Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband Terahertz Polarizing Beam Splitter Based on a Graphene-Based Defective One-Dimensional Photonic Crystal

Seeking operative terahertz (THz) devices has always stimulated considerable attention. Of particular interest is the THz beam splitter. Here, a tunable THz polarizing beam splitter (PBS) is proposed based on a graphene-embedded quarter-wave stack with a central defect layer of air. The spectral performance of the structure is investigated by the transfer matrix method. It is found that the electromagnetic waves can be decomposed into two separate polarized waves at incident angles greater than the critical angle. Furthermore, it is shown that a new kind of Brewster angle is found at the low THz frequencies due to the existence of the graphene nano-layers. The appearance of this angle which we call it the graphene induced Brewster angle results in the separation of TM- and TE- polarized waves at the low THz frequencies ($f< 2$ THz) in addition to the high THz region. It is shown that the working frequency range of PBS can be easily tuned by adjusting the width of the defect layer of air and also by tuning the chemical potential of graphene nano-layers via a gate voltage. The analysis of the proposed PBS is confirmed by the Finite Element Method (FEM) simulations that were performed with the commercial software COMSOL 5.2. Our investigations also reveal that the high transmission extinction ratio ($>200$ dB) for TM waves with frequency $f< 2$ THz is achieved by increasing the chemical potential to 0.5 eV. Moreover, this structure can exhibit extremely high extinction ratio for TE waves with high THz frequencies. Finally, the degree of polarization equals to one is reported for the PBS proposed here. This structure offers the opportunity to realize a high-efficiency PBS with very high extinction ratios at the broadband THz frequency.
Theoretical foundations for layered architectures and speed-accuracy tradeoffs in sensorimotor control

Nervous systems sense, communicate, compute, and actuate movement, using distributed hardware with tradeoffs in speed and accuracy. The resulting sensorimotor control is nevertheless remarkably fast and accurate due to highly effective layered architectures. However, such architectures have received little attention in neuroscience due to the lack of theory that connects the system and hardware level speed-accuracy tradeoffs. In this paper, we present a theoretical framework that connects the speed-accuracy tradeoffs of sensorimotor control and neurophysiology. We characterize how the component SATs in spiking neuron communication and their sensory and muscle endpoints constrain the system SATs in both stochastic and deterministic models. The results show that appropriate speed -accuracy diversity at the neurons/muscles levels allow nervous systems to improve the speed and accuracy in control performance despite using slow or inaccurate hardware. Then, we characterize the fundamental limits of layered control systems and show that appropriate diversity in planning and reaction layers leads to both fast and accurate system despite being composed of slow or inaccurate layers. We term these phenomena “Diversity Sweet Spots. ” The theory presented here is illustrated in a companion paper, which introduces simple demos and a new inexpensive and easy-to-use experimental platform.
Frequency Diversity versus Channel Training in Latency-Constrained Massive MIMO

The effect of correlation between neighboring resource blocks (RBs) in the outage probability performance of OFDM-based Massive MIMO systems is investigated. An upper bound on the outage probability, which is the relevant performance metric for latency-constrained communication, of two operations that exploit this correlation structure is derived and compared with the base scenario of orthogonal communication, where the correlation is ignored. It is observed that substantial outage probability improvement can be reaped already when moderate correlation is present. Closed-form upper and lower bounds on the investigated outage probability are derived. The bounds are shown to be tight for a wide range of system parameters and can be used to draw insights on the optimal design of latency-constrained Massive MIMO systems.
Edge-Based Shortest Path Caching for Location-Based Services

Shortest path queries on road networks are widely used in location-based services (LBS), e.g., finding the shortest route from my home to the airport through Google Maps. However, when there are a large number of path queries arrived concurrently or in a short while, an LBS provider (e.g., Google Maps) has to endure a high workload and then may lead to a long response time to users. Therefore, path caching services are utilized to accelerate large-scale path query processing, which try to store the historical path results and reuse them to answer the coming queries directly. However, most of existing path caches are organized based on nodes of paths; hence, the underlying road network topology is still needed to answer a path query when its querying origin or destination lies on edges. To overcome this limitation, we propose an edge-based shortest path cache in this paper that can efficiently handle queries without needing any road information, which is much more practical in the real world. We achieve this by designing a totally new edge-based path cache structure, an efficient R-tree-based cache lookup algorithm, and a greedy-based cache construction algorithm. Extensive experiments on a real road network and real point-of-interest datasets are conducted, and the results show the efficiency, scalability, and applicability of our proposed caching techniques.
Security Enhancement for Mobile Edge Computing Through Physical Layer Authentication

In this paper, we investigate the security threats in mobile edge computing (MEC) of Internet of things, and propose a deep-learning (DL)-based physical (PHY) layer authentication scheme which exploits channel state information (CSI) to enhance the security of MEC system via detecting spoofing attacks in wireless networks. Moreover, three gradient descent algorithms are adopted to accelerate the training of deep neural networks, which enables smaller computation overheads and lower energy consumptions. In addition, the maximum likelihood function of multi-user authentication method is derived, which explains why cross entropy is chosen as the loss function. The vectorization cost function is also derived. The mini batch scheme and $\ell _{2}$ regularization are adopted to improve training accuracy and avoid over-fitting, respectively. Moreover, the simulation and experimental results show that the DL-based PHY-layer authentication approaches can distinguish multiple legitimate edge nodes from malicious nodes and attacker by CSIs, effectively. Our proposed method supports a better performance compared with the traditional hypothesis test based method.
Gradient Based Optical Flow In Micro Aerial Vehicles

Achieving stability on drones and Micro Aerial Vehicles (MAVs) with only an Odometry sensor has always been hard and required several environmental conditions. Some of these conditions are reflection and texture of the surface. all of the current Odometry algorithms that are used on drones are feature-based which results in lower quality or even wrong estimations. This article propose a different algorithm that does not use feature-base algorithms and have a higher quality estimation on surfaces with similar textures and reflective surfaces. This approach can be used as a source to control MAV reach position stability in GPS-denied environments
Model-free Learning to Avoid Constraint Violations: An Explicit Reference Governor Approach

Constraints, including thermal, power, traction and rollover limits, as well as actuator range and rate limits, are ubiquitous in advanced ground vehicles and propulsion systems, and in their components, especially as these systems are downsized. These vehicles and systems will be operating in unknown environments where the recognition and avoidance of degradation or damage will be required. This paper proposes a model-free learning algorithm that over time modifies the parameters of an explicit reference governor (ERG) scheme so that violations of pre-specified constraints are avoided after a sufficiently informative learning phase. The ERG modifies setpoint commands to a nominal closed-loop system. Our learning algorithm modifies the ERG parameters based on observed constraint violations during a learning phase so as to eliminate constraint violations after learning is completed. Theoretical properties of the algorithm are analyzed and several examples that illustrate its effectiveness are presented.
A Study of UWB Microstrip Antenna Parameters for Wireless Applications

This paper presents design and simulation of a novel microstrip patch antenna in the ultra wide band frequency region (3.1 ⨯ 10.6GHz)and its modified versions with enhanced bandwidth on a FR408 substrate having relative permittivity of 3.75 and thickness of 1.5 mm respectively. The simulated antenna performance was obtained by Computer Simulation Technology Microwave Studio (CST MWS). The antenna performance was investigated by analyzing its return loss (S11), radiation pattern, voltage standing wave ratio (VSWR)parameters. The simulated S11 parameter has shown that antenna operates for Wi-Fi (3.6⨯3.7GHz), WiMAX (3.4⨯3.6, 3.7⨯4.2GHz), and Wireless Local Area Network (WLAN 5.3⨯6.3GHz).
A Novel Predictive Haptic Control Interface for Automation-to-Human Takeover of Automated Vehicles

Human driver's intervention is still needed before achieving fully autonomous driving, and this poses a great challenge for ensuring safety and smoothness during control transition from automation to human driver. This paper addresses this challenge by proposing a novel predictive haptic takeover control method. First, a new human-machine interaction model considering two phases, namely machine dominance and human dominance, is developed to describe the interactive behaviors between human driver and automation system during takeover. Based on the model built, a novel haptic takeover controller is designed by using model predictive control approach in order to achieve a safe and smooth handover transition. The optimal steering input is derived by considering the expected paths of both human driver and automation system. The takeover controller generates predictive haptic steering torque based on driver's states, guiding and assisting human driver to gradually resume manual control. Simulation is conducted under a typical driving condition with proposed method. Simulation results indicate that the automation-to-human control transfer is completed safely and smoothly with the proposed haptic takeover controller, and meanwhile the vehicle performance and stability are also guaranteed.
Effect of Different Pretreatment Methods on Lipid Yield and Biodiesel Quality of Desmodesmus sp. Isolated from Sri Lanka

The present study investigates the growth rate and the effect of different pretreatment methods on lipid yield and biodiesel quality produced from Desmodesmus sp. isolated from Beira Lake, Colombo, Sri Lanka. Desmodesmus sp. was cultivated using photobioreactors providing the optimum cultivation conditions and growth was determined by measuring the optical density and direct cell count of the culture. Four different physical cell disruption methods including manual grinding, autoclaving, microwaving and water bath heating were carried out using chlorophyll removed microalgae biomass to investigate the effect of pretreatment on lipid yield and biodiesel quality. The highest lipid yield was obtained as 31.46 % for Desmodesmus sp. using water bath heating method. However, there was no significant impact on lipid yield after employing different cell disruption methods. In addition, chlorophyll removal reduced the lipid yield and increased the quality of the biodiesel produced from Desmodesmus sp. Furthermore, chlorophyll removal and cell disruption enhanced the quality of biodiesel by increasing the saturated fatty acid and monounsaturated fatty acid contents. Moreover, cetane number, higher heating value and iodine value of the biodiesel were within the acceptable range according to biodiesel quality standards. Therefore, Desmodesmus sp. can be identified as a potential source for microalgae biodiesel production.
Achieving High Stiffness Range of Force Feedback Gloves using Variable Stiffness Mechanism*

Force feedback glove is a promising interface for producing immersive haptic sensation in virtual reality and teleoperation systems. One open problem of existing gloves is to simulate virtual objects with adjustable stiffness in a fast dynamic response, along with lightweight and good back-drivability. In this paper, we introduce a leverage pivot modulating mechanism to achieve variable stiffness simulation for force feedback gloves. To simulate free space operation, the revolute pairs of the mechanism move in the unlocked state, which allows the user to clench his/her fist or fully extend fingers. To simulate constrained space operation, the revolute pairs are locked and passive feedback forces are generated at the fingertip. The total weight of the single-finger prototype glove is 55g. Experimental results show that the backdrive force of the glove is less than 0.069N in the free space, and the fingertip force reaches up to 12.76N in the constrained space. The stiffness of the glove is tuned by changing its structural stiffness, which ranges from 136.96Nmm/rad to 3368.99Nmm/rad.
Prosthetic Legs Output Feedback Control via Variable High Gain Observer

This paper addresses the state estimation and control of a robot/prosthesis control system with four joints: vertical hip displacement, thigh, knee and ankle angles. The motivation was inspired by several drawbacks regarding the usage of load cells and/or sensors in robots and prosthetic legs to capture gait data, external forces (GRFs) and moments during walking. We propose the implementation of a high gain observer (HGO) to estimate the prosthesis joint velocities with a time-varying HGO gain synthesized from measurable signals designed to reduce the amount of noise in the control effort while keeping an acceptable tracking error transient performance. Numerical simulations analyze the robustness of the closed-loop control with respect to parametric errors and measurement noise.
Towards Mobile Cognitive Fatigue Assessment as Indicated by Physical, Social, Environmental, and Emotional Factors

This research sought to establish which in-situ measures of cognitive fatigue, physical activity, social interaction, location, emotional state and facial landmarks, made using a smartphone application, could be used to indicate episodes of cognitive fatigue. This assessment was realised using cognitive tests (assessing memory, attention, reaction time, information processing speed and executive function), self-assessment, contextual factors and facial feature analysis. This study also investigated the use of an ensemble algorithm for the classification of cognitive fatigue utilising facial features and a Rotation Forest approach. Self-assessment of cognitive fatigue was shown to directly correlate with reaction time through a Psychomotor Vigilance Task (r =.643, p =.001), and self-reported increases in the level of social activity (r =.377, p =.001). Facial feature analysis revealed dominant emotions of sadness and anger when participants were cognitively fatigued. It also revealed underlying facial cues that indicated higher levels of cognitive fatigue including expressions of negative valence, and Facial Action Coding System units of increased brow furrow, eyelid tightening and lip suck. In addition, a Principle Component Analysis based Rotation Forest ensemble with a ternary output demonstrated a cognitive fatigue classification accuracy of 82.17%. The findings presented indicate that the inclusion of data relating to surrounding cognitive, social, physical and emotional factors can improve the accuracy of mobile in-situ cognitive fatigue assessment using our previously validated smartphone-based cognitive fatigue assessment approach. The findings further suggest gross-level fatigue status may be potentially classified to a reasonable degree of accuracy using facial features, which may give rise to personalised in-situ fatigue detection.
An Analysis of the Effects of Flexibility Modification on System Frequency Regulation

With the flexibility modification of thermal power plant, the minimum technical output of units has been reduced. Different kinds of operating modes can be chosen by operators of power grids, which will influence the frequency regulation of power grids. The mathematical model of reheater steam turbine and load frequency control (LFC) are established considering flexibility modification of thermal power plants. Two scenarios are created and simulations are carried out. The results indicate that with the increase of units investigated in power system, the frequency deviation decrease obviously in both a stand-alone control area and two interconnected control areas. The conclusions presented here provide some guidance on operation of thermal power plants in regions to reduce the amplitude of frequency oscillation.
On Blockchain Architectures for Trust-Based Collaborative Intrusion Detection

This paper considers the use of novel technologies for mitigating attacks that aim at compromising intrusion detection systems (IDSs). Solutions based on collaborative intrusion detection networks (CIDNs) could increase the resilience against such attacks as they allow IDS nodes to gain knowledge from each other by sharing information. However, despite the vast research in this area, trust management issues still pose significant challenges and recent works investigate whether these could be addressed by relying on blockchain and related distributed ledger technologies. Towards that direction, the paper proposes the use of a trust-based blockchain in CIDNs, referred to as trust-chain, to protect the integrity of the information shared among the CIDN peers, enhance their accountability, and secure their collaboration by thwarting insider attacks. A consensus protocol is proposed for CIDNs, which is a combination of a proof-of-stake and proof-of-work protocols, to enable collaborative IDS nodes to maintain a reliable and tampered-resistant trust-chain.
Prognostic value of PNN in prostate cancer and its correlation with therapeutic significance

Prostate cancer (PCa) is the most common malignancy. New biomarkers are in demand to facilitate the management. The role of the pinin protein (encoded by PNN gene) in PCa has not been thoroughly explored yet. Using The Cancer Genome Atlas (TCGA-PCa) dataset validated with Gene Expression Omnibus (GEO) and protein expression data retrieved from the Human Protein Atlas, the prognostic and diagnostic values of PNN were studied. Highly co-expressed genes with PNN (HCEG) were constructed for pathway enrichment analysis and drug prediction. A prognostic signature based on methylation status using HCEG was constructed. Gene set enrichment analysis (GSEA) and the TISIDB database were utilised to analyse the associations between PNN and tumour-infiltrating immune cells. The upregulated PNN expression in PCa at both transcription and protein levels suggests its potential as an independent prognostic factor of PCa. Analyses of the PNN’s co-expression network indicated that PNN plays a role in RNA splicing and spliceosomes. The prognostic methylation signature demonstrated good performance for progression-free survival. Finally, our results showed that the PNN gene was involved in splicing-related pathways in PCa and identified as a potential biomarker for PCa.
Effects of Likeness and Synchronicity on the Ownership Illusion over a Moving Virtual Robotic Arm and Hand

In this study we investigated body ownership over a virtual hand and arm as a function of their visual appearance (likeness) and synchronicity of visuo-tactile stimulation with a virtual electric toothbrush and a vibrotactile glove. In all conditions, participants controlled the movement of arm and fingers, maintaining synchronicity in motor-proprioceptive-visual signals. While the effects of varying likeness and temporal synchronicity of visual and haptic stimuli on the ownership illusion have both been investigated individually before, their relative contribution is still unknown. We find that likeness should be complete: making only the hand robotic reduces the subjective ownership illusion to same level as that of a full robotic arm and hand. Visuo-tactile synchronicity is not a hard prerequisite for an ownership illusion to occur: a high degree of agency with congruent motor-proprioceptive-visual cues and an arm/hand layout similar to one’s own body can be sufficiently strong to overrule incongruent visuo-tactile cues. This work is part of a larger study on the relative contribution of factors such as likeness, viewing mode, tactile stimulation and degree of agency on the body ownership illusion. The results may contribute to the enhancement of dexterous performance in remote telemanipulation tasks.
A Low-complexity Receiver for Massively Concurrent Non-orthogonal Multiple Access

We consider the multi-user joint detection problem of overloaded massively-concurrent NOMA (MC-NOMA) systems by designing a receiver capable of resolving effectively and efficiently the multi-user interference associated with the nonorthogonality inherent in such systems. The proposed receiver is effective in the sense that it is found to achieve the same performance of the brute-force maximum likelihood detector, and is efficient in the sense it significantly outperforms the latter in terms of computational complexity (i.e. run time). The design of the new receiver is based on a two-fold contribution. The first is a new tree optimization (or reduction) technique obtained via successive row- and column-permuted QR decomposition of the MC-NOMA-precoded channel, while the second is a new probabilistic thresholding scheme with a threshold constructed from estimated residual noise distribution and magnitude. We validate the proposed receiver in the context of a downlink MC-NOMA system, with simulation results supporting and quantifying the gains in overloading capability, as well as, complexity reduction.
Improvement of Control Performance of Sampling Based Model Predictive Control using GPU

This paper presents the application of Graphics Processing Unit (GPU) to improve the control performance of sampling based predictive control algorithms. As an example problem, obstacle avoidance situation with parked cars in a street is modeled as a non-linear model predictive control problem. Car dynamics and non-linear constraints are considered to achieve collision avoidance. The control input must be optimized in every control step in real-time considering the non-linear constraints. Sampling based approach is used to solve this problem and one of the major limitations to this approach is the computational cost involved. In this paper, the sampling-based optimization algorithm was adapted to utilize the parallel computing capabilities of GPU using CUDA. The generated input sequence and the computational speeds were compared with a CPU based program for the same case. The proposed method is implemented in a simulation experiment with car dynamics simulator to verify its performance in terms of path tracking. Finally, a general relationship between sample size and GPU acceleration of its calculation speed is also discussed.