Miracle-dz/UniFAR-FTUs huggingface dataset:
RaFIDe: A Machine Learning based RFI free observation planner for the SKA Era

Signal anomalies in astronomical data mainly come from Radio Frequency Interference (RFI). Radio Frequency Interference (RFI) has plagued the field of radio astronomy. RFI can be either internal (generated by instruments) or external that originates from intentional or unintentional radio emission generated by human activity. Radio Telescopes are known to generate massive amounts of astronomical data. With the huge amount of data being available, a clustering technique can be applied to detect RFI. The quality of the incoming radio signal will be determined by the clustering technique. This will enable us to detect the anomalies in the signal at a particular instant of time. This effort will further enable us to build a database and subsequently apply reinforced time-series machine learning models to predict the quality of the signal. This paper proposes a machine learning approach to study the signal quality over the recent past and make use of this knowledge to plan the near-future observation slots in frequency spectrum and time.
Dynamics of Tensegrity Robots With Negative Stiffness Elements

Tensegrity structures have unique features such as low mass to payload ratio, strength, and robustness. Therefore, they present great potential in robotics, aerospace, and civil engineering. The dynamics of tensegrity robots is highly nonlinear and constrained. As a result, their modeling, simulation, state estimation, and control are non-trivial. Strings in tensegrity structures are usually modeled as linear springs. Utilization of nonlinear elastic/damping elements in tensegrities would further enrich their dynamics and endow them with additional properties, such as multiple equilibrium configurations. In this paper, our preliminary work on the dynamics of actuated tensegrities with strings containing nonlinear elastic and/or damping elements is presented. At first, the formulation of tensegrity dynamics with general nonlinear elastic/damping elements is explored. Later dynamics of tensegrities with negative stiffness honeycombs incorporated into strings are considered. Simulations are performed on three tensegrity systems: two-bar, three-bar, and six-bar structures. Results demonstrate that negative stiffness honeycombs result in nonlinear steady-state response to constant external force, reduced force magnitudes in strings and bars, and increased range of motion.
Efficacy of Crotoxyphos Sprays for the Control of the Southern Cattle Tick in Puerto Rico

Field trials were conducted near Cabo Rojo and Mayaguez, Puerto Rico, to evaluate the efficacy of crotoxyphos (afpha-methylbenzyl 3-hydroxycrotonate dimethyl phosphate) applied as a whole-body spray to cattle for control of the southern cattle tick, Boophilus microplus (Canestrini). Percent control was determined by comparing numbers of 4.5- to 8.0-mm-sized engorged females present on control and treated animals on days 0, 1, 7, 14, and 21 posttreatment. Crotoxyphos at concentrations of 0.49 to 0.015% a.i. afforded an average control in excess of 92% (92.9 to 99.8%) for all concentrations. Differences in percent control of ticks between concentrations and also between days on which counts were made were determined by analysis of variance to be statistically nonsignificant.
Optimization of Bike Sharing Service: Its Acceptance and Preferences

Non-motorized transport can be seen as one of the important mode that is considered as sustainable transport system. There are many types of non-motorized transport such as cycling, walking, rollerblading also known as skating, rickshaws and other types that does not used combustion engine to move. This study focus on bike-share service since it has been widely used and recognized as one of the alternative in going towards sustainable future for human society. Thus, it is one of the current service implemented in Malaysia. Recently, bike-share system has been widely used around the world which have helped to promote cycling as a viable and valued transport option. In going towards sustainable future, bike sharing service is a must for a country to ensure the green initiatives to be applied smoothly. Using 2016 data from TomTom Traffic Index website, this paper examines congestion rate of traffic flow during that time at Bukit Bintang area. Regardless of promoting the usage of bicycle as one of the best alternative in going towards sustainable living, the utilization rate was seen to be still low due to the congestion level and usage of other types of vehicle. Questionnaire surveys were carried out. Two identified factors used to forecast public views in using bicycle: 1) Environmental Factors; and 2) Subjective Norm Factors were seen influenced the decision of using bicycle as one of the alternative to avoid congestion. Multiple Regression analysis is used to identify the relationships between these factors and its influence towards public views on bicycle usage. It provides strategies to change the choice of using bicycle as the alternative in going towards green and sustainable living.
Multi-Level Reversible Data Anonymization via Compressive Sensing and Data Hiding

Recent advances in intelligent surveillance systems have enabled a new era of smart monitoring in a wide range of applications from health monitoring to homeland security. However, this boom in data gathering, analyzing and sharing brings in also significant privacy concerns. We propose a Compressive Sensing (CS) based data encryption that is capable of both obfuscating selected sensitive parts of documents and compressively sampling, hence encrypting both sensitive and non-sensitive parts of the document. The scheme uses a data hiding technique on CS-encrypted signal to preserve the one-time use obfuscation matrix. The proposed privacy-preserving approach offers a low-cost multi-tier encryption system that provides different levels of reconstruction quality for different classes of users, e.g., semi-authorized, full-authorized. As a case study, we develop a secure video surveillance system and analyze its performance.
Reassortment, positive selection, and the inter-segmental patterns of divergence and polymorphism in influenza virus H3N2

Reassortment in viruses with segmented genome is a major evolutionary process for their genetic diversity and adaptation. It is also crucial in generating different levels of sequence polymorphism among segments when positive selection occurs at different rates on them. Previous studies have detected intra-subtype reassortment events in human influenza H3N2 by between-segment incongruity in phylogenetic tree topology. Here, we quantitatively estimate the reassortment rate, probability that a pair of segments in a viral lineage become separated in a unit time, between hemmaglutinin (HA) and four non-antigenic segments (PB2, PB1, PA and NP) in human influenza virus H3N2. Using statistics that measure incongruity in tree topology or linkage disequilibrium between segments and performing simulations that are constrained to reproduce the various patterns of H3N2 molecular evolution, we infer that reassortment rate ranges between 0.001 and 0.01 assuming one generation to be 1/80 year. However, we find that a higher rate of reassortment is required to generate the observed pattern of ~40% less synonymous sequence polymorphism on HA relative to other non-HA segments, which results from recurrent selective sweeps by antigenic variants on the HA segment. Here, synonymous diversity was compared after correcting for difference in inferred mutation rates among segments, which we found significant. We also explored analytic approximations for inter-segmental difference in sequence diversity for a given reassortment rate to understand the underlying dynamics of recurrent positive selection. It is suggested that the effects of clonal interference and potentially demography-dependent rate of reassortment in the process of recurrent selective sweeps must be considered to fully explain the genomic pattern of diversity in H3N2 viruses.
Brain Decoding of Viewed Image Categories via Semi-Supervised Multi-View Bayesian Generative Model

Brain decoding has shown that viewed image categories can be estimated from evoked functional magnetic resonance imaging (fMRI) activity. Recent studies attempted to estimate viewed image categories that were not used for training previously. Nevertheless, the estimation performance is limited since it is difficult to collect a large amount of fMRI data for training. This paper presents a method to accurately estimate viewed image categories not used for training via a semi-supervised multi-view Bayesian generative model. Our model focuses on the relationship between fMRI activity and multiple modalities, i.e., visual features extracted from viewed images and semantic features obtained from viewed image categories. Furthermore, in order to accurately estimate image categories not used for training, our semi-supervised framework incorporates visual and semantic features obtained from additional image categories in addition to image categories of training data. The estimation performance of the proposed model outperforms existing state-of-the-art models in the brain decoding field and achieves more than 95% identification accuracy. The results also have shown that the incorporation of additional image category information is remarkably effective when the number of training samples is small. Our semi-supervised framework is significant for the brain decoding field where brain activity patterns are insufficient but visual stimuli are sufficient.
Battery Storage for Resilient Homes

Small-scale battery energy storage systems (BESS), especially for behind-the-meter applications, are still relatively expensive, but we show that it can be a potent solution to render homes resilient to storm related power outages. We present a stochastic programming model formulation to optimize PV/BESS explicitly accounting for resilience benefits these investments entail, over and above their ability to reduce cost of supply. The stochastic optimization considers uncertainties around storm related grid supply failures as well as variability of solar PV. The model includes an embedded Monte Carlo simulation module that considers storm related outage risks using climate model reanalysis data. It is a least-cost planning framework that optimizes selection of BESS, solar PV, grid supply, and diesel generator, from a home-owner’s perspective. We present two case studies with low and high storm risks that demonstrate how different risk exposures can impact on the selection of alternative options to build resilience. Duals, or shadow prices, of demand-supply constraints from the model for both normal days and for storm related contingencies, provide interesting insights into the marginal cost of supply that can inform innovative pricing schemes to promote customer level resilience measures. The case study results reveal significant merits of BESS, in combination with PV, to enhance resilience. We find that in low-risk areas like Bethesda, MD, incremental PV and BESS required for a more resilient system can add $79 (4%) to annual electricity costs for a typical household, and a considerably higher $208 (10.6%) in Miami, FL which is at a much greater risk. These options are, however, 27% (in Bethesda) and 20% (in Florida) less expensive than the conventional solution of installing a diesel generator. These results provide insights into the value of BESS as part of a resilient and clean energy solution for households.
Analysing Emotions on Lecture Videos using CNN AND HOG (Workshop Paper)

Facial Expressions play a vital role in the process of recognizing emotions and also to have non-verbal communication as well as in identifying people. Emotional Recognition turns out to be very important in everyday life just next to the tone of voice. Novel applications in Human-Computer Interaction (HMI) have been enabled by this and in many other areas. Inevitably most of the recent research on this area focuses on Convolutional Neural Networks (CNN) for extraction of features and inference from those features. In this paper we used CNN algorithm along with Histogram of gradients (HOG) features for higher accuracy. We perform emotional analysis of lecturers from Impartus videos. By detecting their emotions from a sequence of videos during their coursework, we evaluate the feedback they are expected to receive at the end of the duration of their course.
Reinforcement Learning-based Auto-router considering Signal Integrity

In this paper, we propose artificial intelligent (AI)router, a reinforcement learning (RL)-based auto-router considering signal integrity (SI), for the first time. Our algorithm has two main stages. At first, we design the transformer-based novel neural architecture considering the keep-out region, crosstalk region, and the number of vias for SI optimization. Then, the designed neural network is optimized by the policy gradient, one of the RL algorithms. Compared with the conventional maze routers, the A* algorithm, and the lee algorithm, it is verified that our AI-router outperforms the algorithms in terms of wire-length and crosstalk in a specific test case. Furthermore, it is shown that AI-router successfully performs multi-layer routing which is not feasible with conventional maze routers.
Decoupling cellular response to topography and stiffness in three dimensions

Biophysical aspects of in vivo tissue microenvironments include microscale mechanical properties, fibrillar alignment, architecture or topography of the extracellular matrix (ECM), and the repertoire of ECM ligands present, all of which provide cues to drive cellular response. Cell-ECM interactions are important regulators of both normal tissue homeostasis and malignancy. Thus, understanding both extracellular cues and the cellular responses they elicit is fundamental to developing therapeutic strategies. Various in vitro platforms for 3D cell culture and tissue engineering have been used to study cellular response to the microenvironment. However, recapitulating the diversity of tissue architectures present in vivo in a controlled manner in three-dimensional tissue mimetics is challenging using naturally derived ECM hydrogels. Here, we use a bottom-up approach to build fibrillar architecture into 3D amorphous hydrogels using self-assembly of magnetic colloidal particles functionalized with human ECM proteins. Human ECM proteins associated with organ-specific pathological states were used. We determined that, while the bulk tissue mechanics of hydrogels containing either aligned fibers or randomly distributed colloidal particles were similar, aligned hydrogels exhibited spatial heterogeneities in microscale mechanical properties near aligned fibers. We then used this platform in combination with 2D substrates of defined elastic modulus to decouple the role of topography from microscale tissue mechanics for normal and tumor cells. We determined that topographical cues dominate cellular response for human and normal cells, which responded independently of microscale mechanics and ECM composition in 3D hydrogels. These data suggest that topography alone can drive fundamental cellular responses such as polarization and migration for both normal and transformed cells.
Hierarchical Evasive Path Planning Using Reinforcement Learning and Model Predictive Control

Motion planning plays an essential role in designing self-driving functions for connected and autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to follow, fulfilling different requirements, such as safety, efficiency, and passenger comfort. In this area, algorithms must also meet strict real-time expectations, since, especially in an emergency, the decision time is limited, which raises a trade-off for the feasibility requirements. This article proposes a hierarchical path planning solution for evasive maneuvering, where a Twin Delayed DDPG reinforcement learning agent generates the parameters of a geometric path consisting of chlotoids and straight sections, and an underlying model predictive control loop fulfills the trajectory following tasks. The method is applied to the automotive double lane-change test, a common emergency situation, comparing its results with human drivers’ performance using a dynamic simulation environment. Besides the test’s standardized parameters, a broader range of topological layouts is chosen, both for the training and performance evaluation. The results show that the proposed method highly outperforms human drivers, especially in challenging situations, while meeting the computational requirements, as the pre-trained neural network and path generation algorithm can provide a solution in an instant, based on the experience gained during the training process.
Low-Voltage Capacitive-Based Step-Up DC-DC Converters for RF Energy Harvesting System: A Review

Bulky off-chip inductors are predominantly adopted for inductive-based step-up DC-DC converter in RF energy harvesting (RFEH) systems, which impose a restriction in physical form factor for miniaturized device. This article review and explores the capacitive-based step-up DC-DC converters (charge-pump) as voltage boosting element for low-voltage RFEH systems. An overview of RFEH is established and a comprehensive review of CMOS charge-pump is followed along with the complementary frequency generation circuit used as a clocking element. Key design considerations of charge-pump circuits are included here along with recommendations to circumvent its bottlenecks for future development in RFEH systems.
Design of Quad Plane UAV with Carrier Gripper for First Aid of Victims from Aircraft Accident

Accidents can happen in many kind of public transportation to all people. This main of this kind of accident is evacuate process of transportation accident like plane and ship, meanwhile the location of accident can not reach, in case like aircraft accident at mountain forest and shinking of ship in the middle of the sea. Exploration in the accident area will be difficult with terrain to access it from rescue team. Therefore UAV is needed, UAV must have capability to hover at deep forest and can cruise in the middle of ocean, so the rescue team can easily find it. The author design a quad plane with an estimation of the plane’s total weight is 4400 gram and have ability to hover using four vertical motor and cruise using one horizontal motor that placed in the behind of UAV. Whereas to make transition from hover mode to cruise mode it requires an airspeed sensor as the parameter to make transition so than the plane will not lose the lift force. From the results of flight tests, quad plane can make the transition from QHOVER mode to FBWA at 10 m/s, with an altitude at 34 meters, a flying distance of quad plane at 188 meters, and a maximum speed of quad plane is 25 m/s. The quad plane also able to release the payload in the form of first aid which is useful for providing first aid to survivors.
The Comparison Performance of MPPT Perturb and Observe, Fuzzy Logic Controller, and Flower Pollination Algorithm in Normal and Partial Shading Condition

Efficiency of photovoltaic can be optimal with power tracking system. Maximum Power Point Tracking (MPPT) is static tracking to harvest the electrical energy of photovoltaic module. Photovoltaic module can optimal generated energy in ideal condition that mean irradiation condition about 1000 W/m2 and temperature of 25ºC without shadow on surface of photovoltaic module. However, the implementation photovoltaic work in static and dynamic weather. Surface of photovoltaic module it is often blocked by a shadow like a tree, building, dust and any object around photovoltaic module. This problem resulted two or more peak power on P-V curve. This condition is called partial shading. Partial shading can’t be solved by using the conventional method therefore artificial intelligent method is needed. Hence, this paper comparison some method of MPPT that is Perturb and Observe, Fuzzy Logic Controller, and Flower Pollination Algorithm for normal and partial shading condition. MPPT using Zeta converter to control switching duty cycle. The performance of three methods is tested simulation by PSIM. Result from simulation show that the Fuzzy Logic Controller the best solution in normal condition with accuracy more than 99% and response to convergence. The other hand, Flower Pollination Algorithm method is the best solution for partial shading condition without oscillation and accuracy more than 98%.
Computational modelling of modern cancer immunotherapy

Modern cancer immunotherapy has revolutionised oncology and carries the potential to radically change the approach to cancer treatment. However, numerous questions remain to be answered to understand immunotherapy response better and further improve the benefit for future cancer patients. Computational models are promising tools that can contribute to accelerated immunotherapy research by providing new clues and hypotheses that could be tested in future trials, based on preceding simulations in addition to the empirical rationale. In this topical review, we briefly summarise the history of cancer immunotherapy, including computational modelling of traditional cancer immunotherapy, and comprehensively review computational models of modern cancer immunotherapy, such as immune checkpoint inhibitors (as monotherapy and combination treatment), co-stimulatory agonistic antibodies, bispecific antibodies, and chimeric antigen receptor T cells. The modelling approaches are classified into one of the following categories: data-driven top-down vs mechanistic bottom-up, simplistic vs detailed, continuous vs discrete, and hybrid. Several common modelling approaches are summarised, such as pharmacokinetic/pharmacodynamic models, Lotka–Volterra models, evolutionary game theory models, quantitative systems pharmacology models, spatio-temporal models, agent-based models, and logic-based models. Pros and cons of each modelling approach are critically discussed, particularly with the focus on the potential for successful translation into immuno-oncology research and routine clinical practice. Specific attention is paid to calibration and validation of each model, which is a necessary prerequisite for any successful model, and at the same time, one of the main obstacles. Lastly, we provide guidelines and suggestions for the future development of the field.
Analyses of Pinned Photodiodes With High Resistivity Epitaxial Layer for Indirect Time-of-Flight Applications

This article analyzes the performance of pinned photodiode (PPD) for Time-of-Flight(ToF) applications on high resistivity epitaxial wafers. Due to its wide depletion region, high epitaxial resistivity PPDs feature some key advantages in collecting photogenerated charges. This article compares the demodulation contrast of the same pixel design on epitaxial wafers with different resistivity at near-infrared wavelength to analyze their performance. By comparing the simulated profile of pixels with different epitaxial resistivity, the characteristics of the PPD pixel on high resistivity epitaxial wafer when collecting photogenerated charges are concluded. This article also discusses the effects of using high resistivity epitaxial wafer with various design parameters. It is found that the use of high resistivity epitaxial wafer can greatly improve crosstalk performance, allow pixels to work at higher modulation frequencies, and enable large-sized pixels to have good demodulation capabilities.