are required to track the power of a PV system. The proposed method tracks the power of a PV system without the use of any current sensor. It not only reduces the cost of the system, but also reduces the communication burden, internal disturbances, and complexity. The proposed scheme estimates the current internally by using Luenberger observer technique. Thus, the estimated current is given to perturb and observe based MPPT technique, to generate the required duty for a dc–dc boost converter. The proposed scheme is experimentally investigated, and thus, obtained results are compared with well-established MPPT techniques. A comparison shows that the proposed system results are in a good agreement with the current sensor based MPPT technique.
Fully Integrated Active Quenching Circuit Driving Custom-Technology SPADs With 6.2-ns Dead Time

The minimization of the dead time necessary for the operation of a single-photon avalanche diode (SPAD) plays a crucial role in many demanding single-photon applications. Among these, it is worth mentioning the implementation of airborne light detection and ranging systems exploited to scan the terrain topography through semiporous obscurations. In this letter, we present the development and the experimental characterization of a fully integrated active quenching circuit able to drive custom-technology SPADs with a dead time as low as 6.2 ns, corresponding to a maximum photon count rate of more than 160 Mcps. Thanks to the use of a high-voltage CMOS fabrication technology, the circuit is able to operate also SPADs that require an excess bias of few tens of Volts, like the recently developed red-enhanced SPAD up to a maximum photon count rate of 100 Mcps.
Automatic monitoring of honeybees’ activity outside of the hive from UHD video

Studying the behavior of social insect using computer vision algorithms is an interesting topic for both biological and signal processing communities. One of the most interesting aspects in the field is tracking of honeybees. Regarding computer vision method, honeybees’ behavior has been mostly monitored inside and at the entrance of the hive. In this research we are proposing the method for automatic monitoring of honeybees’ activity outside of the hive. Experiments showed that the activity of honeybees outside the hive can estimated using an ultra-high definition video captured with UAV from distance of 10 meters. Specific spots where honeybees are gathered can be detected using heat maps which represent the density of their occurrence in the observed time interval.
A cross-shaped MIMO reconfigurable dielectric resonator antenna for GSM and LTE/UMTS applications

In this work a MIMO Reconfigurable Dielectric Resonator antenna (DRA) is proposed. The antenna comprises of a cross-shaped dielectric resonator, fed by two orthogonal probes. Adjacent to both probes, two rectangular micro strip patches are stacked to the walls of DRA to enhance the return loss and improve the impedance matching. Two C-shaped deformations are made on the ground plane. Four PIN-Diodes are located on the C-shaped deformations. Re-configurability is achieved when PIN-diodes are switched OFF or switched ON. The re-configurability is attained for two resonance frequencies. For the configuration I, the antenna resonates at 1.84 GHz but the overall bandwidth covers GSM 1900 MHz. For the configuration II, the antenna resonates at 2.08 GHz but the total bandwidth includes frequencies, which can be used for LTE/UMTS 2100 MHz applications. The antenna performance is analyzed in terms of simulated return loss, radiation pattern, diversity gain and envelope correlation function. Simulated results confirm that this reconfigurable MIMO system has promising values for the mentioned parameters.