Miracle-dz/UniFAR-FTUs huggingface dataset:
TrafficChain: A Blockchain-Based Secure and Privacy-Preserving Traffic Map

Intelligent Connected Vehicles (ICVs) can provide smart, safe, and efficient transportation services and have attracted intensive attention recently. Obtaining timely and accurate traffic information is one of the most important problems in transportation systems, which would allow people to select fast routes and avoid congestions, thus saving their travel time on the road. Currently, the most popular ways to obtain traffic information is to inquire navigation agents, e.g., Apple map, and Google map. However, these navigation agents are essentially centralized systems, which are vulnerable to service congestions, a single point of failure, and attacks. Furthermore, users’ privacy gets compromised as the agents can know their home and work addresses and hence their identities, track them in real-time, etc. In this paper, we propose TrafficChain, a secure and privacy-preserving decentralized traffic information collection system on the blockchain, by taking advantage of fog/edge computing infrastructure. In particular, we employ a two-layer blockchain architecture in TrafficChain to improve system efficiency, design a privacy-preserving scheme to protect users’ identities and travel traces, and devise LSTM based deep learning mechanisms that can defend against Byzantine attacks and Sybil attacks in our system. Furthermore, an incentive mechanism is designed to motivate users to participate in the system. Simulation results show that TrafficChain works very efficiently and is resilient to both Byzantine attacks and Sybil attacks.
Filter-bank local region complex-valued common spatial pattern for Motor imagery classification

This paper represents a novel motor-imagery (MI) classification method based on a local region filter-bank common spatial pattern (LRFBCSP) using complexed form of electroencephalography (EEG) signals. LRFBCSP approach selects the MI-relevant local region which is constructed by individual channels and their neighbors by comparing their eigenvalue disparity. We propose an extension version of the LRFBCSP by considering the complex-valued spatial filtering rather than the real-valued spatial filtering. The complex-valued spatial filtering improves the discrimination of each local region and provides enhanced CSP features. Simulation result shows the performance improvement of the proposed method for BCI competition III dataset IVa by comparing the CSP-based methods.
Study of Interruptible Load Utility Model for Building Users

Effective utilization of interruptible load in buildings can not only improve the flexibility of power grid and enhance the reliability of power grid, but also alleviate the imbalance between supply and demand. According to interruptible load contract, building users can benefit from participating in dispatching. However, it deviates from the original load profile, and user comfort will also be affected. Therefore, in order to utilize interruptible load flexibility of buildings, a utility model including benefits and comfort is proposed in this paper, wherein the calculation of benefits considers users' psychological activities through prospect theory. On this basis, the optimal model of interruptible load of building users with the goal of maximum utility is established. Finally, by an example of a real commercial building, the validity of the proposed model is verified. Meanwhile, the results show that the utility model can effectively evaluate the load characteristics of buildings and guide them to participate in the optimal dispatch of power grid demand response.
Fuzzy Controller based Energy Management of Hybrid System

Meeting the energy demand of the users in isolated areas using various renewable energy sources is a major challenge. Standalone microgrid is an effective solution for the electricity needs in remote areas. Electricity generation using energy sources like solar which depends highly on the weather conditions, can be unpredictable. Availability of such renewable energy sources may not match with the load demand. Thus providing electricity on a regular mode in all times to meet the demand is a big challenge. Implementation of a controller in the system helps in solving this problem. In this work, selection of different sources in a hybrid solar and diesel system according to the weather conditions using a fuzzy logic controller is implemented. This proposed method also provides constant dc bus voltage in the system.
Directional Sparse Filtering for Blind Estimation of Under-Determined Complex-Valued Mixing Matrices

We propose an algorithm that exploits the benefits of sparse filtering and directional clustering when estimating under-determined mixing matrix from mixtures of sufficiently sparse sources. To express the direction of each sample by only a few vectors in which one vector is more dominant than the remaining ones, we propose to minimize the power mean of the magnitude-squared cosine distances between the estimated mixing matrix and the data. For the special case of estimating determined mixing matrix, we derive a stability condition for methods based on the magnitude-squared cosine metric. Our stability condition shows that the proposed approach, K-hyperlines, and sparse filtering can recover the invertible mixing matrix when the sources are i.i. d. super-Gaussian. Simulations using both synthetic data and recorded speech mixtures show that the proposed algorithm outperforms existing algorithms with lower computational complexity.
A Novel Miniaturized WLAN/WiMAX Antenna Inspired With Metamaterial

A compact low-profile planar antenna for WLAN and WiMAX applications based on multiplex usage of left-handed metamaterial (LHM) is presented in this paper. The antenna is a double-sided dual-band LHM surrounded by a G-style monopole antenna. Because of the near field resonant parasitic (NFRP) theory, the employment of LHM unit shifts the passing bands of the G-style monopole to lower frequencies, satisfying the 2.45GHz and 5.5GHz band requirements, respectively; on the other hand, the LHM unit is also serving as a loop antenna fed through the coupling with the G-style monopole, operating a third passing band of 3.5 GHz. A prototype of the proposed antenna is fabricated. The simulated and measured results match well, identified good radiating performance.
Experimental Researches on Determination of Angles of Side Illumination by Laser Radiation of Optical Devices and Optoelectronic Devices

The work is devoted to the research of laser illumination of optical and electronic means of observation and intelligence of military and civilian use. The purpose of the research was to experimentally determine the angles of lateral illumination by laser radiation of optical-electronic means, taking into account the features of the design and modes of adjustment of the lenses, which cause a significant loss of information component obtained in real time. Known open publications in this area do not fully reflect the dynamics of the effects of laser radiation, and highlight the ultimate maximum effect of dazzle or have finite energy performance lesions to the receiving matrices of optoelectronic means. The results obtained by the method of physical experiment showed that the value of the side-illumination angle depends on the design of the lens. The angle of side illumination increases, depending on the increase in the field of view of the lens, the greater the angle between the optical axis of the lens and the tangent to its body (the constructive surface of the external lens). Side illumination also depends on the presence of protective glass, which scans laser radiation and, as a consequence, affects the regular operation of optoelectronic devices. The results obtained can be used as initial data in the military sphere - in solving problems of counteraction to opto-electronic means; law enforcement experts, developers of optical devices and opto-electronic devices, as well as specialists in telecommunications specialties - in solving the problems of protection of video surveillance systems, etc.
Chronic Kidney Disease (CKD) Diagnosis using Multi-Layer Perceptron Classifier

Chronic Kidney Disease or CKD is one of the most widespread Kidney diseases that affect people on a larger scale. It gives rise to other biological problems like weak bones, anemia, nerve damage, high blood pressure and can even lead to complete kidney failure. Millions of deaths are caused each year because of CKD. The diagnosis of CKD is a problematic job as there is no major symptom that serves a classification feature in detecting this disease. This paper proposes a Multi-Layer Perceptron Classifier that uses a fully connected Deep Neural Network to predict whether a patient suffers from the problem of CKD or not. The model is trained on a dataset of around 400 patients and considers various symptoms like blood pressure, age, sugar level, red blood cell count, etc. that assist the model in performing accurate classification. Our experimental results show that the proposed model can perform classification with the testing accuracy of 92.5&, surpassing the scores achieved by SVM and Naïve Bayes Classifier.
Spatial-Musical Association of Response Codes Based on Trained Visual Shapes

The spatial-musical association of response codes (SMARC) effect, in which a high-pitched (low-pitched) tone facilitates an upper (lower) response, is considered to reflect the spatial coding of sound pitch. However, previous studies have not excluded the directional effects of sound localization. Because a high-pitched (low-pitched) tone is automatically misperceived as originating from a spatially high (low) location, the location of a perceived sound source might artificially elicit the SMARC effect. This study challenged this issue, by demonstrating whether the SMARC effect occurs in response to sound imagery without sound input. To achieve this goal, participants were first trained to associate visual stimuli (novel contoured shapes) with sound pitches (high-pitched or low-pitched pure tones). Subsequently, they elicited the SMARC effect for trained visual stimuli (novel contoured shapes) associated with pitch height. Therefore, the SMARC effect was observed without sound localization but with the activation of sound imagery that was spatial.
Segmental Symbolic Execution Based on Clustering

As the impact of security vulnerabilities on information systems becomes more and more serious, program analysis and vulnerability discovery techniques play an increasingly important role in the field of information security. Among many binary program analysis techniques, dynamic symbolic execution technology has been deeply researched and widely applied as an important automated test and vulnerability discovering technology in the information security field. Aimed at the existing problems in dynamic symbolic execution, this paper proposes a binary program segmental symbolic execution approach based on a clustering algorithm. Different from the previous approach of dividing the program segment according to the function process or method in the program, the proposed approach divides the program into larger segments by an improved GN algorithm, and then performs dynamic symbolic execution on each segment. Finally, the results are merged to complete the analysis of the entire program. In this paper, the approach is compared with the regular symbol execution using angr, and the experimental results show the effectiveness of the proposed approach in its time consumption, calculation and storage resource occupation.
Trust and Reputation in the Internet of Things: State-of-the-Art and Research Challenges

This paper deals with the principal aspects emerging from the application of trust techniques to the IoT domains with respect to the particular viewpoint of an IoT environment. We consider intelligent agents technology to add social behavior to the community of the smart objects, and we analyze the fundamental role of the concepts of trust and reputation in this perspective. Also, we analyze the existing architectures for IoT and explain how to integrate IoT with fog/edge computing. Besides discussing the main proposals present in the literature, the key contribution of this paper consists of providing a comparative study of the main current architectures for modeling trust in IoT environments. In this setting, we propose a comparison based on the important characteristics of the IoT layer and the architecture type. Such an analysis allows us to highlight both advantages and limitations of each approach, and to discuss the emerging research challenges.
Investigation on Range Sidelobe Modulation Effect of Co-designed Radar-Communications Shared Waveforms

The growing demand for radio frequency (RF) spectrum requires the sharing of frequency bandwidths among different users. Variant signaling strategies have been proposed to embed communication information into the radar waveforms to achieve coexistence between radar function and communication systems, which has alleviated the competition for spectrum resources. However, the waveform changes in dual function systems may affect the radar performance. In this paper, we investigate the RSM effect of two commonly-used information embedding radar waveforms, which is never discussed in previous works. Simulation results show that the two sets of co-designed radar-communications shared waveforms can achieve good radar detection performance when we employ appropriate processing methods. Both schemes are discussed in pulse-Doppler radar systems equipped with a uniform linear array of N antennas.
Correlating MSM's Mental Health with Usage Behaviors on MSM-Specific Social Applications

Homophobia and discrimination towards men who have sex with men (MSM) make the mental health of this community a severe concern. The prevalence of mobile social apps for MSM provides a new channel to study their mental health issues. However, the correlation between the psychological states of MSM and their behaviors on social apps are still uninvestigated. In this paper, we conduct a case study of 103 MSM in China to explore whether the User Generated Content, Profile, Mobility information of MSM on social apps correlate with their depression, loneliness, anxiety and stress level. The analysis results indicate that the mental health disorder level of MSM is correlated with the emotion expressed in posts, whether the user has filled in self-description blank, the social relationship among online friends, frequency of visiting popular gays-meeting places. Our findings imply future opportunities in early psychological problems detection and intervention.
A High-Performance Parallel Hardware Architecture of SHA-256 Hash in ASIC

The SHA-256 algorithm is used to ensure the integrity and authenticity of data in order to achieve a good security thus is playing an important role in various applications, such as e-transactions and bitcoins. The SHA-256 computation capacity is a main research direction of Hashing Algorithm. In order to improve the computation capacity of hardware, the proposed design first uses pipeline principle and circuitry of timing prediction to find a most efficient architecture for implementation. Then it is optimized with hash function and hardware characteristics to give a high-performance hardware architecture of SHA-256 hash. Three pipelines are used to replace the critical path in the round functions which can shorten the timing path, and divide the computation chain into independent steps. Multi-computation of SHA-256 is working in parallel pipelines, indicating that the computation capacity can be 3 times of that with standard SHA-256 implementation. The proposed SHA-256 hardware architecture has been implemented and synthesized with Intel 14nm technology. Simulation and synthesis results show the proposed SHA-256 hashing throughput can be improved by 3 times with 50.7% power reduction, at an area cost of 2.9 times compared to that of the standard implementation.
Optimization of a customized Simultaneous Algebraic Reconstruction Technique algorithm for breast CT

Iterative CT reconstruction algorithms coupled with edge-preserving filters are attracting a growing interest in the field of biomedical X-ray imaging. In many cases the application of such algorithms results in an improved reconstruction quality when compared with filtered back-projection (FBP). Iterative algorithms commonly entail a decrease of image noise or, equivalently, an increase of contrast-to-noise ratio, while preserving image detail. Conversely, they modify the shape of noise power spectrum, producing a shift towards lower spatial frequencies with respect to FBP. This results in a "patchy" or "waxy" appearance of the reconstructed images. Changes in image texture affect radiologists’ perception of image quality, possibly influencing their willingness to use an iterative algorithm in clinical practice. In this work we present a GPU implementation of a simultaneous algebraic reconstruction technique algorithm, combined with a bilateral regularization filter, and we discuss the optimization of the algorithm’s parameters in terms of noise texture, selecting those parameters which preserve its "natural" appearance. We evaluated the performances of the algorithm, compared to FBP, both on a test phantom and on a surgical mastectomy sample by using contrast-to-noise ratio and spatial resolution metrics. Samples were imaged at the SYRMEP beamline of the Elettra synchrotron facility with a monochromatic beam (32 keV) in propagation-based phase-contrast configuration, delivering a clinically-compatible radiation dose of 5 mGy and using a large-area CdTe photon-counting detector. Results show, in the specific application on breast specimens, that the implemented algorithm can be tuned to preserve the noise texture and spatial resolution observed in FBP reconstructions, while improving contrast-to-noise ratio up to 30%.
Implementation and Analysis of a Serverless Shared Drive with AWS Lambda

Serverless computing is an emerging new paradigm in cloud computing. Serverless denotes a special kind of software architecture in which application logic is executed in an environment without visible processes, operating systems, servers or virtual machines. Serverless enables the deployment of applications and services to the cloud as code snippets or functions as opposed to a monolithic program. While serverless is rapidly evolving, it lacks a standard definition and general consensus within the technical community but has the potential to massively alter the way in which software delivers business value. This work presents the architecture and implementation of a shared drive web application using AWS Lambda. The application is tested to compare the response time for cold and warm requests, effect on load balancing, performance on memory reservation and resource retention behavior. Additionally, the gaps and limitations in current serverless architecture are discussed.
AI Powered Unmanned Aerial Vehicle for Payload Transport Application

Recently unmanned aerial vehicles (UAV) have received a growing attention due to their wide range of applications. Here, we demonstrate UAVs with artificial intelligence (AI) capabilities for application in autonomous payload transport. An algorithm is developed for target detection with multiple phases on the ground, which once the target is detected, would trigger the release of the payload that is attached on the drone. The experimental results show that the average frame rate over x seconds achieved a 19.4010717352 fps (frame per second) detection speed. Releasing the payload is achieved using a 3D printed system based on rack and pinion gears. In addition, auto flight program is developed to enable the autonomous movement of the drone. As a proof-of-concept, a small drone known as "Phantom DJI" is used for . 6 kg autonomous payload transport along a predefined route to a target location.
Simulation of Solar Energy Photovoltaic Conversion

Renewable energy sources, namely, the energy of the sun, wind, water, etc., are of great importance in solving electricity needs. The main source of energy on the earth is the sun. At present, solar energy is one of the most promising directions in the development of renewable energy sources. For the efficient use of solar energy it is necessary to have an opportunity to predict its power under the influence of various factors. The task of modeling the photovoltaic conversion of solar energy is particularly relevant in connection with the trend of a gradual transition to renewable energy sources. Solar panel of the Mitsubishi PV-MLE255HD type in the software and hardware system of the real-time “RTDS” is simulated in the article. The model was developed using basic equations of photovoltaic solar cells which take into account the effects of solar radiation and temperature drops.
An Improved ACO with First-job Restriction and P-ANT for Batch Scheduling Problem

This research applies ant colony optimization (ACO) algorithm to minimizing the makespan and the maximum tardiness for scheduling the jobs with non-identical sizes and release time on parallel batch process machines. To improve search efficiency, a new first job selection strategy and a dynamic heuristic are provided. Additionally, a new role, called P-ANT, is designed to improve the search ability of the ant colony. To solve the studied problem, a novel multi-objective ACO algorithm with P-ANT (MACOP) is proposed. The MACOP is compared with several other state-of-the-art algorithms through extensive experiments. The results demonstrate the effectiveness of the proposed MACOP algorithm.
Persistent Cooperative Monitoring System of Disaster Areas Using UAV Networks

Information acquisition and rescue operation management can be facilitated by the use of unmanned aerial vehicles (UAVs) equipped with cameras and sensors that are capable of aerial photography and acquire important information from disaster-stricken areas. UAV technology is a relatively low-cost, flexible, and safe solution in missions that are difficult for humans or other systems. UAV usage in pre-and post-disaster assessment, response, and management is an active area of research. There are two important technical aspects of UAV applications in disaster situations. The first is the limited communication range. Information acquired and photography produced by UAVs are only transmitted in real-time to the ground control station (GCS) within its communication range. Remote real-time monitoring of distant areas beyond the UAV's communication range is not possible using a single UAV. It is only possible through communication among several UAVs. The second is the relatively short flight time due to limited battery capacity. UAV replacement is required in order to carry out a persistent monitoring mission. In this paper, we propose a new remote real-time monitoring system of a distant disaster area using multiple UAVs. Wireless communications between multiple UAVs constitutes a UAV network in our proposed system. Moreover, the proposed system replaces the UAVs in-flight with standby UAVs according to the remaining energy levels. We compare the performance of the proposed system with an existing UAV system. Our simulation results show that the proposed system can achieve efficient remote real-time monitoring.