Miracle-dz/UniFAR-FTUs huggingface dataset:
UHF RFID Channel Emulation Testbed for Wireless IoT Systems

We propose an ultra high frequency (UHF) radio frequency identification (RFID, 902–928 MHz in the US) channel emulation testbed that is capable of simultaneously emulating unique wireless channels. The proposed system can potentially be an invaluable tool in the design and validation of RFID-based Internet of Things (IoT) sensors and systems. Emulation of ray-tracing-based wireless channels enables the evaluation of inherently difficult and complex radio frequency (RF) scenarios, particularly in situations when in-person experimentation is not feasible or desirable (e.g., during a pandemic or in a critical care facility). Furthermore, the emulation testbed is able to generate a large amount of sensor data in a limited time period. Machine learning techniques used in wireless IoT can be greatly enhanced by a large amount of data extracted from the emulation of dynamic and challenging environments. The proposed multi-channel emulation testbed is therefore a valuable solution for experimentation on real hardware and a convenient tool for pre-clinical-trial system validation.
Polarization-Maintaining Multi-Core Few-Mode Fiber With a Cladding Diameter of 125 μm

We present the theoretical study of polarization-maintaining multi-core few-mode fiber (PM MC-FMF) with a cladding diameter of 125 μm, in order to secure the maximum number of independent spatial channels arising in the single fiber. By taking both the inter-core crosstalk (XT) and the intra-core mode coupling into account, we identify that the PM five-core three-mode fiber is able to provide 19 parallel channels without the need of MIMO signal processing. After the numerical optimizations, the inter-core XT of MC-FMF is less than −30 dB/100km, due to the use of trench-assisted heterogeneous core distribution. Meanwhile, the effective refractive index (RI) differences among both spatial modes and polarization modes are more than 8 × 10−4 and 3.26 × 10−4 over the C+L band, respectively, due to the introduction of stress and geometric birefringence. Moreover, the cladding diameter of such MC-FMF is the same as that of the standard single-mode fiber (SSMF), leading to a convenient upgrading from the SSMF to the MC-FMF.
Testing Triangle Freeness in the General Model in Graphs with Arboricity O(√n)

We study the problem of testing triangle freeness in the general graph model. This problem was first studied in the general graph model by Alon et al. (SIAM J. Discret. Math. 2008) who provided both lower bounds and upper bounds that depend on the number of vertices and the average degree of the graph. Their bounds are tight only when $d_{\rm max} = O(d)$ and $\bar{d} \leq \sqrt{n}$ or when $\bar{d} = \Theta(1)$, where $d_{\rm max}$ denotes the maximum degree and $\bar{d}$ denotes the average degree of the graph. In this paper we provide bounds that depend on the arboricity of the graph and the average degree. As in Alon et al., the parameters of our tester is the number of vertices, $n$, the number of edges, $m$, and the proximity parameter $\epsilon$ (the arboricity of the graph is not a parameter of the algorithm). The query complexity of our tester is $\tilde{O}(\Gamma/\bar{d} + \Gamma)\cdot poly(1/\epsilon)$ on expectation, where $\Gamma$ denotes the arboricity of the input graph (we use $\tilde{O}(\cdot)$ to suppress $O(\log \log n)$ factors). We show that for graphs with arboricity $O(\sqrt{n})$ this upper bound is tight in the following sense. For any $\Gamma \in [s]$ where $s= \Theta(\sqrt{n})$ there exists a family of graphs with arboricity $\Gamma$ and average degree $\bar{d}$ such that $\Omega(\Gamma/\bar{d} + \Gamma)$ queries are required for testing triangle freeness on this family of graphs. Moreover, this lower bound holds for any such $\Gamma$ and for a large range of feasible average degrees.
An Improved Off-Grid Algorithm Based on CoSaMP for ISAR Imaging

In the application of ISAR imaging based on compressed sensing recovery algorithm, due to the division of sparse basis discrete grids, off-grid phenomenon often occurs, which greatly reduces the imaging quality. This paper proposes an improved algorithm based on compressive sampling matching pursuit (CoSaMP) to solve the off-grid problem in CoSaMP by linearizing the model. The effectiveness of the algorithm is verified by the measured data of Mig-25.
Raining Detection with Deep Learning Method For Vehicle System

This research develops the raining detection using deep learning method. A training model is established through a neural network-like architecture to classify the images recorded by the vehicle camera into three classes: no rain, heavy rain, and light rain. The classification results are used to control the speed of the motor to implement an automatic wiper control system. The training model with ResNet is used to classify the image. For real-time application, the camera module on the Google Coral Dev board embedded development platform is used to test the video stream and to estimate the performance of this system. Results show that the recognition accuracy reaches 95%, and the processing speed can achieve 20 frames per second (fps).
Design of a Half-Wave Dipole Antenna for Wi-Fi & WLAN System using ISM Band

Due to the low cost and high data transmission speed, the demand for wireless communication systems (WLAN, Wi-Fi) is increasing day by day. Nowadays, high-speed data transmission can be provided to a whole area or a building using WLAN and Wi-Fi systems. Since better performances and efficiency are expected for WLAN and Wi-Fi systems in the future, considering these issues, a Half-wave Dipole Antenna has been designed with an operating frequency of 2.45 GHz (ISM-Band). The antenna has been developed using the Copper (annealed) and the Perfect Electrical Conductor (PEC) material in the CST Studio Suite 2019 Software. After completing the simulation of the antenna, a Return Loss of -31.019 dB has been successfully achieved. A Gain of 2.026 dBi and a Directivity of 2.032 dBi was also achieved, respectively. The antenna's efficiency was determined as 99.70%, which was calculated by using the value of Gain and Directivity. The other obtained performance parameters of the antenna are Surface Current, Bandwidth, etc. Considering all of the above performance parameters, the simulated Half-wave Dipole Antenna will be an ideal option for the WLAN and Wi-Fi systems.
Resilience of Autonomous Vehicle Object Category Detection to Universal Adversarial Perturbations

Due to the vulnerability of deep neural networks to adversarial examples, numerous works on adversarial attacks and defenses have been burgeoning over the past several years. However, there seem to be some conventional views regarding adversarial attacks and object detection approaches that most researchers take for granted. In this work, we bring a fresh perspective on those procedures by evaluating the impact of universal perturbations on object detection at a class-level. We apply it to a carefully curated data set related to autonomous driving. We use Faster-RCNN object detector on images of five different categories: person, car, truck, stop sign and traffic light from the COCO data set, while carefully perturbing the images using Universal Dense Object Suppression algorithm. Our results indicate that person, car, traffic light, truck and stop sign are resilient in that order (most to least) to universal perturbations. To the best of our knowledge, this is the first time such a ranking has been established which is significant for the security of the data sets pertaining to autonomous vehicles and object detection in general.
Detecting System Alzheimer’s Dementia by 1d CNN-LSTM in Japanese Speech

Dementia patients have increased year by year in many developed countries. We need to solve this problem. The questionnaire method is generally used for present dementia screening. However, this method requires time for 10 to 15 minutes with a doctor and a clinical psychologist, which puts a burden on hospitals and test subjects. For this reason, we propose a detecting method using deep learning by means of 1d CNN-LSTM to construct a dementia discriminant system using speech voice. Our method obtained the Accuracy of 0.908 and the F-measure of 0.897.
CRTSII Track Slab Crack Detection Based on Improved YOLOv3 Algorithm

As one of the achievements of our country's highspeed railway, CRTSII type ballastless track, has been widely digested, absorbed and re-innovated by researchers. In the maintenance phase, however, the traditional artificial track slab crack detection method still exists some problem, such as time-consuming, low detection accuracy and difficult to detect small cracks, etc. To solve those problems, an improved YOLOv3 (You Only Look Once) algorithm is proposed. In the residual module of feature extraction network, we introduce a deep separable convolution with an inverted residual structure and SENet (Squeeze and Excitation), which reduces network parameters while appropriately deepening the depth of the network. In order to improve the accuracy of small target cracks identification, we firstly adopt the Mish activation function with better stability and accuracy, then introduce the path fusion method in the feature. Experimental results show that the accuracy of the improved YOLOv3 is 5.3% higher and the speed is 26% increase than the original YOLOv3 network. Compared with the traditional crack image processing technology, the method in this paper has better detection effect and robustness, which has a good application prospect in the detection of track slab cracks.
On the Seamless Integration and Co-Existence of Chipless RFID in Broad IoT Framework

RFID has permeated in wide-ranging applications that may eventually fall under the broad IoT umbrella. However, the adaption of RFID for commercial purposes of IoT-centric applications still faces numerous technical challenges due to its high cost. The Chipless RFID can become a boon considering its significantly reduced cost and tag complexity in this context. In this article, therefore, different aspects of the chipless RFID are discussed and analyzed, and a potential road map is explored for the effective seamless integration of chipless RFID in the amalgamated IoT. At first, a brief theoretical preliminary about the Chipless RFID is presented, and it is succeeded by the application’s specific roles of this emerging RFID in the wide variety of domains. Subsequently, the analysis of various physical parameters shows that chipless RFID technology can greatly broaden IoT’s horizon. It essentially shows that it has the potential to bring a paradigm shift in the way RFID can coexist with IoT. Finally, a section which briefs about the future directions is also included to throw some light on Chipless RFID’s direct fitment in the IoT.
Entropy based Defensive Mechanism against DDoS Attack in SDN-Cloud enabled Online Social Networks

Online social network (OSN) is now the part of every human life. Users create social relationships with their friends and family members. Today the OSN based on SDN-Cloud becomes the best computation platform for consumers and providers to use scalable and cost-effective services. But at the same time it also has to face many security threats and issues such as privacy, data protection, malwares and virus, sniffing, DDoS, and traffic monitoring. Amongst all threats, Distributed Denial of services (DDoS) is the most severe threat as it utilizes multiple distributed attack sources. We know that every network in the system has an entropy based on their randomness, and increases in the randomness causes entropy to decrease. Therefore, to enhance the detection and mitigation capabilities of a DDoS attack, we use POX controller under the Software Defined Network Cloud OSN. In this paper, we proposed a defensive mechanism to detect such attacks based on the entropy variation of the destination IP address. And then based on entropy value, we block that specific port in SDN-cloud enabled OSN to fight against DDoS threats. We use OpenFlow switches in SDN environment in the Mininet emulator tool. We performed extensive simulation-based experiments and the generated results are used to validate our claims.
Reactive Power Management and Protection Coordination of Distribution Network with High Solar Photovoltaic Penetration

Distribution generation is greatly supporting the utility grids in balancing the power demand and maintaining the power quality. The main contributors of distributed generation are renewable energy resources such as wind and photovoltaics. However the increased penetration levels of these renewable energy resource, which are intermittent, causing certain technical issues such as power quality and protection coordination. This paper deals with the protection coordination and reactive power issues due to higher renewable energy penetration. The power distribution network of a known industry having large PV power plant is taken for case study. Simulations are performed at various PV penetration levels with E-TAP software to know the effects on reactive power and relay coordination by creating fault at different locations of distribution network. All the results are tabulated and analyzed with the help of spread sheets and SPSS software. Conclusions are drawn with a direction for the need of certain policy guidelines and standards for future work.
A Study on the Development of Traffic Safety Risk Information Sharing Technology through Vehicle-Road Cooperation

This paper proposes a method for the development of technology to share traffic safety risk information that combines driving situation information between other vehicles and road environment information to support safe driving of intelligent autonomous vehicles. Recently, various technologies related to the safety of self-driving vehicles are being developed. In particular, technologies are being developed to recognize road driving information and share it with surrounding vehicles. By providing and sharing driving situation information through communication with objects around the road, up to V2V, V2I, and V2X, it has the advantage of guiding driving situation information that was not previously recognized to subsequent vehicles. In addition, it will be possible to support safer self-driving by sharing information (tunnel, road line, traffic facilities, etc.) corresponding to road environment information. This paper recognizes driving conditions such as driving, driver, road conditions, and object emergence by applying the driving condition recognition technology. It also presents a way to provide traffic safety information to local driving vehicles by collecting and sharing information on road structures for the recognition of road environment. However, since not all vehicles are equipped with dedicated terminals to receive information, this study suggests a way to provide traffic safety information with smartphones. Therefore, it is possible to reduce traffic accidents and smooth traffic flow by suggesting a method of collecting the recognized traffic safety support information in a centralized manner and providing it back to the driver's smartphone of vehicles driving in a corresponding region.
An Integrated Architecture for Maintaining Security in Cloud Computing Based on Blockchain

Due to its wide accessibility, cloud services are susceptible to attacks. Data manipulation is a serious threat to data integrity which can occur in cloud computing – a relatively new offering under the umbrella of cloud services. Data can be tampered with, and malicious actors could use this to their advantage. Cloud computing clients in various application domains want to be assured that their data is accurate and trustworthy. On another spectrum, blockchain is a tamper-proof digital ledger that can be used alongside cloud technology to provide a tamper-proof cloud computing environment. This paper proposes a scheme that combines cloud computing with blockchain that assures data integrity for all homomorphic encryption schemes. To overcome the cloud service provider’s (CSP) ultimate authority over the data, the proposed scheme relies on the Byzantine Fault Tolerance consensus to build a distributed network of processing CSPs based on the client requirements. After certain computations performed by all CSPs, they produce a master hash value for their database. To ensure immutable data is produced, master hash values are preserved in Bitcoin or Ethereum blockchain networks. The master hash values can be obtained by tracking the block header address for verification purposes. A theoretical analysis of the overhead costs associated with creating master hash values for each of the cryptocurrencies is presented. We found that Ethereum leads to lower client financial costs and better online performance than Bitcoin. We also specify the data security requirements the proposed scheme provides, the ground-level implementation, and future work. The proposed verification scheme is based on public cryptocurrency as a back-end service and does not require additional setup actions by the client other than a wallet for the chosen cryptocurrency.
A New Concept for Multiplexers in Interconnect Blocks of FPGAs

The importance of FPGAs in modern electronic components has remarkably grown over the years and FPGAs play a significant role in various applications nowadays. A comparably big portion of dissipated power goes back to the inter-connect logic inside of each reconfigurable chip. Programmable switch matrices are used in FPGA to realize connections between different logic blocks. These matrices consume a large percentage of the overall fabric and optimization for low-power usage were never of highest priority for FPGA vendors. In this paper, a selected number of existing multiplexers are analyzed and compared with a newly developed, ultra-low-power multiplexer architecture.
Motor Imagery Recognition Method based on Multi-Classifier Integrated Desision

Motor imagery brain-Computer Interface has a unique advantage in stroke rehabilitation. It encourages the patient to imagine the affected side of motor motion based on plasticity of brain nerve. Motor imagery plays an indispensable role on stroke rehabilitation for its benefit to the reconstruction of brain's damaged nerve. In order to further improve the classification accuracy and reliability, enhance patients' confidence in treatment, at the same time, it also provides more accurate feedback for the online training part of the follow-up rehabilitation system. SVM” KNN (k-nearest neighbor) and LDA (linear discrimination analysis) are used to classify EEG data. For each sampling data, three classification methods are used, each classification method produces a corresponding classification result, and the final classification result is determined by voting mechanism. The comprehensive classification effect of multi-classifier is 2 %~6% higher than that of SVM classifier only.
Practical IoT-Enabled Monitoring Platform for Solid Waste Collection: A case study for the municipality of Baba Hassen, Algiers, Algeria

This work presents a step forward solution for solid waste collection using an adequate IoT-based practical monitoring platform. The system is designed to assist Algerian municipalities in collecting solid waste by monitoring the fullness status of outdoor bins. The solution framework is based on (1) wireless sensor nodes connecting the (2) intelligent bins to the (3) monitoring station by sending the bins filling level status for analysis in order to control the waste in real-time and avoid bins over-filling. This process leads to an improvement in waste management via bins collection-based route optimization process. With the proposed solution, the benefit would be the timely-efficient waste collection, operating costs and gas emissions reduction, and preserve the environment from pollution with future prediction of waste generation.
Multiuser Data Dissemination in OFDMA System Based on Deep Q-Network

In this paper, a multiuser data dissemination problem is analyzed in an orthogonal frequency division multiple access(OFDMA) downlink system. By dynamically allocating subchannels and power to the mobile users, the system aims to minimize the time consumption in order to successfully deliver data to multiple mobile users under the restriction of total energy consumption. Both the objective and the constraint of the optimization are related to the real-time resource allocation strategies. Moreover, neither the statistics nor the full channel state information is known to the base station. In order to solve the global optimization problem with partial channel information, a Deep Q-Network algorithm is adopted. The numerical results show that compared with the other algorithms, Deep Q-Network can learn the optimal resource allocation strategies and achieve very good system performance.
The Dynamic Behavior of Large Scale Safawi PV plant integrated to the National Transmission Grid of Jordan, Particularly 132 Kv Busbar

The objective of the transient stability studies is to verify the compliance of the performance of the proposed (PV) Photo Voltaic plant power system design, without reactive power compensation, with the( NEPCO) National Electric Company (GCC) Grid Code Compliance requirements of Low Voltage Fault Ride Through (LVFRT), High Voltage Fault Ride Through (HVFRT), and dynamic voltage control abilities specified for the connection of the 50 MW projected Safawi PV plant to the NEPCO system. The present paper introduces a complete study of dynamic behavior of large scale PV plants integrated to the power grid. The DIgSILENT power factory software is used to model the national transmission grid of Jordan. Three-phase short circuit calculations were performed and analyzed for the grid with Safawi PV plants. It was found that the projected PV plants to be connected in the period of (2015-2020) are complied with the LVRT and HVRT characteristics of the GCC except 3 cases, when the installation of (SVC) Static Var Compensation is required. Voltage profiles of Safawi PV plants that will be connected to the grid during the period of (2015-2020) is complied with the LVRT and HVRT characteristics of the GCC except for cases 4, 6 and 7.For these cases, it is required to install SVC, in order to provide the required steady state and dynamic voltage support. Another option is to change the projected capacity of the PV plants.