Miracle-dz/UniFAR-FTUs huggingface dataset:
Auto Focusing Ophthalmoscope for Smartphone

Several retinal pathologies can cause severe damages and may lead to permanent vision loss. Early diagnosis is highly recommended to take a precautionary measure to reduce the risk of ocular diseases. Therefore, an eye test by an ophthalmologist plays a crucial role. This does, however, require paying visit to a hospital, which becomes a burden for elders and patients with physical disabilities. Consequently, these patients usually get a late diagnosis and treatment, which the symptoms may have progressed and developed. Here, we develop an optical toolkit with engineered software for smartphones enabling the smartphone to take images of human retina as a direct ophthalmoscope. Thanks to the modern smartphone specifications, which usually come with high resolution cameras and sufficient computing power for the software. The optical toolkit illuminates an eye with an appropriate wavelength and light intensity and it has been provisionally tested for health and safety based on the ISO-10942 and IEC 62471 standards. An image processing algorithm is embedded in the software providing an auto focusing capability.
A distributed biometric authentication scheme based on blockchain

Biometric authentication is the preferred authentication scheme in modern computing systems. While it offers enhanced usability, it also requires cautious handling of sensitive users’ biometric templates. In this paper, a distributed scheme that eliminates the requirement for a central node that holds users’ biometric templates is presented. This is replaced by an Ethereum/IPFS combination to which the templates of the users are stored in a homomorphically encrypted form. The scheme enables the biometric authentication of the users by any third party service, while the actual biometric templates of the user never leave his device in non encrypted form. Secure authentication of users in enabled, while sensitive biometric data are not exposed to anyone. Experiments show that the scheme can be applied as an authentication mechanism with minimal time overhead.
AudiTread: Towards a Low-cost Wireless Auditory Feedback and Gait Analysis System

Post stroke individuals going through rehabilitation exercises are given feedback on their performances to potentially regain a normal gait. Research suggests that technologically complemented feedback helps patients reinforce behavioral changes faster via neuroplasticity. In this work, we develop a novel wearable real-time auditory feedback and gait analysis system: AudiTread. The device can provide auditory feedback on multiple parameters based on the thresholds set on the computer system and other variables can be added if required through its code accessible online. The device consists on multiple distance sensors, Bluetooth-enabled microcontroller, battery management system - all of which are integrated together to form an easily replicable system. The device is tested against standard Vicon data through time-series data analysis and signal processing to demonstrate the validity of the prototype. A comprehensive testing with stroke patients is currently under way and shall be followed. An elaborate description is provided on how system can be constructed and thereby corroborated.
An Improved K-Means Algorithm Based on Fuzzy Metrics

The traditional K-means algorithm has been widely used in cluster analysis. However, the algorithm only involves the distance factor as the only constraint, so there is a problem of sensitivity to special data points. To address this problem, in the process of K-means clustering, ambiguity is introduced as a new constraint condition. Hence, a new membership Equation is proposed on this basis, and a method for solving the initial cluster center points is given, so as to reduce risks caused by random selection of initial points. Besides, an optimized clustering algorithm with Gaussian distribution is derived with the utilization of fuzzy entropy as the cost function constraint. Compared with the traditional clustering method, the new Equation’s membership degree can reflect the relationship between a certain point and the set in a clearer way, and solve the problem of the traditional K-means algorithm that it is prone to be trapped in local convergence and easily influenced by noise. Experimental verification proves that the new method has fewer iterations and the clustering accuracy is better than other methods, thus having a better clustering effect.
HARRD: Real-time Software Rejuvenation Decision Based on Hierarchical Analysis under Weibull Distribution

Software rejuvenation are developed to mitigate serious consequences caused by software aging mainly through restarting software systems. As such restart actions will temporarily stop the software service, how to select the restart time precisely becomes the core research issue. Current main-stream machine learning based software rejuvenation methods predict the trend of resource usage of hardware parameters to determine the restart time. However the actual aging status in many software systems are not strongly related to the resource usage of hardware parameters, it is not rigorous to define the aging status with single hardware parameters. In this paper, we propose a novel real-time software rejuvenation decision method, named HARRD, where classic Weibull distribution in the field of reliability analysis is well utilized to simulate and model the state transition process of software aging. Then, based on this model with real-time resource usage of hardware monitoring parameters, and together integrating three model indicators, we construct the rejuvenation decision function using the analytic hierarchy process(AHP) to weight above parameters, which could finally be used as the rejuvenation decision basis for aging software systems. Our rejuvenation decision method could balance the unpredictable factors in software aging process by using accurate simulation models, and consider more indicators for rejuvenation time decision. The experimental results show that the software system based on our proposed method could achieve better software rejuvenation effects in terms of time consumption performance, average task processing speed and system stability.
Gold Nanoparticle Synthesis for Surface Enhanced Raman Spectroscopically Active Substrate

Large and small nanospheres, large and small nanoplates, nanorods and nanostars have been synthesized and fabricated into SERS substrates consisting of sandwiched and aggregated structure. Using 633 nm laser as excitation, individual SERS spectra of each labeling molecules, benzenethiol, 4-nitrobenzenethiol and 4-quinolinethiol, have been successfully obtained and the combination of these three molecules have the least amount of overlapping and can all be identified from the reference multiplexed spectra. Among all the substrates that have analyzed, the substrate made from nanospheres with sandwiched structure is able to produce multiplexed SERS spectra with more details and higher reproducibility. Although multiplexed SERS spectra can also be observed from substrates made from small nanoplates, nanostars and nanorods substrates with sandwiched structures, the unique peaks representing the labeling molecules are less consistent in their intensity. In addition, substrates with micro sized plates in sandwiched configuration are found to exhibit much lower SERS activities and this can be due to the size of the plate being much greater than the light source, restraining the surface plasmon resonance effect. Most of the substrates fabricated with aggregated nanoparticles have very low reproducibilities and saturated signals with 633 nm excitation. The spectra peaks are much easier to identify and are much more reproducible when 785 nm excitation have been adopted. This can be due to the size of the aggregated nanoparticles are much bulkier which a deeper penetrating light source is required to induce more molecules labels to exhibit SERS activities. A novel SERS substrate has been fabricated with nanoparticle-thiol-microplate sandwiched configuration by using a double ended thiol molecules, benzenedithiol, to strongly connect nanospheres and the plates together. However, the measurement of the SERS activity is limited by the overpowering of the light source, which has frequently melted and evaporated the plate samples once they have been exposed to the excitation radiation. In addition, instead of spreading evenly on the microplate surfaces, the nanoparticles have appeared to be aggregated which may have increased the difficulty in obtaining SERS activity.
Compact I-V Model for Ambipolar Field-Effect Transistors With 2D Transition Metal Dichalcogenide as Semiconductor

In this work, a compact current-voltage (I-V) model is proposed for 2-dimensional transition metal dichalcogenides (TMDs) ambipolar field-effect transistors (ambipolar FETs). Charge control method was used to derive the I-V model. Impurity dopant, interface traps and dependence of carrier mobility on gate voltage are considered in the model. The compact I-V model is based on semiconductor device physics. I-V characteristics of TMD ambipolar FETs predicted by the compact model agree well with those obtained by 2-dimensional numeric simulator and no fitting parameter is needed. In addition, I-V characteristics and carrier area density calculated by the compact model are compared with experimental data published elsewhere. And the result derived from the compact model is consistent with the experimental data published. The compact I-V model provides a tool to evaluate the performance of TMD ambipolar FETs and integrated circuits based on these devices.
Similarities between the Evolution of COVID-19 Number of Cases in Romanian Counties

Our study investigates possible similarities detected in the dynamics of COVID-19 pandemic concerning the number of confirmed cases in Romanian counties, as reflected in official reports, in a period of 136 days (between April 2nd and August 15th, 2020; two methods of assessment are used: the direct comparison of the values by calculating the Euclidean norm and the comparison of their trends expressed through the slopes of the regression lines. The biggest similarities between the daily number of new cases were observed between the counties Satu Mare and Sălaj (12.340); the global situation of Romania is the most similar with the situation from Bucharest (14.900), whereas the counties with the worst evolution are Suceava and Arges, with significantly increased numbers of cases as compared with the rest of the country. The smallest differences between their slopes were observed between the counties Tulcea and Neamt (0.00002) and the global situation of Romania is the most similar with the situation from Timisoara (0.00040); again, the counties with the worst pandemic evolution were found Suceava and Arges. (Abstract)
Low-Power MIMO Guided-Wave Communication

This article demonstrates the use of guided elastic waves (GEW) for multiple-in and multiple-out (MIMO) data communication in the framework of a structural health monitoring (SHM) system. Therefore, miniaturized low-voltage communication nodes have been developed. They are arranged in a spatially distributed and permanently installed network. Wireless exchange of encoded information across a metallic plate and a stiffened carbon-fiber reinforced plastics (CFRP) structure is investigated. A combination of square-wave excitation sequences and frequency-division multiplexing (FDM) is explored for parallel communication with multiple nodes. Moreover, the impact of the excitation-sequence length on the reliability of information transmission is studied in view of future energy-aware application scenarios. The presented system achieves in both studied structures error-free transmission at a data rate of 0.17 kbps (per carrier frequency) with a power consumption of 224 mW.
Scalable Time Series Classification in streaming and batch environments on Apache Spark

Time series classification is an important problem since data from sensors become more prevalent over time. In addition most of the data arrive in the form of a stream and thus have to be handled with the limitation that apply to streaming environments (low latency,low memory footprint). In this paper we address the problem of scalable time series classification on both Batch and Streaming environments. More specifically we implemented two state-of-the-art time series classification on top of Apache Spark and we adapted one of them for streaming applications. We evaluated our algorithms against two open datasets on a 10-node cluster. The algorithms we implemented scaled gracefully both in the batch and streaming environment.
Young’s Modulus Evaluation for the Blood Vessel Walls using Intravascular Optical Coherence Tomography

Compression elastography technique for intravascular optical coherence tomography (OCT) is described. The pulse wave is used as a low-traumatic deforming effect and Young’s modulus is the main measured value. The absolute displacements of the structures of the blood vessel wall are calculated from the displacements of the control points on the averaged A-scans. The thickness of the deformable part in OCT images is considered to be equal to the coherence probing depth. The area of the deformable part is equal to the area of the surface of the intravascular probe. The described method is to be combined with technical means for stenting the affected arteries, cerebral arteries with aneurisms, in particular.
Low Latency Automated Epileptic Seizure Detection: Individualized vs. Global Approaches

Seizures significantly reduce the quality of life for epilepsy patients. Short detection latency allows the implementation of a fast response algorithm that can be used for real-time epileptic seizure detection applications. In this paper, we propose an efficient algorithm that extracts time-domain features in the form of one second long EEG recordings, using as input one second long EEG recordings, to ensure reduced computational complexity. The features are fed to a simple neural network for detection. We validate the model, individually and globally on two real-world data sets, namely: Upenn-Mayo Clinic and CHB-MIT. On a per patient basis, results show an accuracy, sensitivity, and specificity of up to 99.17%, 99.44%, and 98.89%, respectively, while globally we reach 92.69%, 91.99%, and 93.38%, respectively.
A tool for proving Michelson Smart Contracts in WHY3*

This paper introduces a deductive verification tool for smart contracts written in Michelson, which is the low-level language of the Tezos blockchain. Our tool accepts a formally specified Michelson contract and automatically translates it to an equivalent program written in WhyML, the programming and specification language of the Why3 framework. Smart contract instructions are mapped into a corresponding WhyML shallow-embedding of the their axiomatic semantics, which we also developed in the context of this work. One major advantage of this approach is that it allows an out-of-the-box integration with the Why3 framework, namely its VCGen and the backend support for several automated theorem provers. We also discuss the use of our tool to automatically prove the correctness of diverse annotated smart contracts.
Virtual Proprioception for Eccentric Training through Conformal Wearable and Wireless Inertial Sensor Systems

Eccentric strength training represents a robust rehabilitation and therapy capability, which is inherently oxygen efficient. The oxygen efficient properties of eccentric training are uniquely suited for elderly people with impaired cardiopulmonary capacity. Furthermore, this technique of eccentric training may be extended to people with reduced cardiopulmonary capacity due to post-symptoms from COVID- 19. Synthesis of conformal wearable and wireless inertial sensor systems with eccentric strength training enables the evolution of Virtual Proprioception for eccentric training. The conformal properties of the wearable and wireless inertial sensor, such as the BioStamp nPoint, feature the ability to flexibly mount by adhesive to effectively any segment of the human anatomy, such as about the forearm near the wrist for quantifying a biceps curl. Using Virtual Proprioception for eccentric training with a conformal wearable and wireless inertial sensor, such as the BioStamp nPoint, the eccentric phase can be emphasized by visualized feedback of the associated gyroscope signal with a predefined threshold. Machine learning classification, such as a support vector machine, achieves considerable classification accuracy to distinguish between standard strength training and eccentric strength training for a biceps curl through a preliminary application of Virtual Proprioception for eccentric training with a conformal wearable and wireless inertial sensor.
Wavelet-Based Electromyographic Feature Selection Method for Real-Time Ankle Movement Recognition

This paper proposes a feature selection method aiming to determine the optimal feature subset for online recognition of four ankle movements, namely abduction, adduction, dorsiflexion, and plantarflexion. Sequential forward selection means is utilized to select from an original feature set comprising information from the mean absolute value (MAV) of wavelet decomposition coefficients, of the wavelet multi-level reconstructions, and of the signal itself. The impacts of different window lengths and overlap percentage on online classification accuracy are also evaluated. Support vector machines algorithm is adopted to recognize the ankle movements. The highest real-time classification accuracy of 97.32% is achieved by means of nine features, windows of 0.1 s length, and 80% overlap between windows. It shows that the proposed method is capable of selecting features for real-time ankle movement recognition with high accuracy and low latency effect.
Deep Learning Networks for Vectorized Energy Load Forecasting

Smart energy meters allow individual residential, commercial, and industrial energy load usage to be monitored continuously with high granularity. Accurate short-term energy forecasting is essential for improving energy efficiency, reducing blackouts, and enabling smart grid control and analytics. In this paper, we survey commonly used non-linear deep learning timeseries forecasting methods for this task including long short-term memory recurrent neural networks and nonlinear autoregressive models, nonlinear autoregressive exogenous networks that also include weather data, and for completeness, MATLAB’s nonlinear input-output model that only uses weather. These models look at every combination of load sequence data and weather information to identify which factors and methods are most effective at predicting short-term residential load. In this paper, the traditional nonlinear autoregressive model predicted short term load values most accurately using only energy load information with a mean square error of 7.53E-5 and a correlation coefficient of 0.995.
Impact of Cu Doping on PV Cell Parameters of NiO:Cu Nanostructure-Based Organic-Inorganic Perovskite Solar Cells

Solar energy is abundant, reliable, and clean among all the existing renewable energy sources. Due to the fast growth of the conversion efficiency of organic-inorganic perovskite (PVT) solar cells (PSCs) has fascinated the consideration of the researchers and photovoltaic (PV) industries. To achieve high performance and stability, the selection of a hole-transport material (HTM) of PSC is essential. In this regard, we have studied the potential of solution-processed Cu-doped NiO (NiO:Cu) nanoparticles (NPs) as efficient HTL for PSCs. NiO:Cu is also able to decrease the recombination at the HTL/PVT interface. The optical loss and charge transportation rate is analyzed with the Cu doping concentration. The UV-Vis result shows that the maximum transmittance NiO:Cu HTL is obtained at a Cu doping concentration of 5%. Moreover, the time-resolved photoluminescence reviled that the same NiO:Cu HTL with Cu doping of 5% has the greatest hole transportation rate. The obtained values Jsc, Voc, FF and η of the champion PSC are 21.26 mA/cm2, 1.033 V, 0.7261, and 15.95%, respectively for RCu/Ni = 5%.
MEMS-SOA Spectrum-Sliced Auto-Equalized Source Enabling Uniformly Tunable Microwave Photonic Filter

In this work, we report a Micro-Electro-Mechanical System (MEMS)-Semiconductor Optical Amplifier (SOA) active interferometer as a low cost and miniaturized tunable broadband sliced source and optical loss auto-equalizer for single pass band microwave photonic filter (MPF). A low finesse Fabry-Perot MEMS interferometer is formed from a movable deeply etched micro-mirror in front of a cleaved fiber facet. The interferometer slices the amplified spontaneous emission (ASE) output of the SOA, while the output is feedback to the SOA again for amplification and optical loss auto-equalization through the SOA gain saturation effect. This configuration improved the uniformity of the MPF passband attenuation along the tuning range down to 3.72 dB only. The filter center frequency can be finely tuned electrically for ±241.6 MHz with a MEMS electrostatic comb actuator driving the mirror. The center frequency is controlled by the distance between fiber facet and micro-mirror. The operation of the proposed MEMS-SOA based MPF is experimentally verified as a proof of concept from 1.2 to 4.5 GHz and can be easily extended to higher frequencies. A tuning resolution of 6.04 MHz/ $\mu \text{m}$ is achieved with stopband attenuation larger than 20 dB and 3-dB bandwidth mean value of 141 MHz.
On Improving Perception for Visually Impaired: Requirements, Research and Practicality

The development of sensory substitution devices for visually impaired is one of the most complex interdisciplinary research areas. These devices exploit the ability of the brain to adapt, learn and process the lost sensory information (vision) using sounds and/or haptics. Starting from previously published research results, this paper explores the users’ requirements and practicality aspects necessary in the design of sensory substitution devices for visually impaired. A new mobile perception architecture is proposed and preliminary results are presented.
ZyON: Enabling Spike Sorting on APSoC-Based Signal Processors for High-Density Microelectrode Arrays

Multi-Electrode Arrays and High-Density Multi-Electrode Arrays of sensors are a key instrument in neuroscience research. Such devices are evolving to provide ever-increasing temporal and spatial resolution, paving the way to unprecedented results when it comes to understanding the behaviour of neuronal networks and interacting with them. However, in some experimental cases, in-place low-latency processing of the sensor data acquired by the arrays is required. This poses the need for high-performance embedded computing platforms capable of processing in real-time the stream of samples produced by the acquisition front-end to extract higher-level information. Previous work has demonstrated that Field-Programmable Gate Array and All-Programmable System-On-Chip devices are suitable target technology for the implementation of real-time processors of High-Density Multi-Electrode Arrays data. However, approaches available in literature can process a limited number of channels or are designed to execute only the first steps of the neural signal processing chain. In this work, we propose an All-Programmable System-On-Chip based implementation capable of sorting neural spikes acquired by the sensors, to associate the shape of each spike to a specific firing neuron. Our system, implemented on a Xilinx Z7020 All-Programmable System-On-Chip is capable of executing on-line spike sorting up to 5500 acquisition channels, 43x more than state-of-the-art alternatives, supporting 18KHz acquisition frequency. We present an experimental study on a commonly used reference dataset, using on-line refinement of the sorting clusters to improve accuracy up to 82%, with only 4% degradation with respect to off-line analysis.