Miracle-dz/UniFAR-FTUs huggingface dataset:
A Linear Array of Christmas-tree Shaped Antennas for DBS Applications

A two-element linear array with Christmas-tree shaped fractal antennas has been presented in this paper. The proposed single element is designed to operate in Ku-band with the center frequency $(f_{r})$ of 12.50 GHz for the application of Direct Broadcast Satellite (DBS) (12.2-12.7GHz) systems like Direct-2-Home (DTH). The motif has been scaled by 50%, pasted in the optimum place inside the structure and etched it to make the tree-shaped fractal antenna. Wilkinson power divider with equal magnitude is imposed to design the feed network of the two-element linear array. The single element and the array maintains a gain of 7.64 and 9.76 dB respectively. The obtained absolute bandwidth of the proposed antenna and the array are capable to cover more than 40% of the entire bandwidth of DBS service band.
A Novel Experimental Study to Enhance the Attentional State using EEG Signals

In this paper, we propose a simple low-complex classification framework for the cognitive enhancement with the sustained attention stimuli using Electroencephalography (EEG) signals. The visual stimuli comprise of four face images: two happy (one male and one female) and two unhappy (one male and one female). The neuronal response is decoded using a combination of discrete wavelet transform (DWT) and ensemble classifier. The features are extracted by decomposition of recorded EEG signals using Daubechies wavelet filter (db4) and used the statistical methods such as the absolute mean value, power, and standard deviation for classification. The proposed methodology is validated on in-house recorded visual attention EEG (VA-EEG) dataset using six subjects (three males, three females) and evaluated the performance on six binary combinations of facial stimuli. The performance results show that the binary combination of male happy (MH) and female happy (FH) facial stimuli aids in cognitive enhancement for the people suffering from cognitive symptoms. The proposed low-complex feature extraction classification framework obtained a mean classification accuracy (CA) and a mean kappa value of 86.58% and 0.72, respectively.
IOTApass: Enabling Public Transport Payments with IOTA

The extensive use of smartphones combined with the rise in the Internet of Things adoption has fostered the emergence of several use-cases to provide added comfort and peace of mind in our everyday life. Public transportation payment in large cities, where frequent commuters and generic users often spend a significant amount of time buying and validating tickets, can become a cumbersome process. This paper presents the design and implementation of a seamless payment system named IOTApass, which handles payments through the usage of a smartphone and a distributed ledger technology to avoid the limitations of centralized payment solutions. The IOTApass enables a user to seamlessly pay for public transports using a smartphone App without explicit (user) interface interactions, thus reducing payment complexity. Besides describing the architecture, operation, and implementation of IOTApass, the paper documents its experimental validation and confirms its feasibility.
Emerging High Power mm-Wave RF transistors

The growing number of commercial and military wireless applications are generating a vast amount of RF signals and increasingly crowding the electromagnetic environment. Operating in a crowded and contested spectral environment, high dynamic range transistors are required in transmitting and receiving circuits. Over the past decades, RF power transistors have been implemented on the well-established planar device topology, in which it is difficult to attain high power with high efficiency at mm-wave frequency. The DARPA Dynamic Range-enhanced Electronics and Materials (DREaM) program is developing advanced high power and high dynamic range transistor technology with 4X higher output power density and 100X better linearity compared to state-of-the-art transistors today. The program explores novel wide bandgap materials. multi-channel epitaxy, and new device innovations such as FinFET-like compound semiconductor device structures. This paper will provide an overview of recent device demonstrations with significantly higher linearity and power density at 30 GHz.
LabChain: an Interactive Prototype for Synthetic Peer-to-Peer Trade Research in Experimental Energy Economics

Blockchain-based peer-to-peer (P2P) electricity markets received considerable attention in the past years, leading to a rich variety of proposed market designs. Yet, little comparability and consensus exists on optimal market design, also due to a lack of common evaluation and benchmarking infrastructure.This article describes LabChain, an interactive prototype as research infrastructure for conducting experiments in (simulated) P2P electricity markets involving real human actors. The software stack comprises: (i) an (open) data layer for experiment configuration, (ii) a blockchain layer to reliably document bids and transactions, (iii) an experiment coordination layer and (iv) a user interface layer for participant interactions.As evaluation environment for human interactions within a laboratory setting, researchers can investigate patterns based on energy system and market setup and can compare and evaluate designs under real human behavior allowing alignment of intentions and outcomes. This contributes to the evaluation and benchmarking infrastructure discourse.
A Hierarchical Architecture Using Biased Min-Consensus for USV Path Planning

This paper proposes a hierarchical architecture using the biased min-consensus (BMC) method, to solve the path planning problem of unmanned surface vessel (USV). We take the fixed-point monitoring mission as an example, where a series of intermediate monitoring points should be visited once by USV. The whole framework incorporates the low-level layer planning the standard path between any two intermediate points, and the high-level fashion determining their visiting sequence. First, the optimal standard path in terms of voyage time and risk measure is planned by the BMC protocol, given that the corresponding graph is constructed with node state and edge weight. The USV will avoid obstacles or keep a certain distance safely, and arrive at the target point quickly. It is proven theoretically that the state of the graph will converge to be stable after finite iterations, i.e., the optimal solution can be found by BMC with low calculation complexity. Second, by incorporating the constraint of intermediate points, their visiting sequence is optimized by BMC again with the reconstruction of a new virtual graph based on the former planned results. The extensive simulation results in various scenarios also validate the feasibility and effectiveness of our method for autonomous navigation.
Intelligent Classification of Multi-Gesture EMG Signals Based on LSTM

In the study of prosthetic control technology, researchers usually use decoded electromyographic signals (EMG) to obtain amputees' motion intentions. Predicting and accurately classifying the intent of human gestures can be used not only for rehabilitation and entertainment robots, but also for artificial intelligence robots. It enables us to develop new, more natural methods of human-computer interaction. In this paper, we propose an advanced gesture recognition model. In this model, myoelectric signal of forearm measured by commercial sensor myo is used as input. To extract and classify these gestures, a long-term and short-term memory neural network is applied and compared with other deep learning algorithms. The experimental results show that the model proposed in this paper can recognize the same four gestures as Myo Armband's proprietary recognition system, with an average recognition accuracy of 97.75%, which has higher recognition accuracy than the comparison algorithm. The proposed deep learning model is robust, which means that it can recognize a user's gestures even if there is no user's data in the training data set.
DMRS-Applied Repetition Transmission (DART): Grant-Free Scheme for mMTC

In this paper, we develop a repetition-based grant-free scheme, called DMRS-Applied Repetition Transmission (DART), to support mMTC user equipments (UEs). In DART, the demodulation reference signals (DMRSs) are exploited to estimate the channels as well as to implicitly inform the repetition patterns to the gNB. Based on the detected DMRSs, the gNB performs successive interference cancellation (SIC) to further decode the data of mMTC UEs that transmit their data on the same resource block. DART achieves high success rate, low latency, and low SIC overhead by adopting DMRS-based SIC ordering. Simulation results show that DART is able to support mMTC UEs effectively.
Analysis of E3 effect in two dimensional ground ionospheric waveguide

It cannot be neglected that the coupling effect and induced current of large-size victim system from the high-altitude nuclear explosion electromagnetic pulse E3 wave. The Newmark method is used to discretize the time axis in finite element time domain method. In this paper, the time step is chose in the order of seconds. This method is used to calculate and analyze the electromagnetic characteristic of E3. Numerical examples show that the applicability and correctness of the algorithm for the calculation of long pulses.
Impact of Prediction Errors on High Throughput Predictive Resource Allocation

Predictive resource allocation (PRA) can leverage machine learning and historical data to boost the performance of mobile networks, say to support high throughput or improve the quality of service. While the large gain of PRA has been demonstrated by assuming perfect prediction or by optimizing with modeled prediction errors, how the prediction errors translate to the performance loss of PRA and how the future information should be predicted are not well understood. In this paper, we consider high throughput PRA policy for non-real-time (NRT) mobile users when the base stations also serve real-time (RT) users, where the future average rates of NRT users in a prediction window need to be predicted. The average rate can be predicted in various ways, where one typical way is to predict the rate directly from the historical average rates and another typical way is to predict the rate indirectly by first predicting traffic load and user trajectory. Given that the traffic load and user location can be gathered easily in cellular networks, we consider such an indirect prediction and analyze the impact of prediction errors of traffic load and trajectory on the prediction errors of average data rate. To this end, we first find the relation between the RT traffic load and the residual bandwidth after serving the RT users by resorting to effective capacity and effective bandwidth theory. To further justify why we analyze the indirect prediction, we compare the predictability of the time series used for the typical direct prediction and indirect prediction with entropy theory. Since different machine learning techniques lead to different prediction bias and prediction error variance, we then derive the relation between the prediction error statistics of the average data rate and those of traffic load and trajectory. We evaluate the performance loss of PRA using both modeled prediction errors and real predictions with deep learning. Our analyses show that for a network busy with RT service, the prediction errors of RT traffic load have larger impact on those of average data rate than the prediction errors of trajectory. The prediction bias has a larger impact on the throughput loss of PRA than the prediction error variance. This provides a guidance of designing learning techniques for predicting the required information.
Sensing Teamwork During Multi-objective Optimization

We describe, build, and test a collaborative design environment with IoT sensors to help researchers study engineering teamwork behavior as it relates to design performance during multi-objective optimization. As a case study, we use an instrumented environment to observe engineers designing a fleet of crude oil tankers with the assistance of an interactive computer simulation. Recorded observations include user-triggered software events, simulation inputs and results, conversation activity, and video data. We describe the results of a trial run that demonstrates successful logging and cross-referencing of data from multiple sensor feeds. This work lays the foundation for continued experimentation with larger sample sizes, so that we may answer research questions about the nature of teamwork behavior and design performance.
Hyperband Tuned Deep Neural Network With Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud

Cloud computing has very attractive features like elastic, on demand and fully managed computer system resources and services. However, due to its distributed and dynamic nature as well as vulnerabilities in virtualization implementation, the cloud environment is prone to various cyber-attacks and security issues related to cloud model. Some of them are inability to access data coming to and from cloud service, theft and misuse of data hosted, no control over sensitive data access, advance threats like malware injection attack, wrapping attacks, virtual machine escape, distributed denial of service attack (DDoS) etc. DDoS is one of the notorious attack. Despite a number of available potential solutions for the detection of DDoS attacks, the increasing frequency and potency of recent attacks and the constantly evolving attack vectors, necessitate the development of improved detection approaches. This article proposes a novel architecture that combines a well posed stacked sparse AutoEncoder (AE) for feature learning with a Deep Neural Network (DNN) for classification of network traffic into benign traffic and DDoS attack traffic. AE and DNN are optimized for detection of DDoS attacks by tuning the parameters using appropriately designed techniques. The improvements suggested in this article lead to low reconstruction error, prevent exploding and vanishing gradients, and lead to smaller network which avoids overfitting. A comparative analysis of the proposed approach with ten state-of-the-art approaches using performance metrics-detection accuracy, precision, recall and F1-Score, has been conducted. Experiments have been performed on CICIDS2017 and NSL-KDD standard datasets for validation. Proposed approach outperforms existing approaches over the NSL-KDD dataset and yields competitive results over the CICIDS2017 dataset.
Investigating Reward/Punishment Strategies in the Persuasiveness of Social Robots*

This paper presents the results of a user study designed to investigate social robots’ persuasiveness. In the design, the robot attempts to persuade users in two different conditions comparing to a control condition. In one condition, the robot aims at persuading users by giving them a reward. In the second condition, the robot tries to persuade by punishing users. The results indicated that the robot succeeded to persuade the users to select a less-desirable choice comparing to a better one. However, no difference was found in the perception of the robot’s warmth nor discomfort, comparing the two strategies. The results suggest that social robots are capable of persuading users objectively, but further investigation is required to investigate persuasion subjectively.
Practical Machine Learning-Based Rate Adaptation Solution for Wi-Fi NICs: IEEE 802.11ac as a Case Study

Many rate adaptation (RA) solutions have been proposed for legacy Wi-Fi standards. However, these solutions lack extensibility, and cannot therefore be easily applied to new Wi-Fi standards such as IEEE 802.11ac. Furthermore, while several practical RA solutions have been proposed for IEEE 802.11ac network interface cards (NICs), e.g., Minstrel and Iwlwifi, they achieve poor goodput performance in some cases since they lack scalability or thoroughness. Given a large number of rates in IEEE 802.11ac, the lack of scalability results in a slow convergence on the search of the best rate over time. To accelerate the rate search, several RA solutions skip some rates to narrow down the scope of candidate rates, but lack thoroughness and may miss the best rate. Accordingly, the present study proposes a holistic RA solution, designated as Machine Learning-based RA (MLRA), which is not only practical, but also extensible, scalable and thorough. MLRA achieves the extensible property by leveraging machine learning to automatically identify the correlations among the rate, goodput performance and link quality. Moreover, it achieves the scalable and thorough properties using a two-level rate search procedure and a congestion detector. The performance of MLRA is evaluated on commodity IEEE 802.11ac NICs using TensorFlow with an asynchronous framework implemented across the kernel and user spaces of the operating system. The results show that MLRA outperforms other practical RAs by up to around 133.0%-658.0% with a negligible overhead.
Defense Against Advanced Persistent Threats: Optimal Network Security Hardening Using Multi-stage Maze Network Game

Advanced Persistent Threat (APT) is a stealthy, continuous and sophisticated method of network attacks, which can cause serious privacy leakage and millions of dollars losses. In this paper, we introduce a new game-theoretic framework of the interaction between a defender who uses limited Security Resources(SRs) to harden network and an attacker who adopts a multi-stage plan to attack the network. The game model is derived from Stackelberg games called a Multi-stage Maze Network Game (M2NG) in which the characteristics of APT are fully considered. The possible plans of the attacker are compactly represented using attack graphs(AGs), but the compact representation of the attacker’s strategies presents a computational challenge and reaching the Nash Equilibrium(NE) is NP-hard. We present a method that first translates AGs into Markov Decision Process(MDP) and then achieves the optimal SRs allocation using the policy hill-climbing(PHC) algorithm. Finally, we present an empirical evaluation of the model and analyze the scalability and sensitivity of the algorithm. Simulation results exhibit that our proposed reinforcement learning-based SRs allocation is feasible and efficient.
S11 Calibration Method for a Coaxial-loaded Cut-off Circular Waveguide using SOM Termination

In this study, termination with conditions referred to as short, open and reference material (SOM) was used for S11 calibration at the front of samples with a coaxial-feed-type cut-off circular waveguide as a preliminary step for dielectric measurement in liquids. With pure water as the reference material, the S11 value for the jig at the front of the sample was then calibrated in the frequency band of 0.50 to 3.0 GHz using a VNA (vector network analyzer) via the proposed method. Next, S11 at the front surface of the sample was measured with various liquids in the jig after calibration. The results showed close correspondence of measured values for each method, thereby indicating the validity of the proposed S11 calibration approach.
Impact of Communication Reliability on Achieving Energy Sharing in Smart Cities

With the development of internet of things (IoT) technology, massive distributed energy resources (DERs) can be aggregated to improve the economy and reliability of power grids by sharing the surplus energy among a community. Energy sharing in smart cities requires the support of advanced information and communication technology (ICT) to ensure communication reliability. However, existing literature has ignored the impact of communication reliability (CR) on energy sharing. Therefore, an energy sharing model is proposed considering CR in smart cities, which aims to investigate the impacts of CR on achieving energy sharing benefits. A multi-microgrid connected by a distribution network is conceived in this paper. Each microgrid (MG) includes photovoltaic, energy storage, controllable load, and a communication base station (BS). The BS is responsible for the communication interaction between the local MG and the distribution system operator (DSO). Due to the interference and noise among the MGs, CR is modeled as a function of transmit power of different BSs. The proposed energy sharing scheme is to achieve DER sharing under the premise that each MG meets its physical and CR constraints. Case studies based on the IEEE 33-bus feeder system validate the effectiveness of the proposed framework and method. Traditional energy sharing without considering CR may lead to the violation of CR requirements. Additionally, BSs can be used as a kind of controllable load in the distribution network to reduce operation costs.
A SAR Target Recognition Based on Guided Reconstruction and Weighted Norm-Constrained Deep Belief Network

The deep learning has some new vitality to the synthetic aperture radar (SAR) automatic target recognition (ATR). By introducing different constraints, deep belief network (DBN) has become apply to SAR target recognition recent years, but the existing DBN algorithms have some questions including the high training epochs, low recognition rate and complex structure. Therefore, an algorithm based on guided reconstruction and weighted norm-constrained DBN is proposed. Firstly, in order to reduce the dimension of the image output feature, increase the speed of preprocessing, generate a one-dimensional image vector and normalized, a SAR target classification algorithm with two-scale fusion character based on guided filter reconstruction algorithm is introduced. Then, the sparse feature extraction of SAR image is carried out by weighted norm-constrained DBN. By the regularization constraint of the probability distribution, the algorithm can minimize the joint probability distribution of visual layer and hidden layer by samples. The low-dimensional feature is further improved based on the generalized optimization of norm-constrained RBM. Finally, by a regularized Softmax which can classify the targets and obtain output results. The experimental results show that the SAR target recognition algorithm based on the guided reconstruction and weighted constrained deep confidence network not only improves the target recognition performance and generalization ability, but also reduces the output feature dimension and network training times, and the recognition performance of the algorithm is further improved.
Timing-Driven Placement for FPGA Architectures with Dedicated Routing Paths

The idea of introducing dedicated, fast paths between certain FPGA elements in order to reduce delay is neither new nor particularly hard to come up with. What is less obvious, however, is how to put such paths to actual use. In this work, we propose an effective ILP-based detailed placer for FPGA architectures with direct connections between LUTs. We discuss various aspects of making such an approach practicable, from efficient formulation of the integer programs themselves, to focused application of the placer on specific portions of the circuit where it could have the greatest impact. These careful considerations allow us to simultaneously move tens of LUTs with tens of candidate positions each, in a matter of minutes. This more than doubles the advantage of additional connections on the critical path delay compared to the previously reported results that relied on architecture-oblivious placement algorithms.