Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling sensory integration for ankle- and hip control in human balance

Falling is a problem in elderly, affecting 30% of people over 65 years old, and leading to serious injuries in 10% of the cases. Because balance is controlled in a closed loop system with a high level of redundancy, the cause of impaired balance control is often unknown. A balance control model in combination with perturbed balance experiments could be a useful aid to detect the underlying cause of balance impairment, which would allow for better treatment. A disturbance estimation and compensation (DEC) model has a strong neurological basis and is able to describe multiple conditions with one set of parameters. An existing DEC model, containing separate ankle and hip control, was extended by adding visual and mechanical disturbance estimations. The model was fitted to experimental data of elderly subjects maintaining balance in three conditions containing four disturbances simultaneously; visual and ankle proprioceptive perturbations and mechanical perturbations at the hip and shoulder level. To validate the fitting procedure, the model parameters were also fitted to data simulated by the model, and a sensitivity analysis was done.Being fitted to experimental data, the model could reproduce about 60% of the behaviour. Mainly the low frequency behaviour could not fully be tracked. Parameter estimation proved challenging, with some parameters consistently estimated larger or smaller than expected or physiologically plausible. Fitting the model on its own simulated data could reproduce g99.9% of the behaviour, but with some parameters consistently estimated up to a factor 10 too small or too large. Many adaptations and extensions to the existing DEC model were made simultaneously and with the available experimental data it was not possible to investigate the individual influence of each adaptation. If a broader range of experimental data is available, with conditions where only one, two or three perturbations are applied, it is possible to validate each model adaptation separately. This would likely improve the model. With the suggested improvements, the multisegmental DEC model opens up the way towards better treatment for balance impairment.
A Data-Driven Approach for Network Intrusion Detection and Monitoring Based on Kernel Null Space

In this study, we propose a new approach to determine intrusions of network in real-time based on statistical process control technique and kernel null space method. The training samples in a class are mapped to a single point using the Kernel Null Foley-Sammon Transform. The Novelty Score are computed from testing samples in order to determine the threshold for the real-time detection of anomaly. The efficiency of the proposed method is illustrated over the KDD99 data set. The experimental results show that our new method outperforms the OCSVM and the original Kernel Null Space method by 1.53% and 3.86% respectively in terms of accuracy.
Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.
A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO

One of the major challenges in implementing wireless virtualization is the resource discovery. This is particularly important for the embedding-algorithms that are used to distribute the tasks to nodes. MARVELO is a prototype framework for executing different distributed algorithms on the top of a wireless (802.11) ad-hoc network. The aim of MARVELO is to select the nodes for running the algorithms and to define the routing between the nodes. Hence, it also supports monitoring functionalities to collect information about the available resources and to assist in profiling the algorithms. The objective of this demo is to show how MAVRLEO distributes tasks in an ad-hoc network, based on a feedback from our monitoring tool. Additionally, we explain the work-flow, composition and execution of the framework.
Integration and testing of a digital transceiver for a dual frequency, pulse-doppler radar

This dissertation focuses on the development of a digital transceiver system for a dual-band, polarimetric radar, which is to form part of the NeXtRAD multistatic radar. NeXtRAD is being developed as an instrument for research into the behaviour of clutter and targets as observed by multistatic radars. The Pentek Cobalt model 71621 software defined radio interface was procured for use as the digital transceiver in the system. The goal was to develop the software needed to use this product as the digital transceiver in a prototype version of the NeXtRAD active node, and to ensure that it could be readily integrated with other subsystems in the final system. The active node is essentially a monostatic pulse-doppler radar. Laboratory tests of the transceiver showed that it was possible to generate and digitize pulsed waveforms at a 125 MHz intermediate frequency which is used by the existing receiver exciter in the system. After extensive laboratory testing and development, phase coherent waveform generation and multichannel digitization was achieved. A low transmit power version of the active node was constructed and tested at both operating frequencies. Equipment used in the testing and development of the digital transceiver included laboratory signal generators, spectrum analyzers and oscilloscopes. The digital transceiver was able to function at pulse repetition rates exceeding 2 kHz, with a single transmit channel and three receive channels active. The lowpowered monostatic prototype system was constructed to test the digital transceiver using a receiver exciter subsystem, RF amplifiers and antennas. This prototype radar was used to take measurements of targets at ranges below 300 m and successfully detected reflections from large structures. Cars and pedestrian traffic were detected by their doppler shifts at both Land X-band frequencies. The detection of moving and stationary targets confirmed the suitability of the digital transceiver for use in the envisioned multistatic radar system.
IDEA: Intrusion Detection through Electromagnetic-Signal Analysis for Critical Embedded and Cyber-Physical Systems

We propose a novel framework called IDEA that exploits electromagnetic (EM) side-channel signals to detect malicious activity on embedded and cyber-physical systems (CPS). IDEA first records EM emanations from an uncompromised reference device to establish a baseline of reference EM patterns. IDEA then monitors the target device's EM emanations. When the observed EM emanations deviate from the reference patterns, IDEA reports this as an anomalous or malicious activity. IDEA does not require any resource or infrastructure on, or any modification to, the monitored system itself. In fact, IDEA is isolated from the target device, and monitors the device without any physical contact. We evaluate IDEA by monitoring the target device while it is executing embedded applications with malicious code injections such as Distributed Denial of Service (DDoS), Ransomware and code modification. We further implement a control-flow hijack attack, an advanced persistent threat, and a firmware modification on three CPSs: an embedded medical device called SyringePump, an industrial Proportional-Integral-Derivative (PID) Controller, and a Robotic Arm, using a popular embedded system, Arduino UNO. The results demonstrate that IDEA can detect different attacks with excellent accuracy (AUC > 99.5%, and 100 percent detection with less than 1 percent false positives) from distances up to 3 m.
Multi-objective optimal dispatching of microgrid based on improved genetic algorithm

Aiming at the optimization problem of micro-grid energy dispatch, the environmental benefit, system operation and maintenance cost, start-stop cost and main grid electricity price are proposed as the objective function, and the influence of the actual operation constraints of micro-grid system and the distributed non-scheduled new energy are taken into account at the same time. The multi-objective optimal dispatching model of microgrid is constructed, and the model is solved based on improved genetic algorithm. The numerical simulation results show that compared with the traditional single-target scheduling method of microgrid, the optimal scheduling method of this paper is beneficial to the micro-grid in the process of scheduling decision-making to achieve the overall coordination of economic, environmental and social benefits of microgrid.
Household Power Consumption Forecasting using IoT Smart Home Data

The use of the forecasting system is becoming more prominent in recent years. One of the implementations of the forecasting system is to predict electricity consumption demand. In this paper, we have developed a forecasting system for household electricity consumption using a well-known Extreme Gradient Boosting algorithm. We utilized time-series data from a smart meter dataset to make a predictive model. First, we evaluated the importance of time-series feature from the dataset and resampled the original dataset. Then, we used the resampled data to train the model and calculated training loss function. Our experimental studies with real IoT Smart Home data demonstrate that our forecasting system works well with small dataset using one-hour downsampling on the dataset.
Secure mmWave Communication Using UAV-Enabled Relay and Cooperative Jammer

Communication assisted by unmanned aerial vehicles (UAVs) has been regarded as an effective technique for reliability improvement in both military and civilian domains, whereas it also makes the information vulnerable to passive eavesdropping due to its wide broadcast. In this paper, we investigate the secure millimeter wave (mmWave) communication assisted by multiple UAV-enabled relays and jammers, where exist multiple randomly distributed eavesdroppers on the ground. Leveraging the models of 3D-antenna gain and stochastic geometry, new closed-form expressions of secrecy outage probability are derived on the basis of the opportunistic relay selection scheme involving the characteristics of air-to-ground channel, and the secrecy improvement is demonstrated when the relay density increases. In addition, a cooperative jamming scheme, where a part of UAVs transmit the jamming signals, is designed to degrade the qualities of eavesdropping channels and further enhance physical layer security. The simulation results show the impacts of different system parameters on secrecy outage probability and verify our analysis. It’s also revealed that there exist the optimal attitude of UAVs, jamming power and density of jammers for achieving the best secrecy performance.
Human-like trapezoidal steering angle model on two-lane urban curves

With the rapid development of automated vehicles, there is currently a significant amount of automated driving research. Giving automated vehicles capabilities similar to those of experienced drivers will allow them to share the road harmoniously with manned vehicles, especially on two-lane urban curves. To represent the steering behavior of experienced drivers, a series of curve feature distances are proposed, which is determined by multi-regression. These series of curve feature distances are used to generate a trapezoidal steering angle model which imitates the steering behavior of the experienced test drivers. To verify the feasibility and human-likeness of the proposed trapezoidal steering angle model, the model is used with constant vehicle speed to plan a human-like trajectory which is tracked using model predictive control. The simulation results show that the proposed trapezoidal steering angle model is human-like and could be used to give automated vehicles human-like driving capability when driving on two-lane curves.
Analog hardware security and hardware authentication

Hardware security and hardware authentication have become more and more important concerns in the manufacture of trusted integrated circuits. In this dissertation, a detailed study of hardware Trojans in analog circuits characterized by the presence of extra operating points or modes is presented. In a related study, a counterfeit countermeasure method based upon Physically Unclonable Function (PUF) authentication circuits is proposed for addressing the growing proliferation of counterfeit integrated circuits in the supply chain. Most concerns about hardware Trojans in semiconductor devices are based upon an implicit assumption that attackers will focus on embedding Trojans in digital hardware by making malicious modifications to the Boolean operation of a circuit. In stark contrast, hardware Trojans can be easily embedded in some of the most basic analog circuits. In this work, a particularly insidious class of analog hardware Trojans that require no architectural modifications, no area or power overhead, and prior to triggering, that leave no signatures in any power domains or delay paths is introduced. The Power/Architecture/Area/Signature Transparent (PAAST) characteristics help the Trojan “hide” and make them very difficult to detect with existing hardware Trojan detection methods. Cleverly hidden PAAST Trojans are nearly impossible to detect with the best simulation and verification tools, even if a full and accurate disclosure of the circuit schematic and layout is available. Aside from the work of the author of this dissertation and her classmates, the literature is void of discussions of PAAST analog hardware Trojans. In this work, examples of circuits showing the existence of PAAST analog hardware Trojans are given, the PAAST characteristics of these types of hardware
Testing the efficacy of 3D-printed geologic models as tools for fostering spatial visualization abilities

ABSTRACT Geoscience educators seek new technologies and teaching materials that will successfully foster their students’ spatial visualization skills. As 3D-printing technology becomes more readily accessible, there are endless opportunities to create new instructional products that can help improve students’ spatial rotation, spatial manipulation, and visual penetrative ability early in their geoscience coursework. We developed a set of eight 3D-printed geologic block models that can easily be incorporated into any 3-hr long geologic structures module of a physical geology laboratory course. Our main objective was to investigate their efficacy in fostering all three spatial visualization abilities. Our test results, based on 583 undergraduate participants at California State University, Fullerton, indicate that access to the 3D-printed geologic block models had insignificant effects on the development of spatial manipulation or visual penetrative abilities and a statistically significant negative impact on students’ spatial rotation skill development. In-class observations suggest that pattern-matching instead of actively practicing spatial rotation may have hindered the development of this skill, however, observations also suggest that the blocks generated discourse and improved explanations of geologic structures between students. We recommend that future adopters of 3D-printed educational products redesign their laboratory exercises that require active engagement and sketching using the models.
Distinguisher and non-randomness of Grain-v1 for 112, 114 and 116 initialisation rounds with multiple-bit difference in IVs

In this study, the authors construct two different distinguishers on Grain-v1 with 112 and 114 initialisation rounds. Their first distinguisher can distinguish Grain-v1 with 112 initialisation rounds from a uniform random source for 99% of the randomly chosen keys from full key space. The second one can distinguish Grain-v1 from a random source for 73% of the randomly chosen keys for one-fourth of the total key space (2 78 keys out of 2 80 keys). Our results improve upon the earlier distinguishers. The technique used for the distinguishers is conditional differential cryptanalysis. The existing works in this direction considered only one bit difference in the initialisation vector. However, for the first time, they could handle complicated conditions for the 2-bit difference to obtain better cryptanalytic results. Extending their technique by allowing the 1-bit difference in the pair of keys (i.e. related keys) and the 4-bit difference in IVs, they could observe the non-randomness till 116 initialisation rounds with a success in 62% cases.
Accurate likelihood inference for the volume under the ROC surface.

BACKGROUND With three ordered diagnostic categories, the volume under the receiver operating characteristic (ROC) surface, which is the extension of the area under the ROC curve for binary diagnostic outcomes, is the most commonly used measure for the overall diagnostic accuracy. For a continuous-scale diagnostic test, classical likelihood-based inference about the area under the ROC curve can be inaccurate, in particular when the sample size is small, and higher order inferential procedures have been proposed. AIM The goal of this paper is to illustrate higher order likelihood procedures for parametric inference in small samples, which provide accurate point estimates and confidence intervals for the volume under the ROC surface. METHODS Simulation studies are performed in order to illustrate the accuracy of the proposed methodology, and two applications to real data are discussed. RESULTS We show that likelihood modern inference provide refinements to classical inferential results. Furthermore, the freely available R package likelihoodAsy makes now their use almost automatic. CONCLUSION Modern likelihood inference based on higher-order asymptotic methods for the area under the ROC surface provide refinements to classical inferential results. A possible limitation of higher-order asymptotic methods for practical use is that their software implementation can be awkward. Nevertheless, use of the freely available R package likelihoodAsy makes such implementation straightforward.
Suggestive GAN for supporting Dysgraphic drawing skills

The squat competence of dysgraphia affected students in drawing graphics on paper may deter the normal pace of learning skills of children. Convolutional neural network may tend to extract and stabilize the action-motion disorder by reconstructing features and inferences on natural drawings. The work in this context is to devise a scalable Generative Adversarial Network system that allows training and compilation of image generation using real time generated images and Google QuickDraw dataset to use quick and accurate modalities to provide feedback to empower the guiding software as an apt substitute for human tutor. The training loss accuracy of both discriminator and generator networks is also compared for the SGAN optimizer.
Finger-Adaptive Illumination Control and Exposure Fusion Imaging for Biometric Finger Vein Recognition

Finger vein recognition is an emerging technique in biometrics. Capture of sufficient vein detail from a well-exposed image is crucial for its operation. We present a novel finger-adaptive illumination control algorithm and an exposure fusion based imaging procedure for finger vein imaging on a prototype device. The novel approach enables sub-second capturing of well-exposed images and shows significant improvements in match scores.
Nonintrusive multimodal attention detection

With the increasing deployment of computers in a wide variety of applications, the ability to detect the user’s attention, or engagement, is becoming more important as a key piece of contextual information in building effective interactive systems. For instance, one can imagine that a system that is aware of whether the user is attending to it would be able to adapt itself better to the user activities to enhance productivity. The ability to detect attention would also be useful for system analysis in designing and building better systems. However, much previous work in attention detection is either obtrusive or imposes demanding constraints on the context and the participants. In addition, most approaches rely on uni-modal signals, which are often limited in availability and stability. This paper attempts to address these two major limitations through a noninvasive multimodal solution, which allows participants to work naturally without interference. The solution makes use of common off-the-shelf items that could reasonably be expected of any computing environment and does not rely on expensive and tailor-made equipment. Using a three-class attention state setting, it achieves average accuracy rates of 59.63% to 77.81%; the best result being 77.81% for a general searching task, which shows 11.9% improvement over the baseline. We also analyze and discuss the contribution by individual features to different models. Keywords-Affective computing; keystroke dynamics; facial expression; multimodal recognition; attention detection
Efficient localization of target in large scale farmland using generalized regression neural network

Although simple to implement, the traditional trilateration technique is generally associated with significant location estimation errors because of highly nonlinear relationship between Received Signal Strength Indicator (RSSI) and distance. In case of agricultural farmland, there is always noise uncertainty in the RSSI measurements because of signal propagation issues such as NLOS, multipath propagation, and reflection. In the context of such environmental dynamicity, the localization algorithm must be efficient in terms of Localization Accuracy and Execution Speed to provide real‐time performance. The Generalized Regression Neural Network (GRNN) is a noniterative highly parallel neural architecture with the capability to get trained quickly using very few training samples. This paper introduces a range free GRNN localization algorithm as an alternative to the traditional range‐based trilateration technique for a large scale wheat farmland. This paper also presents the modified Optimal Fitted Parametric Exponential Decay Model (OFPEDM)‐based signal path loss model to deal with the issue of environmental dynamicity. The evaluation of localization performance of the trilateration and the proposed GRNN‐based approaches is carried out with the help of Wireless Sensor Network (WSN) using three path loss models, namely, Log Normal Shadow Fading (LNSM), Original OFPEDM, and proposed Modified OFPEDM. For all these implementations, the proposed GRNN algorithm demonstrates superior localization performance (localization accuracy of the order of few centimeters) over traditional trilateration irrespective of nonlinear system dynamics, path loss model, and environmental dynamicity. The execution speed of the proposed algorithm is of the order of few milliseconds.