Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dielectric Resonator Fed Wideband Metasurface Antenna With Radiation Pattern Restoration Under its High Order Modes

In this article, a dielectric resonator (DR) excited wideband metasurface (MTS) antenna with differential feeding is proposed, which uses two modes of the MTS and the fundamental mode of the DR to achieve multimode operation and obtain wide bandwidth. Initially, based on the characteristic mode theory, relevant modes of the MTS are analyzed. Then, a DR operating at the fundamental mode ( ${\mathrm {TE}}_{\delta 11}^{\mathrm {x}}$ ) is introduced as the magnetic current source to feed the MTS, exciting the dominant mode and high order mode (HOM) of the MTS simultaneously. However, this HOM has a weak gain at the broadside direction and large side lobes. According to the superposition principle of radiation patterns, the maximum at the broadside generated by the DR is utilized to enhance the boresight gain of the HOM, thus transforming the top-weak radiation pattern of this HOM into a broadside one and improving the gain flatness. Additionally, the differential feeding technique is introduced to improve the asymmetry of radiation pattern. Furthermore, by analyzing the out-of-phase currents and the periodicity of the MTS patches, three measures —loading transverse open-ended slots, shortening periodicity and loading longitudinal slots— are introduced to reduce the high side lobe level of the HOM while preserving radiation patterns undistorted and improving the gain stability. As a result, this HOM and the dominant mode of the MTS are combined with the fundamental mode of the DR, achieving multimode operation. Finally, the proposed antenna was fabricated and measured. The measured results agree well with the simulated ones, indicating an impedance bandwidth of 51.4% (4.38-7.41 GHz), a 5–9 dBi in-band gain and a 15 dB front-to-back ratio (FBR) within the operating band.
PeerBFT: Making Hyperledger Fabric’s Ordering Service Withstand Byzantine Faults

In a blockchain system, there is an entity called an ordering service or miner that is responsible for ordering transactions, building a new block, and broadcasting the new block to all the blockchain peers. In a distributed system, the safety property is violated when an ordering service sends inconsistent blocks to peers and the liveness property is not guaranteed when the ordering service stops sending blocks to peers. Therefore, the ordering service must behave correctly to guarantee safety and liveness in blockchain operations. Hyperledger Fabric, a permissioned blockchain platform assumes the correct behavior of the ordering service. It also adopts an endorsement policy to deal with malicious behaviors of peers. However, a malicious ordering service can easily make the endorsement policy ineffective. Therefore, Hyperledger Fabric is not reliable in an environment where an ordering service may be hacked. In this paper, we propose PeerBFT that enables peers to handle Byzantine faults in the ordering service of Hyperledger Fabric, assuming that <inline-formula> <tex-math notation="LaTeX">$n \geq 3f+1$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the total number of peers and <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> is the maximum number of malicious peers. In PeerBFT, each peer audits the behavior of the ordering service. When a group of peers detects any incorrect operations in the ordering service, they collectively migrate to a new ordering service. We have built a prototype of PeerBFT in Hyperledger Fabric 1.2. It is shown that PeerBFT can handle the Byzantine faults of the ordering service. The experimental evaluation shows that PeerBFT achieves approximately 90.8% of TPS (Transactions Per Second) of Hyperledger Fabric with Solo ordering service.
Investigation of the Radiation Effect on BOTDR Under Low Space Radiation Doses

To verify the feasibility of Brillouin optical time domain reflector (BOTDR) applied in space station, three kinds of commercial fibers, YOFC fiber (about 1 km), Corning fiber (about 1 km) and Fujikura fibers (about 1.5 km), have been radiated by Co60 in radiation experiment. Based on the Brillouin Gain Spectrum (BGS) along sensing fiber, we analyze power curve of Brillouin backscattered light, Brillouin Frequency Shift (BFS) along sensing fiber and spatial resolution carefully. The results indicate that radiation has little effect on BFS when signal-to-noise ratio (SNR) is large enough. However, since radiation will lead to radiation-induced attenuation, the amplitude of BGS will decrease accordingly. And if the SNR becomes small enough, the accurate BFS cannot be obtained. For the stressed fiber section of BOTDR, the result shows that radiation has little effect on the strain coefficient at the total radiation dose of 200 Gy, but radiation will deteriorate the spatial resolution of BOTDR with the increase of radiation dose. These results are good references for the introduction of BOTDR to space station.
Empirical Comparison of Approaches for Mitigating Effects of Class Imbalances in Water Quality Anomaly Detection

Imbalanced class distribution and missing data are two common problems and occurrences in water quality anomaly detection domain. Learning algorithms in an imbalanced dataset can yield an overrated classification accuracy driven by a bias towards the majority class at the expense of the minority class. On the other hand, missing values in data can induce complexity in the learning classifiers during data analysis. These two problems pose substantial challenges to the performance of learning algorithms in real-life water quality anomaly detection problems. Hence, the need for them to be carefully considered and addressed to achieve better performance. In this paper, the performance of a range of several combinations of techniques to deal with imbalanced classes in the context of binary-imbalanced water quality anomaly detection problem and the presence of missing values is extensively compare. The methods considered include seven missing data and eight resampling methods, on ten different learning state-of-the-art classifiers taking into account diversity in their learning philosophies. The different classifiers are evaluated using stratified 5-fold cross-validation, based on three performance evaluation metrics namely accuracy, ROC-AUC and F1-measure. Further experiments are carried out on nineteen variants of homogeneous and heterogeneous ensemble techniques embedded with resampling and missing value strategies during their training phase as well as an optimized deep neural network model. The experimental results show an improvement in the performance of the learning classifiers, especially when dealing with the class imbalance problem (on the one hand) and the incomplete data problem (on the other hand). Furthermore, the neural network model exhibit superior performance when dealing with both problems.
Automatic Annotation Approach for Prohibited Item in X-ray Image based on PANet

In this paper, we propose an approach based on Polygon-Attention Network (PANet) for automatic annotation of prohibited item instances in X-ray images, aiming at accelerating annotation process for new datasets. X-ray image is fully special, mainly because it has a large amount of overlapping phenomenon, resulting in blurred boundaries of prohibited items. To solve this problem, we add an adaptive multi-level attention module to a generic encoder-decoder annotation architecture, which enables the model to adaptively fuse the required middle layer features in real-time according to characteristics of object boundary during each inference. To evaluate the proposed approach, we present a high-quality X-ray segmentation dataset named Prohibited Item X-ray (PIXray). Experimental results demonstrate that, through our approach, the annotation process speeds up by a factor of 2.4 in all classes in PIXray, and achieves an accord of 92.3% with ground-truth in IoU.
Implementation of an Optical Sensor to Detect Fish in Aquiculture Tanks

The increase in the world population increments the demand for fish. In this context, the fish farms have to improve the production in their infrastructures being this very expensive. In this paper, we propose a Low-Cost Wireless Sensor Network based in an optical sensor, for monitoring the presence of sea breams at different distances. In addition, we use a test tube with foil to simulate the similar characteristics of this kind of fishes to perform the tests. We evaluate the use of a light-dependent resistor and a photodiode for the infrared spectrum to detect the object at different distances. Our results indicate that the best is the photoresistor because its data was more accurate than the photodiode. In the experiment, we locate the simulation of fish at different distances of 5 cm, 9 cm, 10 cm, 15 cm, 17 cm, 26 cm, and 37 cm. We measure at different points of the tank measuring the data triplicate. The results displayed that the maximum resistance is 15. 44Ω when the test tube is located near the photoresistor and the minimum 10. 39Ω when the tube is located far away from the photoresistor. Finally, we perform a test using a real fish that we thought in front of the photoresistor. The results show that when fish is in front of the photoresistor, the maximum resistance is 15. 45Ω (with fish) and minimum resistance of 6. 42Ω (without fish). We concluded that we are able to distinguish the presence of fish at different distances using light demand resistors.
Implementing Low Cost and Secure Data Transmission Layer for Image Transmission in Wireless Sensor Network

Wireless Sensor Networks (WSN) continuously send information collected from the environment to the other node and then to the server or main collector. As the network structure grows, the size of the data transfer within the network also increases dramatically. If the network transmits image, the data in traffic also increases. In this study, a low-cost transmission protocol (interlayer) was created for picture transmission in Wireless Sensor Networks. The protocol used uses the pixels that generates the picture. Lena’s 512x512 pixel gray level image was used in the study. The original size is 256 KB, the size of the picture is reduced to 192 KB and a 25% advantage is provided. The result of study reached the value 51,1365 for Structural Similarity Ratio (SSIM) and 0,9976 for Peak Signal Noise Ratio (PSNR). The data is encrypted using the Advanced Encryption Standard (AES) encryption algorithm before being transferred. Thus, a safer transmission layer was created. As a result of the study, according to the information obtained without changing the picture 12.5% and 25% of data transfer to create a transfer layer has been created and data transmission in the Wireless Sensor Networks data transmission is reduced.
A Method to Utilize Mismatch Size to Produce an Additional Stable Bit in a Tilting SRAM-Based PUF

A new concept for Physical Unclonable Functions (PUFs), the Mirror PUF, is proposed. The Mirror PUF can be applied to existing preselected PUF circuits by post-processing the preselection test results and has the potential to double the number of effective bits, with no additional area at the bit-cell level. The Mirror PUF utilizes a preselection test, which measures the amount of mismatch within the PUF cell. This data is generally used to determine the mask for a conventional PUF. Here, it is used as an entropy source that is not correlated to the original response of the PUF. Bit-cells with low mismatch are considered as a ‘0’ and cells with high mismatch as a ‘1’. A systematic method is shown for identifying unstable bits in the Mirror PUF response. It classifies the cells to ‘low’, ‘medium’ and ‘high’ mismatch, such that medium mismatch bits are considered as unstable and masked from the response. The concept was applied to a 65nm Si implementation of the Capacitive Tilt PUF (Shifman et al., 2020). All of the unstable cells of the Mirror PUF, except for 0.03%, were identified and masked, with a worst-case corner Bit Error-Rate (BER) of only 2.35E-5 and 0.00026% erroneous responses across all corners. The application of the Mirror PUF to the Capacitive tilt PUF increased the number of stable response bits by 66%. No observable correlation was found between the responses of the original PUF and the Mirror PUF.
A Privacy-Preserving Game Model for Local Differential Privacy by Using Information-Theoretic Approach

Local differential privacy (LDP) is an effective privacy-preserving model to address the problems which do not have a trusted entity. The main idea of the LDP is to add randomness in real data to guarantee individual’s private sensitive information. Here, the technology of randomized response is an effective method to realize the LDP mechanism. In fact, the randomized response is a probabilistic mapping from the real data to perturbed data, which can be modeled as an information-theoretic lossy compression mechanism. What’s more, the privacy budget <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> has become a <italic>de facto</italic> standard to quantify the worst-case privacy leakage. However, such a metrics can not capture the question that which one is the optimal privacy mechanism in a set of equivalent <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-privacy mechanisms. Besides, the privacy and utility are closely correlated with the privacy mechanism, and existing methods do not consider the strategic adversary’s behavior. In this paper, we tackle the problem of tradeoffs privacy and utility under the rational framework within an information-theoretic approach as the metrics. To address the problem, we first formulate this trade-off as a minimax information leakage problem. Then, we propose a privacy preserving attack and defense (PPAD) game framework, that is, a two-person zero-sum (TPZS) game. Further, we develop an alternating optimization algorithm to compute the saddle point of the proposed PPAD game. As a case study, we apply our method to compare several alternative <inline-formula> <tex-math notation="LaTeX">$\ln 2$ </tex-math></inline-formula>-privacy mechanisms, the experimental result demonstrates that can provide an effective method to compare equivalent <inline-formula> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula>-privacy mechanisms. Furthermore, the numeric simulation result confirms that the proposed method also be useful for the protector to assess privacy disclosure risks.
Incorporating Android Code Smells into Java Static Code Metrics for Security Risk Prediction of Android Applications

With the wide-spread use of Android applications in people’s daily life, it becomes more and more important to timely identify the security problems of these applications. To enrich existing studies in guarding the security and privacy of Android applications, we attempted to predict the security risk levels of Android applications. Specifically, we proposed an approach that incorporated Android code smells into traditional Java code metrics to predict how secure an Android application is. With an evaluation of our technique on 3,680 Android applications, we found that: (1) Android code smells could help improve the performance of security risk prediction of Android applications; (2) By building a Random Forest model based on Android code smells and Java code metrics, we could achieve an Area Under Curve (AUC) of 0.97; (3) Android code smells such as member ignoring method (MIM) and leaking inner class (LIC) have a relatively-large influence on Android security risk prediction, to which developers should pay more attention during their application development.
A Fault-Tolerant Algorithm to Enhance Generalization of EMG-based Pattern Recognition for Lower Limb Movement

Real-time application of exoskeleton remains a challenge due to the limited stability of electromyogram (EMG) collected on lower limb. To enhance generalization of EMG based pattern recognition (PR), this work proposed a novel fault-tolerant algorithm based on conventional liner discriminant analysis (LDA). Based on two patterns of EMG collection (static data and dynamic data), the most accurate feature set was first selected in static data to guarantee basic performance of LDA. Detections of LDA formed a decision stream, and provided later analysis with information of movement and muscle activity of lower limb. A BP classifier was trained based on the decision stream and features set to recover misclassification of LDA when interference occurred. Both LDA and BP were trained on static data. Dynamic data were set to imitate instability of EMG in realistic application such as different force intensity and muscle fatigue. The proposed algorithm was tested on a EMG based PR system. 4 able-bodied subjects participated in the experiment. The enhancement in classification accuracy ranged from 7.52% to 13.94% over subjects compared with unoptimized system. After EMG channels reduced, this enhancement ranged from 9.01% to 27.35%, which significantly improved classification performance under limited condition. These results suggested that the proposed algorithm yield excellent performance in improving the generalization of EMG based PR.
System for Monitoring Medical Rehabilitation Activities Using Virtual Reality – Case Study MIRA

Patients recovery is usually a long process requiring professional supervision and an important number of patients are unable to finalize the rehabilitation program because they consider the exercises to be difficult and repetitive. Starting from this finding many scientific studies researched for various ways of improving thee patients’ spirit managing to include elements that keep them involved and focused. The platform Medical Interactive Recovery Assistant – MIRA was created in this respect, transforming the physical rehabilitation exercises in video games. This platform provides a set of medical games based on motion, working by means of a special video cam, Microsoft Kinect, suitable to be used in both hospitals and homes. The connection between the patient and the kinetotherapy laboratory in the hospital is done by help of the Microsoft Kinect sensor. This paper deals with the analysis of the MIRA software by case studies based on gathering statistical data, analyzing them and using the results in order to improve the rehabilitation performance. The results obtained by the patients are presented in graphical form and a thorough SWOT analysis is performed in order to highlight the opportunities offered by the virtual reality platform.
Improvements of a Simple Piezoresistive Array Armband for Gesture Recognition

This study presents a simple Human Machine Interface (HMI) for gesture recognition purpose, based on a wireless piezoresistive armband. The armband embeds three sensors based on Force Sensitive Resistors (FSRs) applied on specific forearm muscles, which provide signals comparable to the electromyography linear envelope. The system aims to recognize in real-time some hand gestures, opportunely processing the force signals. The HMI control system is based on Arduino platform and implements a Linear Discriminant Analysis (LDA) classifier to perform real-time gesture recognition. The HMI, by means of a Bluetooth module can wireless connect to a computer and provide commands to custom graphical interfaces or other applications as videogames. Four healthy volunteers were involved in testing the device. A double threshold segmentation technique, applied to the recorded FSR signals, allowed to detect and extract features regarding the whole-time evolution of a performed gesture, explicitly considering the transition between gestures. LDA classification performances were assessed by applying the 10-fold cross validation. The mean accuracy across all participants resulted 93.48%. The results suggest that the HMI device allows easy user’s interaction with a computer in real-time. In prospective, this device could find practical applications in neuromotor rehabilitation (e.g. exergaming), and in controlling prosthetic hands.
Fault diagnosis of rotating machinery based on FA-DSAE

As one of the most widely used equipment, rotating machinery fault diagnosis is significant. The data-driven methods aim at mining the relationship between monitoring data and fault and realizing automatic and accurate equipment fault diagnosis results. However, current fault diagnosis methods generally depend on prior knowledge and the experienced diagnosticians, unsuitable for a variety of conditions. The deficiencies limit the development of intelligent fault diagnosis technology of rotating machinery. In this research, an adaptive fault diagnosis method of rotating machinery based on firefly algorithm and deep sparse autoencoder (FA-DSAE) is proposed to ensure accurate and stable diagnosis result while greatly reduce the dependence on expert experience and prior knowledge. The method is applied for roll bearing and hob fault diagnosis and have a good performance.
A RESTful Rule Management Framework for Internet of Things Applications

Web technologies are currently regarded as key enabling factors for the Internet of Things (IoT), and substantial effort is being dedicated to bringing sensors and data from the real world to the Web. In addition, rule-based automation mechanisms are expected to play a significant role in the effective integration of the physical world with the virtual world by leveraging a trigger-action paradigm. Although several rule engines are already available, limited effort has been devoted to rule-based solutions that are tailored to the IoT and consider rule configurability and extensibility according to application requirements. In this work, we propose a RESTful rule management framework for IoT applications that satisfies these requirements. The framework is centered around a resource-based graph, which enables the uniform representation of things (e.g., sensors and domain entities) and rules as URI-addressable resources. We describe the design and implementation choices of the main rule management features (rule scheduling, activation and RESTful operations for managing rules at various levels of configurability and extensibility). Finally, we present a case study and performance evaluation results regarding the use of this rule management framework in a set of school buildings that were part of a real-world IoT deployment that was realized within the Horizon 2020 GAIA research project, with the objective of promoting energy -saving behaviors in school communities.