Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuous Cuffless Blood Pressure Measurement using PPG and ECG

Continuous blood pressure changes monitoring with regular time intervals conventionally is performed by auscultatory and oscillometric measurement techniques. It has limitations on the patient naturality and inconvenience because of repeated arm pumping. This study aims to determine a method of blood pressure measurement that has continuous monitoring, cuffless, wearable and ambulatory measurement features and accuracy of clinical standards. The configuration uses PPG sensor and lead-1 ECG to obtain the PTT and PIR parameter. The parameter used to determine systolic and diastolic blood pressure. Tests performed on 40 healthy subjects in seated, stand and lying down position. The results prototypes have a mean±standard deviation (SD) to estimate of systolic, diastolic and pulse pressure were 0.55±7.42 mmHg, 0.37±7.20mmHg, 0.08±10.07mmHg, respectively. The MAD of systolic, diastolic and pulse pressure were 6.02 mmHg, 6.00 mmHg, and 8.03 mmHg, respectively. The highest correlation between estimated and reference blood pressure had Rsquare 0.719 for diastolic and 0.672 for systolic. Regression analysis for each subject between PTT and pulse pressure with highest Rsquare is 0,894 and for PIR and diastole is 0,663.
Parametric Optimization of Self-Switching Diode

Parametric optimization of a novel nano diode called self-switching device has been demonstrated using Silvaco TCAD simulator. The device exhibits non-linear characteristics analogous to a conventional diode without requiring any p-n junction. The cut-in voltage can be tuned by varying channel width of the device. The increase in channel doping concentration exhibits velocity saturation, hence, leading to the saturation of output current at higher voltages. The low subthreshold swing of about 100 mV/decade suggests potential utilization of SSDs in high on-off ratio, low power and low voltage electronic applications.
Physical Layer Encryption Algorithm Based on Polar Codes and Chaotic Sequences

Researches on 5th generation mobile communication networks (5G) are emerging to meet the demands of rapidly developing communications applications. The reliability and security of data have become key factors of its development, and thus 5G has put forward higher requirements for new coding and encryption technologies. In this paper, a physical layer encryption algorithm based on polarization codes and chaotic sequences is proposed to improve the reliability and confidentiality of transmission. In our scheme, chaotic sequences are allocated in the frozen bits of polar codes. Therefore, error correction and encryption can be performed simultaneously. Since the frozen bits are unknown to the eavesdroppers, it is difficult for them to decode the transmitted bits without the knowledge of the chaotic sequences. To further improve the security, we propose to generate the chaotic sequences by using the channel state information. Besides, the chain effects of delayed feedback are applied to increase the decrypted complexity of the eavesdroppers in this paper. Theoretic analysis and simulation results both show that the proposed algorithms can achieve high security without any error rate performance loss.
Analysis of CPG gait parameters and velocity of quadruped robot with spine*

According to the characteristics of animal’s spine movement and specific speed corresponding to a gait pattern, the relationship between gait parameters and speed is analyzed. First, the Hopf oscillator is used to generate the central pattern generator rhythm signal, and the characteristics of parameters of the Hopf oscillator are analyzed. Secondly, the relationship between the speed and the gait frequency in the trot gait is analyzed, and the relationship between the speed and the stride in the bound gait is analyzed. Then, through the Webots simulation, the results show that the addition of the spine makes the robot speed significantly faster in the bound gait. Finally, the rationality of the relationship between the respective speed and gait parameters in the trot and bound gait is verified by Webots simulation. After analysis, the error of the angular frequency is about 3.4 rad/s, the error of the amplitude of the spine is 0.026 rad.
Fault-Tolerant Query Execution over Distributed Bitmap Indices

Advances in storage software and filesystems have proliferated a vast array of easy-to-use distributed storage services, removing the barrier for a growing number of organizations to geo-distribute large data sets. While leaving data in their distributed environments is convenient for data collection, various types of processing (that might use multiple data sources) are precluded due to the prohibitive costs of data movement. Users are therefore burdened with finding creative ways of performing data analysis, often requiring expert knowledge in multiple domains. This paper reports on the design and implementation of a query engine that enables high-level queries over distributed data sets. Our system generates bitmap indices at multiple geo-distributed data sources in order to approximate large amounts of raw data values. The bitmaps are replicated for fault-tolerance and performance. Upon accepting a high-level (SQL-like) query, our system generates a query plan, resolves dependencies, and schedules for its execution over the distributed system. The system has been tested rigorously, and experimental results show that most overheads (i.e., query planning, node spawning, etc.) are negligible. Our testing also shows that our system is capable of delivering query results in the face of node failures, with no observable impact on query execution for up to 20% of the system failing. The system also provides a framework that is easily extendible for future research on the interplay between distributed systems and bitmap indices.
A review of feature indexing methods for fast approximate nearest neighbor search

Fast feature matching is of crucial importance for time-critical applications in computer vision. The main goal of this work is to provide a comprehensive review of the state-of-the-art approaches dealing with the problem of feature indexing. Crucially, indexing methods can be grouped into four classes, including space partitioning, clustering, hashing, and product quantization. The methods are deeply presented, discussed, and linked to each other. An empirical report of performance analysis is also provided to characterize the studied methods. Lastly, we give comments on possible room of improvements for some indexing schemes.
Speech Enhancement Based on Dictionary Learning and Low-Rank Matrix Decomposition

In this paper, a new speech enhancement method is proposed based on dictionary learning and low-rank matrix decomposition with the objective to improve speech quality and intelligibility. In both the learning stage and the enhancement stage of the proposed method, a new noise model is employed to capture the noise characteristics with a combination of a low-rank matrix and an overcomplete noise dictionary. In the learning stage, an unsupervised dictionary learning algorithm is first proposed based on this new noise model to train a noise dictionary with low mutual coherence to the clean speech. Then, a supervised low-rank matrix decomposition algorithm is proposed to extract the low-rank component of noise using the clean speech, noise, and noisy speech training data sets. The estimated low-rank matrix is clustered through the K-means method to generate a codebook which provides useful information for the enhancement stage. Finally, in the enhancement stage, by using the well-trained dictionaries and the reference codebook from the learning stage, a decomposition algorithm for the low-rank matrix and sparse component is developed to effectively estimate the clean speech from the noisy speech. The experimental results show that our proposed method achieves better enhancement performance than some state-of-the-art reference methods in terms of four objective performance evaluation measures especially in low signal-to-noise ratio adverse environments.
Hierarchical Full-Duplex Underwater Acoustic Network: A NOMA Approach

Underwater acoustic is the prevalent technology in underwater wireless communications. The sum rate in underwater acoustic channels is limited by the underwater environment properties. This paper attempts to increase the sum rate of underwater channels without utilizing additional resources, through adding a relay and employing full duplex (FD) and non-orthogonal multiple access (NOMA) technologies. The adopted system model has two sensors and two robotic arms communicating with a buoy via a relay. Employing FD-NOMA allows multiple uplink and downlink transmissions to occur simultaneously, using the same time and frequency resources. The main challenge for this deployment is the interference between the transmissions. Interference cancellation techniques, successive interference cancellation and self-interference cancellation, are employed to mitigate the interference due to NOMA and FD, respectively. In order to maximize the sum rate, an optimization problem over the power is formulated and solved as a convex optimization problem. The performance of the system is benchmarked with the performance of the non-relay (NR) aided FD-NOMA and relay-aided (R) half duplex orthogonal multiple access (HD-NOMA). It is shown that R-FD-NOMA always has higher sum rate than NR-FD-NOMA, irrespective of the efficiency of interference cancelation. In addition, it is shown that at efficient interference cancellation, the sum rate of FD-NOMA is higher than HD-OMA.
An Investigation of the Impact of Language Runtime on the Performance and Cost of Serverless Functions

Serverless, otherwise known as "Function-as-a-Service" (FaaS), is a compelling evolution of cloud computing that is highly scalable and event-driven. Serverless applications are composed of multiple independent functions, each of which can be implemented in a range of programming languages. This paper seeks to understand the impact of the choice of language runtime on the performance and subsequent cost of serverless function execution. It presents the design and implementation of a new serverless performance testing framework created to analyse performance and cost metrics for both AWS Lambda and Azure Functions. For optimum performance and cost management of serverless applications, Python is the clear choice on AWS Lambda. C# . NET is the top performer and most economical option for Azure Functions. NodeJS on Azure Functions and . NET Core 2 on AWS should be avoided or at the very least, used carefully in order to avoid their potentially slow and costly start-up times.
Research on Dynamic Hydrodynamic simulation of fin stabilizer with LBM

In the past, many kinds of researches have been carried out on fin stabilizer hydrodynamics under static conditions, but many influencing factors have been neglected, which may affect the accuracy of calculation results, reduce control precision of fin stabilizers and anti-rolling effect. In this paper, computational fluid dynamics (CFD) software is used to simulate the NACA0015 fin and simplified hull based on Lattice Boltzmann method (LBM). In the given environment, dynamic simulations of the factors that affect the hydrodynamic of the fin stabilizer is performed and the corresponding results are given. The feasibility of the new method is verified by comparing with the experimental data. Compared with the traditional CFD method, the proposed new method is more efficient and accurate in dynamic condition. The simulation results are corresponding to reality, which can bring some theoretical reference significance for the optimal design of the fin stabilizer.
Fast Cosine Similarity Search in Binary Space with Angular Multi-Index Hashing

Given a large dataset of binary codes and a binary query point, we address how to efficiently find <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><inline-graphic xlink:href="eghbali-ieq1-2828095.gif"/></alternatives></inline-formula> codes in the dataset that yield the largest cosine similarities to the query. The straightforward answer to this problem is to compare the query with all items in the dataset, but this is practical only for small datasets. One potential solution to enhance the search time and achieve sublinear cost is to use a hash table populated with binary codes of the dataset and then look up the nearby buckets to the query to retrieve the nearest neighbors. However, if codes are compared in terms of cosine similarity rather than the Hamming distance, then the main issue is that the order of buckets to probe is not evident. To examine this issue, we first elaborate on the connection between the Hamming distance and the cosine similarity. Doing this allows us to systematically find the probing sequence in the hash table. However, solving the nearest neighbor search with a single table is only practical for short binary codes. To address this issue, we propose the angular multi-index hashing search algorithm which relies on building multiple hash tables on binary code substrings. The proposed search algorithm solves the exact angular <inline-formula><tex-math notation="LaTeX">$K$</tex-math><alternatives><inline-graphic xlink:href="eghbali-ieq2-2828095.gif"/></alternatives></inline-formula> nearest neighbor problem in a time that is often orders of magnitude faster than the linear scan baseline and even approximation methods.
Developing a Health-Conscious Energy Management Strategy Based on Prognostics for a Battery/Fuel Cell Hybrid Electric Vehicle

Since the degradation of energy sources in battery/fuel cell hybrid electric vehicles (HEVs) is inevitable and highly influences the durability of the system, an energy management strategy (EMS) aiming at prolonging the lifetime is demanding. In fact, the predictive nature of prognostics gives chance to develop such an EMS, which performs energy management through automatic corrective actions with the help of the awareness of energy sources' health states. This paper has addressed some scientific issues encountered when considering EMS based on prognostics and proposed an EMS based on fuzzy logic control which has successfully mitigated the battery degradation.
Fluidic Elastomer Actuators for Haptic Interactions in Virtual Reality

Virtual reality experiences via immersive optics and sound are becoming ubiquitous; there are several consumer systems (e.g., Oculus Rift and HTC Vive) now available with these capabilities. Other sensory experiences, such as that of touch remain elusive in this field. The most successful examples of haptic sensation (e.g., Nintendo 64's rumble pack and its descendants) are vibrotactile, which do not afford for persistent, morphological shape experiences. This letter presents work on the development of a 12 DOF fluidically pressurized soft actuator for persistent and kinesthetic haptic sensations, a hardware controller for operating it, and software interface with NVIDIA's game VR Funhouse.
Role of Interfacial and Intrinsic Coulomb Impurities in Monolayer MoS2 FETs

In this work, first, 2D scattering rates in monolayer MoS2 FETs due to interfacial and intrinsic Coulomb impurities as well as all other possible scatterers in an asymmetric dielectric environment have been calculated. Next, using the calculated 2D scattering rates, the electron mobility and current degradation in a monolayer MoS2 based FET have been studied employing a particle-based quantum-corrected Monte Carlo device simulator. The intrinsic phonon-limited electron mobility is found to be around $10^{5}\ cm^{2}/V.s$. The interfacial effects (remote phonon and remote Coulomb scattering) degrade this value to $\sim 36cm^{2}/V.s$ and intrinsic Coulomb scattering further reduces the electron mobility to $\sim 3\ cm^{2}/V.s$. It is found that Coulomb scattering is responsible for $\sim92\%$ of current degradation, while remote phonon and intrinsic phonon reduce the current by ~33% and $\sim 10\%$, respectively. Among the Coulomb scattering rates, the remote Coulomb scattering from the top oxide (HfO2) and the bottom oxide (SiO2) reduce the current by $\sim24\%$ and $\sim72\%$, respectively. Whereas, the intrinsic Coulomb scattering alone degrades the current by ~90%.
Strain Tensor Imaging in Compression Optical Coherence Elastography

Compression optical coherence elastography forms images based on the mechanical properties of tissue by mapping the local strain in response to a compressive load. Strain is described by a second-order tensor, comprising six independent components. The majority of compression elastography methods, however, measure and form their analyses from a single axial component, which relies on the assumption that the stress in tissue is uniform and uniaxial. However, in general, tissues are complex and heterogeneous, and rarely comply with this assumption. This can lead to inaccuracies and misinterpretation of image contrast in compression optical coherence elastography. Here, we image the full strain tensor and demonstrate its utility in unambiguously characterizing deformation in structured phantoms and ex vivo tissues. We derive additional parameters from the strain tensor, and map the local compressibility, anisotropy of deformation, and total equivalent strain. Such analysis is enabled by an efficient non-iterative approach to measuring the three-dimensional displacement field via a closed-form solution to a collection of the amplitude of complex correlation coefficients across multiple digitally shifted images. Strain tensor imaging is likely to lead to more accurate estimation of tissue mechanical properties, improving the utility of compression optical coherence elastography in clinical and biological applications.
Adding External Features to Convolutional Neural Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis currently attracts much attention from researchers in sentiment analysis and opinion mining fields. In this problem we simultaneously solve both tasks of aspect detection and sentiment detection. This paper proposes a Convolutional Neural Network based model in which we integrate extended rich information features into the basic CNN model. Our experiment is conducted on the aspect-based sentiment analysis task of Semeval 2016 and achieves the best results in comparison with previous studies.
State of Health Estimation of Lithium-Ion Batteries Based on Fixed Size LS-SVM

In this paper, a fixed size least square - supported vector machine (LS-SVM) algorithm is proposed to estimate the state of health (SOH) of the lithium-ion battery. First, the battery constant current discharging voltage profiles are analyzed, partial discharge curve that can be employed to describe the attenuation of the battery's SOH are extracted, and the suitable voltage range is analyzed. The discharging time with different beginning voltage point is regarded as the input variable of the model. Then the fixed size LS-SVM algorithm is employed, and the corresponding support vector is selected according to the arbitrary entropy. The model parameters are optimized based on Bayesian framework. Experimental results validate that the proposed method can effectively track the aging of batteries. Compared with the traditional supported vector machine (SVM), the fixed size LS-SVM requires only 10 support vectors to achieve the desirable SOH estimation.
Complex Network Based Knowledge Graph Ontology Structure Analysis

Ontology is the core of knowledge graph. Traditional ontology description and ontology representation rely on ontology descriptional language. This kind of representation method makes it difficult for people to quickly grasp ontology’s structure and then reuse it or segment it. To solve this problem, we proposed a method to transform ontologies into complex networks. This paper analyses ontologies’ structural characteristics through ontology visualization and ontologies’ degree distribution, clustering coefficient, average path length and eigenvector centrality. We observed that many ontologies have tree-like structures. Our analyses further revealed that a concept’s importance is positively related to its degree and eigenvector centrality. Experiments in university ontology shows that our method has a good effect in intuitively understanding the ontology structure.
Smart Botanic Gardens Application to reduce littering problem in Kebun Raya Bogor

With the popularity of Bogor Botanic Gardens (Kebun Raya Bogor, Indonesia) as one of the most favorite tourist destination in Indonesia, the number of visitors has extremely increased in the last three consecutive years. However, the increasing number of visitors threat the sustainability of biological and natural resources which is caused by behavior violations of visitors i.e. littering problem. In this work, we propose an initial design of smart botanic gardens application to reduce littering problem that contributes to the preservation of biological and natural resources. We adopt the model of sustainable behavior in implementing mobile application so that there are more interaction and participation from communities that trigger more awareness among the people. Moreover, we enhance our apps with machine learning-based motion detection that automatically detects littering activities inside the garden. The notification will be given to the operational team so that they can take proper reactions.
Syntax-Directed Hybrid Attention Network for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis is a fine-grained task in sentiment analysis that aims at detecting sentiment polarity towards a specific target in a sentence. Previous studies focus on using global attention mechanism that attends to all words in the context to model the interaction between target and sentence. However, global attention suffers from assigning high-attention score to irrelevant sentiment words in the cases where sentence contains noisy words or multiple targets. To address this problem, we propose a novel syntax-directed hybrid attention network (SHAN). In SHAN, a global attention is employed to capture coarse information about the target, and a syntax-directed local attention is used to take a look at words syntactically close to the target. An information gate is then utilized to synthesize the information from local and global attention results and adaptively generate a less-noisy and more sentiment-oriented representation. The experimental results on SemEval 2014 Datasets demonstrate the effectiveness of the proposed method.
False Data Injection Attacks With Incomplete Network Topology Information in Smart Grid

False data injection (FDI) attack causes a disadvantage to the safety of a power system. In the past, the adversary launching FDI attack mostly needed the complete network topology information of the power system and a large amount of measurements. However, the reality is that the adversary has limited resources and can hardly manipulate massive measurements without being detected. This paper proposes a new method to form attack vector in the condition of incomplete system information. The proposed method first maps the restricted data into a new Jacobian matrix by a kernel-independent component analysis, then constructs the undetectable attack model, and finally, designs a less costly attack vector to accomplish the FDI attack. The experimental results on different IEEE buses with Matpower tool illustrate the validity of the proposed method. Additionally, the proposed method shows a relatively high success rate in the cases of different degrees of incomplete information and various quantities of manipulated data in different power systems.