Miracle-dz/UniFAR-FTUs huggingface dataset:
A Numerical Study on the Influence of the Rear Wing Size on the Thrust Performance of the Two Dimensional Tandem Flapping Wings

In this paper, the thrust performance of the two-dimensional elliptic tandem flapping wings is numerically investigated on five different rear wings of chord length varying between 0.5C1 to 1.5C1 at an interval 0.25C1. Here, C1 is the chord length of the front wing that is kept constant. The aspect ratio (i.e. the ratio of the chord length and thickness of the wing) of all the wing is fixed as 8. The Strouhal number and Reynolds number based on the chord length of the front wing is set as 0.32 and 5000, respectively. The starting phase angle between the two wings is maintained at 0 deg for in-phase flapping. It is found that the identical rear wings in a tandem flapping mode (in the presence of front wing) produce a significantly large amount of thrust force as compared to in a single flapping mode (in the absence of front wing). From the thorough investigation of the flow regime and vortex structure, it is observed that the constructive interaction of the shear layers of the front wing and the leading edge vortex of the rear wing results in higher thrust performance in tandem flapping mode. The transient peak thrust is noticed for the rear wing having the smallest size. The correlation of the flow regime and transient thrust coefficient is also discussed in the paper.
Efficacy of Metarhizium anisopliae and Beauveria bassiana against Helicoverpa armigera in Chickpea, under Field Conditions in Nepal

A field study was conducted to evaluate the efficacy of two most virulent native isolates of insect pathogenic fungi (Metarhizium anisopliae and Beauveria bassiana) and compared with four commercial biopesticides against Chickpea pod borer (Helicoverpa armigera Hubner) at Chitwan, Nepal. The number of H. armigera lavae observed in plots treated with M. anisopliae and B. bassiana were significantly lesser than the control plots during vegetative, flowering and pod setting stage of chickpea. Similarly, the chickpea yield was significantly higher in the plots treated with M. anisopliae and B. bassiana than control, however lesser than NPV and Bt treated plots. Based on this study, the native isolates have potential to be a biocontrol agent against the H. armigera in Nepal.
Towards the Exact Memory Rate Tradeoff for the (4,5) Cache Network

The notion of coded caching was introduced by Maddah-Ali and Niesen when they demonstrated the utility of coding in caching systems. Since their seminal work, several schemes have been proposed to characterize optimal memory rate tradeoff to the caching problems. In this paper, we consider the (4, 5) cache network where the server has four files, each of size F bits, and five users are connected to the server through a common shared link. We consider the demands where each file in the server is requested by at least one user. For this cache network, we derive an improved lower bound for the small cache region, where the cache size in the range of $\displaystyle \frac{1}{5}F$ bits to $\displaystyle \frac{4}{5}F$ bits. We also introduce a new caching scheme to achieve the memory rate pair $\left(\displaystyle \frac{61}{20},\frac{1}{4}\right)$. We then derive a new lower bound for the cache region where cache size in the range of $\displaystyle \frac{61}{20}F$ bits to $\displaystyle \frac{16}{5}F$ bits to prove the optimality of the proposed scheme.
Exploration of the physiological response to an online gambling task by frequency domain analysis of the electrodermal activity*

Online gambling has dramatically increased over the last decades, thus the study of the underlying physiological mechanisms could be helpful to better understand related disorders. Specifically, physiological arousal is well-known to play a key role in gambling behavior. In the present study, unconventional frequency feature of the electrodermal activity (EDA) was extracted (EDASympn) and compared to the most common heart rate variability (HRV) spectral parameters (LF, HF, HFn, LF/HF) to measure arousal during an online gambling session. 46 subjects played online slot machines for 30 minutes, while EDA and ECG were recorded. In the analysis the gaming session was divided into three 10-minutes-long phases. A one-way repeated measures analysis of variance was carried out for each spectral parameter, with the game phases as within-subjects factor. All the calculated parameters showed significant differences between the initial phase of the game and the last two (p < 0.001). In particular, EDAsympn displayed a reciprocal trend with respect to HFn: an initial increase (decrease for HFn) was followed by a plateau phase. LF exhibited a significant difference also between the second and the third phases. EDA frequency-domain analysis appears to be a promising method for physiological arousal assessment, by showing the same discriminative power of HRV spectral components. Further research is needed to emphasize these findings.Clinical Relevance-This promotes the use of a new and easy-to-implement method to assess sympathetic activity
BoostCaps: A Boosted Capsule Network for Brain Tumor Classification

Brain tumor is among the deadliest cancers, whose effective treatment is partially dependent on the accurate diagnosis of the tumor type. Convolutional neural networks (CNNs), which have been the state-of-the-art in brain tumor classification, fail to identify the spatial relations in the image. Capsule networks, proposed to overcome this drawback, are sensitive to miscellaneous backgrounds and cannot manage to focus on the main target. To address this shortcoming, we have recently proposed a capsule network-based architecture capable of taking both brain images and tumor rough boundary boxes as inputs, to have access to the surrounding tissue as well as the main target. Similar to other architectures, however, this network requires extensive search within the space of all possible configurations, to find the optimal architecture. To eliminate this need, in this study, we propose a boosted capsule network, referred to as BoostCaps, which takes advantage of the ability of boosting methods to handle weak learners, by gradually boosting the models. BoosCaps, to the best of our knowledge, is the first capsule network model that incorporates an internal boosting mechanism. Our results show that the proposed BoostCaps framework outperforms its single capsule network counterpart.
BSTN: An Effective Framework for Compressed Video Quality Enhancement

In this paper, we propose a boundary-guided spatialtemporal network (BSTN) to enhance the quality of HEVC compressed videos. We first adopt the advanced optical flow algorithm to estimate the motion between the current frame and its frames, these adjacent frames are pre-warped by the estimated motion flow. Then, a bi-directional residual convolutional LSTM (BRCLSTM) is designed to implicitly discover frame variations over time between the compensated frames and current frame. In addition, we generate a guided map by utilizing the partition information of transform units (TUs) to guide our network to concentrate more on the boundaries of TUs, and the guided map is fused into our network by a boundary attention fusion module to enhance the reconstructed quality of current frame. Finally, some stacked convolutional layers with ReLU are used to reconstruct the desired frame. We evaluate our method under different configurations. Experimental results show that our method can produce superior results than the state-of-the-arts, both objectively and subjectively
Enhancement of Synchronization between Physiological Signals during Exercise: A Preliminary Investigation

During running, interactions were considered between three physiological oscillators - the heart, breaths, and steps. During intense exercise, the oscillations of all three systems are close to regular, producing good conditions to observe and characterise synchronization. The origin, as well as any physiological significance, of synchronization between these systems during running is not fully accepted or understood. Furthermore, the impact on synchronization of controlling both breathing and step rate has not been previously reported in detail. This study aims to measure cardiolocomotor, cardiorespiratory and respiratory- locomotor synchronization during different running protocols. Breathing was controlled by taking a fixed number of steps per breath (ratios of 5:1 and 3:1). Step rate was then guided at rates close to active heart rate, to instigate 1:1 phase-locking. Instantaneous phase difference quantified synchronization episodes. We have successfully observed all three forms of synchronization during all running protocols. Furthermore, coupling between heartbeats and steps was more pronounced when step rate was guided, and both cardiorespiratory and respiratory-locomotor coupling were extended when breathing rate was fixed to steps. These are exciting initial results from a novel experimental design, highlighting the complex interconnection that exists between these three systems during running, and the conditions to best observe the phenomena.
An Efficient Neural Network Architecture for Rate Maximization in Energy Harvesting Downlink Channels

This paper deals with the power allocation problem for achieving the upper bound of sum-rate region in energy harvesting downlink channels. We prove that the optimal power allocation policy that maximizes the sum-rate is an increasing function for harvested energy, channel gains, and remaining battery, regardless of the number of users in the downlink channels. We use this proof as a mathematical basis for the construction of a shallow neural network that can fully reflect the increasing property of the optimal policy. This scheme helps us to avoid using big neural networks which requires huge computational resources and causes overfitting. Through experiments, we reveal the inefficiencies and risks of deep neural network that are not optimized enough for the desired policy, and shows that our approach learns a robust policy even with the severe randomness of environments.
Disruption of structural brain networks in Primary Open Angle Glaucoma

Primary open angle glaucoma (POAG) is one of the most common causes of permanent blindness in the world. Recent studies have originated the hypothesis that POAG could be considered as a central nervous system pathology which results in secondary visual involvement. The aim of this study is to assess possible structural whole brain connectivity alterations in POAG by combining multi-shell diffusion weighted imaging, multi-shell multi-tissue probabilistic tractography, graph theoretical measures and a newly designed disruption index, which evaluates the global reorganization of brain networks in group-wise comparisons. We found global differences in structural connectivity between Glaucoma patients and controls, as well as in local graph theoretical measures. These changes extended well beyond the primary visual pathway. Furthermore, group-wise and subject-wise disruption indices were found to be statistically different between glaucoma patients and controls, with a positive slope. Overall, our results support the hypothesis of a whole-brain structural reorganization in glaucoma which is specific to structural connectivity, possibly placing this disease within the recently defined groups of brain disconnection syndrome.
Efficient FPGA Implementation of K-Nearest-Neighbor Search Algorithm for 3D LIDAR Localization and Mapping in Smart Vehicles

K-Nearest-Neighbor search (KNN) has been extensively used in the localization and mapping based on 3D laser point clouds in smart vehicles. Considering the real-time requirement of localization and stringent battery constraint in smart vehicles, it is a great challenge to develop highly energy-efficient KNN implementations. Unfortunately, previous KNN implementations either cannot efficiently build search data structures or cannot search efficiently in massive and unevenly distributed point clouds. To solve the issue, we propose a new framework to optimize the implementation of KNN on FPGAs. First, we propose a novel data structure with a spatial subdivision method, which can be built efficiently even for massive point clouds. Second, based on our data structure, we propose a KNN search algorithm which is able to search in unevenly distributed point clouds efficiently. We have implemented the new framework on both FPGA and GPU. Energy efficiency results show that our proposed method is on average 2.1 times and 6.2 times higher than the state-of-the-art implementations of KNN on FPGA and GPU platform, respectively.
A compression perspective on secrecy measures

The relationship between secrecy, compression rate, and shared secret key rate is surveyed under perfect secrecy, equivocation, maximal leakage, local differential privacy, and secrecy by design. It is emphasized that the utility cost of jointly compressing and securing data is very sensitive to (a) the adopted secrecy metric and (b) the specifics of the compression setting. That is, although it is well-known that the fundamental limits of traditional lossless variable-length compression and almost-lossless fixed-length compression are intimately related, this relationship collapses for many secrecy measures. The asymptotic fundamental limit of almost-lossless fixed length compression remains entropy for all secrecy measures studied. However, the fundamental limits of lossless variable-length compression are no longer entropy under perfect secrecy, secrecy by design, and sometimes under local differential privacy. Moreover, there are significant differences in secret key/secrecy tradeoffs between lossless and almost-lossless compression under perfect secrecy, secrecy by design, maximal leakage, and local differential privacy.
A Revised Principal Component Analysis in the Electricity Market

Li, Cursio and Sun (2018) explore price fluctuations in a multi-area and multiple transmission line an electricity market by constructing a principal component analysis (PCA) model on extreme price records in the real-time market. As a supplement, this paper proposes a revised procedure of PCA to fix the glitch of the classic PCA due to some missing critical conditions. This revised procedure of PCA is a generalized case to resolve restrictions that may be widely occurred in the electricity market.
Deep learning-based image evaluation for cervical precancer screening with a smartphone targeting low resource settings – Engineering approach

Cervical cancer is the fourth most common cancer among women and still one of the major causes of women’s death around the world. Early screening of high grade Cervical Intraepithelial Neoplasia (CIN), precursors to cervical cancer, is vital to efforts aimed at improving survival rate and eventually eliminating cervical cancer. Visual Inspection with Acetic acid (VIA) is an assessment method which can inspect the cervix and potentially detect lesions caused by human papillomavirus (HPV), which is a major cause of cervical cancer. VIA has the potential to be an effective screening method in low resource settings when triaged with HPV test, but it has the drawback that it depends on the subjective evaluation of health workers with varying levels of training. A new deep learning algorithm called Automated Visual Evaluation (AVE) for analyzing cervigram images has been recently reported that can automatically detect cervical precancer better than human experts. In this paper, we address the question of whether mobile phone-based cervical cancer screening is feasible. We consider the capabilities of two key components of a mobile phone platform for cervical cancer screening: (1) the core AVE algorithm and (2) an image quality algorithm. We consider both accuracy and speed in our assessment. We show that the core AVE algorithm, by refactoring to a new deep learning detection framework, can run in ~30 seconds on a low-end smartphone (i.e. Samsung J8), with equivalent accuracy. We developed an image quality algorithm that can localize the cervix and assess image quality in ~1 second on a low-end smartphone, achieving an area under the ROC curve (AUC) of 0.95. Field validation of the mobile phone platform for cervical cancer screening is in progress.
What about intraspecific variation? Reassessment of taxonomic and phylogenetic characters in the genus Synoeca de Saussure (Hymenoptera: Vespidae: Polistinae)

Morphological variation within and between species of the genus Synoeca was studied to test species status and to propose characters that fulfill the principles of communicability, unit delimitation, and conjunction for taxonomic and phylogenetic analyses. Twenty-six morphological characters obtained from the literature were analyzed; 16 of these characters clearly exhibited delimited states but only four of them were unique for each species. The status of every species of the genus was supported by geometric morphometrics of the head (front view, dorsal view and metasomal tergum 1) and by the presence of unique character states for each species. Phylogenetic analyses included nest and genitalia characters. Character reassessment provided a less resolved tree to that previously published. Including polymorphic characters did not improve tree resolution or clade support, while applying the conjunction test offered a more resolved and better supported tree. The study revealed the importance of analyzing variation in taxonomic and phylogenetic studies within and between species. A new key to species and a phylogenetic hypothesis are offered.
Physiological Signal Analysis and Classification of Stress from Virtual Reality Video Game

Stress can affect a person’s performance and health positively and negatively. A lot of the relaxation methods have been suggested to reduce the amount of stress. This study used virtual reality (VR) video games to alleviate stress. Physiological signals captured from Electrocardiogram (ECG), galvanic skin response (GSR), and respiration (RESP) were used to determine if the subject was stressed or relaxed. Time and frequency domain features were then extracted to evaluate stress levels. Frequency domain methods such as low-frequency (LF), high-frequency (HF), LF-HF ratio (LF/HF) are considered the most effective for HRV analysis, Poincare plots are more´ discerning visually and shares a 81% correlation with LF/HF ratio. GSR is associated with EDA activity, which only increases due to stress. Stress and relax were classified using Linear Discriminant Analysis (LDA), Decision Tree, Support Vector machine (SVM), Gradient Boost (GB), and Naive Bayes. GB performed the best with an accuracy of 85% after 5 fold cross validation with 100 iterations, which is admirable from a small dataset with 50 samples.
Performance Analysis of Energy Beamforming WPCN Links With Channel Estimation Errors

In this paper, we analyze the performance of a wireless-powered communication network that consists of a multiple-antenna hybrid access point (AP) and a single-antenna user. The AP transmits radio frequency (RF) power in the downlink (DL) using maximal ratio transmission (MRT) type energy beamforming. The user harvests it and transmits data to the AP in the uplink (UL), which is received with maximal ratio combining (MRC). Previous analyses have assumed the availability of perfect channel estimation. In contrast, we eliminate this unrealistic assumption and consider the effect of imperfect channel estimates. We first derive the distributions of the received AP signal-to-noise ratio (SNR). We then analyze the average throughput performance of delay-limited and delay-tolerant modes by evaluating the outage probability (OP) and ergodic capacity (EC). We also derive the exact bit error rates (BERs) and symbol error rates (SERs) of several digital modulations. Asymptotic performance expressions in the high SNR regime and the large antenna regime are also developed. Finally, analytical and asymptotic results are validated by Monte-Carlo simulations. The impacts of the transmit power, the energy harvesting (EH) time, the number of antennas and the efficiency of EH are investigated.
Massive MIMO Propagation Modeling With User-Induced Coupling Effects Using Ray-Tracing and FDTD

The article describes a numerical approach for Massive MIMO channel modeling that accounts for the effects of electromagnetic coupling between a user and the receiving device. The modeling is performed by a combination of the Finite-Difference Time-Domain and the Ray-Tracing methods, supplemented with a stochastic geometry model of the propagation environment. The influence of user-coupling on the channel properties was studied statistically using the singular value spread and matrix power ratio metrics of the channel correlation matrix. The time-averaged Poynting vector distribution in the near-field of the receiver was evaluated using a realistic human phantom model and the Maximum Ratio Transmission precoding scheme in the downlink. The average enhancement of the time-average Poynting vector magnitude at the receiver location, compared to the surrounding area, was found to be around 10 dB when using 36 antenna elements at the base station. The electromagnetic field exposure of the phantom was assessed in terms of the 10g-average peak-spatial Specific Absorption Rate and compared with the existing public guidelines. Comparison of the EMF and exposure results provides a new perspective on the future regulatory procedures.
A Demand-driven, Proactive Tasks Management Model at the Edge

Tasks management is a very interesting research topic for various application domains. Tasks may have the form of analytics or any other processing activities over the available data. One of the main concerns is to efficiently allocate and execute tasks to produce meaningful results that will facilitate any decision making. The advent of the Internet of Things (IoT) and Edge Computing (EC) defines new requirements for tasks management. Such requirements are related to the dynamic environment where IoT devices and EC nodes act and process the collected data. The statistics of data and the status of IoT/EC nodes are continuously updated. In this paper, we propose a demand- and uncertainty-driven tasks management scheme with the target to allocate the computational burden to the appropriate places. As the proper place, we consider the local execution of a task in an EC node or its offloading to a peer node. We provide the description of the problem and give details for its solution. The proposed mechanism models the demand for each task and efficiently selects the place where it will be executed. We adopt statistical learning and fuzzy logic to support the appropriate decision when tasks’ execution is requested by EC nodes. Our experimental evaluation involves extensive simulations for a set of parameters defined in our model. We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.