Miracle-dz/UniFAR-FTUs huggingface dataset:
A Resonant Pressure Micro Sensor Based on Suspended Assembly

This study presents a resonant pressure micro sensor which is suspended by springs in assembly. Pressures under measurement lead to intrinsic frequency shifts of two resonators, which are, however, prone to environmental disturbances. The suspended assembly proposed in this study can minimize the side effects of environmental noises and stabilize intrinsic frequencies of resonators. More specifically, in response to temperature variations, the fitting errors of the resonant pressure micro sensors with and without suspended assembly were quantified as −13 Pa and 788 Pa, respectively. In response to environmental vibrations, the measurement errors of the resonant pressure micro sensors were decreased from 24 Pa to 6 Pa with the help of the suspended assembly.
5G-SSAAC: Slice-specific Authentication and Access Control in 5G

The fifth generation of mobile cellular networks (5G) is designed to support a set of new requirements and use cases, including connectivity for the IoT (Internet of Things). Due to the industry and the user's expectation of having connectivity embedded into IoT devices, the “wholesale wireless connectivity” concept is gaining more and more attention. According to this concept, connectivity providers sell connectivity to 3rd parties, which in turn provide it to their own devices. However, this concept brings also new architecture and security requirements that are not fully addressed by the state of the art. Taking advantage of the flexibility provided by virtualization technologies (including network slicing), we propose in this paper a new 5G-SSAAC (5G Slice Specific Authentication and Access Control) mechanism that delegates authentication and access control of the devices to the 3rd parties providing these devices, thereby decreasing the load of the connectivity provider's CN (core network), while increasing flexibility and modularity of the whole 5G network.
Topologies for Reduction of Second Harmonic Ripple in Battery Energy Storage Systems

Battery energy storage systems (BESS) are becoming common with the increasing penetration of renewable energy sources. Real-time simulations (RTS) can provide an effective approach in the studies of BESS by reducing initial experimental cost and increasing model and test flexibility. Therefore, the need for developing accurate models of BESS for RTS is rising. This paper develops three RTS models for BESS with detailed power electronics, including ripple-port inverter, boost inverter and H-bridge with series-connected flyback converter. Moreover, the second harmonic current (SHC) affects the battery lifetime. To prolong the lifetime of BESS, the topologies and corresponding control schemes are designed to mitigate the SHC in battery. The operation of the real-time models and the effectiveness of the three topologies is demonstrated through RTS results that also show how the topologies and implementated control schemes can reduce the SHC significantly.
Small Size And Highly Sensitive Differential MEMS Accelerometer Based On Double-Ended Tuning Fork Resonators

Here, a frequency modulated MEMS accelerometer encapsulated in vacuum is presented. The small size (0.25 mm2) and the capability of encapsulation at low pressures (≈ 150 Pa) are the main advantages reported. A high sensitivity (85 Hz/g) for a single double-ended tuning fork (DETF) resonator is achieved due to the addition of a force amplification mechanism, fully optimized through finite-element method analysis (FEM). Since a differential approach, composed by two DETF resonators, is implemented, the sensitivity is doubled to approximately 170 Hz/g. The structures have been fabricated using a commercial process (19 μm thickness active layer) and later characterized to validate the design. Device sensitivity, its temperature dependence (2 Hz/°C) and dynamic behavior have been experimentally measured.
Planning and Overall Economic Evaluation of Photovoltaic-Energy Storage Station Based on Game Theory and Analytic Hierarchy Process

With the application of energy storage systems in photovoltaic power generation, the selection and optimal capacity configuration of energy storage batteries at photovoltaic-energy storage stations (PESS) are becoming more and more important. Aiming at the overall economics of the PESS in the scenario of tracking the planning output, a capacity configuration and economic evaluation method of the PESS based on game theory and analytic hierarchy process (AHP) is proposed. Taking the minimum discarded photovoltaic power and the maximum net income of the PESS as objective functions, a comprehensive model of the PESS is built according to the historical data of the PESS. Combined with the characteristics of different electrochemical energy storage batteries, the relationships among the net income of the PESS, the depths of discharge (DODs) and the capacity of the energy storage batteries are analyzed. When the amount of the discarded photovoltaic power is zero, a selection and capacity configuration model of the energy storage batteries based on game theory is established, and the firefly algorithm (FA) is adopted to solve the model. Starting from the comprehensive evaluation indexes of the PESS, the hierarchical analysis structure under different energy storage schemes is discussed, and the AHP is employed to realize the overall economic evaluation of the PESS, in which the weight of the evaluation indexes in different energy storage schemes is calculated by the entropy weight method. The effectiveness and correctness of the proposed method are verified through the simulation analysis of the actual operation data of a certain PESS in China. The simulation results show that the selection and optimal capacity configuration of the energy storage batteries have an important impact on the overall economics of the PESS, and the overall economics of the PESS obtained by three types of electrochemical energy storage batteries is the highest income of 249.5 Million $, which can provide a theoretical basis for the planning of the PESS and has certain engineering application prospects.
Identification of Urine Odour Using CMOS-Based Metal Oxide Resistive Gas Sensors

In this work, three custom-made metal oxide (MOX) sensors were used to identify gaseous headspace of synthetic urine from acetone, ammonia and ethyl acetate. The sensors have chemical coatings of WO3, Pd/Pt doped SnO2 and CuO. Gases were trialled at 1 to 10 ppm for ammonia and ethyl acetate, 1 to 50 ppm for acetone and 5 to 100% for gaseous synthetic urine. A gas sensing unit was used for experiments and the collected data were processed in a two-layered pattern recognition model for gas classification. Five datasets were collected, four used for training and one for testing. The model has an overall accuracy of 91.1% and 100% for identifying synthetic urine.
Screen Printed Thermal Flow Velocity Sensor for Microfluidic Devices Featuring Intrinsic Compensation of Spurious Heat Transfe

We present a fully screen printed sensor for measuring the flow velocity inside microfluidic chips based on a modified anemometer principle. In contrast to previously reported sensor concepts, this device is designed to automatically compensate for the spurious heat propagation in the substrate and heat conduction in the liquid of interest. Using this approach it is possible to measure the flow velocity of any liquid in a channel directly by means of a single voltage without the need to correct for thermal side effects in a post-processing step. Additionally, fabrication by screen printing allows for integration of the sensor without disturbing the fluid flow.
A Joint Power Allocation and User Association Based on Non-Cooperative Game Theory in an Heterogeneous Ultra-Dense Network

Driven by the increase of data traffic, a heterogeneous ultra-dense network (H-UDN) constitute one of the most promising techniques to support the 5G mobile system. Ultra-dense network (UDN) refers to the idea of densifying the cellular networks to reduce the distance between the access nodes and the user equipment (UE) to achieve the highest possible transmission rates and to enhance the quality of service (QoS). Despite these advantages, (H-UDN) introduces numerous challenges in terms of resource allocation. In this paper, we develop a joint power allocation and user association strategy in H-UDN using non-cooperative game theory. The proposed game is divided into two sub-games, the Backhaul Game is implemented between BS and RNs in the backhaul links and the Access Game is implemented between the BS/RNs and UEs in the access links. The leaders estimate the strategies of their followers to decide on their strategies. Therefore, our solution starts first by solving the users association in the access links to derive the optimal power strategies of the followers and then choosing their optimal power allocation strategies. Subsequently, the followers do the best response to the leaders’ strategies. The simulation results show that our proposed algorithm can achieve the optimal power allocation and improve the performances of the system in term of throughput and UE rate compared to existing methods.
Chip Based Microelectrochemical Cell Array for Whole-Cell Patch-Clamp Recording

High electrostablity and a long life-time of the devices are crucial for ionic current recording through ion channel proteins embedded in biological cell membrane based on planar chip technology for electrophysiological measurements. In this paper, we propose a novel planar microchip integrated with microelectrochemical cell array to more toward a feasible solution for ion channel screening with high resolution and long life-time. Through this chip based method, stable current recording through biological cell membrane is achieved with good electrical sealing resistance. In addition, with no need for complex fluidic connections, this method allows for an easy operation and further miniaturization of the measuring system.
DDoS Attack Detection Using Greedy Algorithm and Frequency Modulation

Distributed Denial of Service (DDoS) attack is one of the major threats to the network services. In this paper, we propose a DDoS attack detection algorithm based on the probability distributions of source IP addresses and destination IP addresses. According to the behavior of source and destination IP addresses during DDoS attack, the distance between these features is calculated and used.It is calculated with using the Greedy algorithm which eliminates some requirements associated with Kullback-Leibler divergence such as having the same rank of the probability distributions. Then frequency modulation is proposed in the detection phase to reduce false alarm rates and to avoid using static threshold. This algorithm is tested on the real data collected from Boğaziçi University network.
Realizing Traffic Isolation and QoS Control in OpenStack Tap-as-a-Service

Capturing and analyzing the actual packets in networks is useful in order to understand the behaviors of a system. Tap-as-a-Service (TaaS), an OpenStack project, has been proposed to enable capturing packets in a virtual network. TaaS, by expanding OpenStack Neutron APIs, provides a functionality that allows users to set up mirroring of the production traffic between virtual machines (VMs) and to transfer the mirrored traffic to the monitoring VM. However, the production traffic and the mirrored traffic affect each other because they share the same physical underlay network in the reference TaaS implementation. This may result in interference of resources. Since capturing packets on the virtual network may affect the performance of business applications running on the VMs, it is impossible to analyze the normal condition. In this paper, we propose two methods to solve this issue: (1) isolation of the production traffic and the mirrored traffic at the underlay network and (2) a quality of service (QoS) control method of mirrored traffic. Using these methods, we can reduce the influence of the mirrored traffic on the performance of the production traffic. Moreover, we also discuss the experimental results. The results confirm the effectiveness of the proposed methods.
Handover Management for Delay-sensitive IoT Services on Wireless Software-defined Network Platforms

Software Defined Networks allow network operators to easily add new services to the network and quickly adapt the network to their own requirements. The main feature of this architecture is separation of control plane and data plane, and logically concentrating network strategic intelligence in a network location. Real-time traffics generated by mobile users still require small end-to-end delay and small handover latency during the user handover, to make smooth communication for users. Most IoT application traffic fall within this category. While the amount of transmitted data in IoT applications might be considerably smaller than that of real-time multimedia applications, many IoT applications have low tolerance to large delays. In this paper, we improved end-to-end delay and handover latency by proposing an SDN-based scheme with a cross-layer approach. We show that the mentioned delays are reduced by using our method. We evaluated our method by using Mininet-WiFi [13] Emulator. We have also reduced the number of handover that users encounter during their mobility.
Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Denoising Autoencoder and Deep Convolutional Neural Network

Radar signal intra-pulse modulation recognition is an important technology in electronic warfare. A radar signal intra-pulse modulation recognition method based on convolutional denoising autoencoder (CDAE) and deep convolutional neural network (DCNN) is proposed in this paper. First, we use Cohen’s time-frequency distribution to convert radar signals into time-frequency images (TFIs). Then image preprocessing is applied to TFIs, including bilinear interpolation and amplitude normalization. Next, we design a CDAE to denoise and repair TFIs. Finally, we design a deep convolutional neural network based on Inception architecture to identify the processed TFIs. Simulation results demonstrate that CDAE effectively reduces the interference of noise on TFIs classification, and improves the classification performance at a low signal-to-noise ratio (SNR). The DCNN architecture we designed makes good use of computing resources and has a good classification effect. The approach has good noise immunity and generalization. It can classify twelve kinds of modulation signals and an overall probability of successful recognition is more than 95% when the SNR is −9 dB.
Classification of Methamorphic Malware with Deep Learning(LSTM)

Nowadays, anti-virus applications using traditional signature-based detection methods fail to detect metamorphic malware. For this reason, recent studies on the detection and classification of malicious software address the behavior of malware. In this study, an LSTM based classification method was developed by using API calls of 8 different types of real malware. With this method, the behaviors of the malware types on the operating system are modeled.
Compressive System Identification for Multiple Line Outage Detection in Smart Grids

Real-time power line outage detection (POD) and localization is an important monitoring task for the modern smart grid. Reliable monitoring of power lines status plays a critical role in the system-wide blackout prevention. In this paper, the main aim is to address the multiple POD problems by exploiting the compressive system identification—a time-efficient approach in a complex network analysis. A typical power network is considered as a single graph, and the mathematical formulation of the POD problem is initialized using the dc power-flow model and graph theory concepts. Next, a sparse representation-based formulation for this problem (POD-SRP) is reported and further improved and generalized in case of multiple large-scale outages. Practical and technical challenges associated with this sparse recovery problem are partially addressed by developing new SRP solvers. Furthermore, a new sparse-based mathematical formulation for POD is introduced and termed as “Binary-POD-SRP,” which specifically deals with two particular issues, namely, the high coherence and the signal dynamic outrange. Finally, the identification performance of the proposed framework is evaluated by a variety of case studies, which are modeled using IEEE standard test-beds. We specifically discuss how the inherent challenges within large-scale multiple-outages can be solved by applying these new techniques and formulations.
Fiber to the Tap: Pushing Coaxial Cable Networks to Their Limits

We explore the potential of a future cable access technology that pushes the capacity of coaxial cable networks to its limit. We show that it is possible to achieve symmetric data rates of multiple tens of gigabit per second using the same coaxial drop cables in use today to connect households to the cable network, and discuss its feasibility and deployment characteristics.
Competitive Autoassociative Neural Networks for Electrical Appliance Identification for Non-Intrusive Load Monitoring

This paper presents a new approach to electrical appliance identification for non-intrusive load monitoring (NILM). In the proposed method a set of autoassociative neural networks is trained so that each one is tuned with the characteristics of a particular electrical appliance. Then, the autoassociative neural networks are set up in a competitive parallel arrangement in which they compete with one another when a new input vector is entered and the closest recognition is accepted to identify the given electrical appliance. The system is trained to recognize specific types of electrical appliances and use the transient power signal obtained from the on/off events for each electrical appliance. To test the proposed method, three public datasets were used, they are, the reference energy disaggregation dataset (REDD), the United Kingdom recording domestic appliance-level electricity (UK-DALE) and the Tracebase dataset containing real residential measurements are used. The accuracy and F-score obtained for the three datasets show the applicability of the proposed method for NILM systems.
A Current Residual-Based Open-Circuit Fault Diagnosis Method for Cascaded H-Bridge Multilevel Converters

The increasing importance of cascaded H-bridge multilevel converters (CHBMC) in supplying electrical energy to loads or grids places a higher priority to detect and protect against fault conditions. This paper proposes a simple open-circuit fault diagnosis method for CHBMC by analyzing the current residuals in the output side. Comparing the estimated output current with the calculated current, the change features of current residuals are extracted to detect and isolate single or multiple faulty switches. Compared to the existing approaches, the proposed method can detect and identify multiple faulty switches in the different faulty cells. The fault feature extraction of different cells is independent, which ensures that the fault diagnosis between cells is not affected each other. Furthermore, all of specified parameters can be obtained directly. And the diagnosis time is within three control periods under the appearance of fault features. Finally, simulation and experiment results have verified the applicability and effectiveness of the proposed method.
Two-Way Code Index Modulation

In this study, a new code index modulation (CIM) aided two-way network coded (TW-CIM) scheme with high energy-efficiency, high data speed and good error performance is proposed. In the proposed system model, unlike conventional decode-and-forward (DF) cooperative network coded communication techniques that perform three time-interval communications, only two time-intervals are used for communication since the spreading codes used are orthogonal to each other. In the proposed system structure, as well as the transmitted modulated symbol data, the spreading code indices carry data. In the TWCIM system, two different users communicate via a half-duplex relay based on the CIM technique. The considered TW-CIM system has been compared with the cooperative TW-DF system at different data rates. The theoretical error analysis of the proposed system is performed and the average bit error probability is achieved. Performance analyzes have been performed on Rayleigh fading channels for BPSK modulation.
Au nano-spiral structure for circular dichroic filter in visible region

In this paper, we report a film with Au nano-spiral structures to behave a circular dichroic filter in visible light region. Rotating Si while cooling to -100K and irradiation of the thermal vapor of Au with 85° angle of incidence created nano-spiral structures with a diameter of about 100 nm on the Si. The fabricated spiral structure was transferred on a Scotch tape to form a film. The transmitted light was measured and it was found that it had strong circular dichroism in the visible light region. This characteristic can be applied as a circular polarizing filter or an anti-reflection filter.
Network Fault Prediction Based on CNN-LSTM Hybrid Neural Network

The network brings convenience and efficiency to people's life and work, and at the same time, the network will also cause loss to human beings because of failures, so it is particularly important to predict faults before the faults occur. The fault prediction technology can prepare the staff to repair the faults in advance, reduce the repair time of the fault, and thus reduce the loss caused by the faults. Therefore, this paper proposes a network log-based CNN-LSTM hybrid prediction model for wireless network faults: firstly, the network log is preprocessed, the sample is extracted by two-level time window, then the sample features are extracted by CNN, and finally, the extracted features are input into LSTM for prediction. To demonstrate the superiority of the CNN-LSTM hybrid neural network prediction model, in this experiment, it is compared with CNN and Random Forest. The result shows that the prediction performance of CNN-LSTM is better than the other two models.
Electromagnetic Bandgap Backed Millimeter-Wave MIMO Antenna for Wearable Applications

A millimeter-wave (mm-Wave) multiple input multiple output (MIMO) antenna operating at 24 GHz (ISM band), suitable for wearable applications, is proposed in this paper. The proposed MIMO antenna consists of two elements, designed with an edge-to-edge distance of 5.14 mm, backed by a <inline-formula> <tex-math notation="LaTeX">$5\times 5$ </tex-math></inline-formula> cell electromagnetic bandgap (EBG) structure. The antenna is fabricated on a flexible Rogers 6002 material (<inline-formula> <tex-math notation="LaTeX">$\epsilon _{r}=$ </tex-math></inline-formula> <italic>2.94</italic>, <italic>tan<inline-formula> <tex-math notation="LaTeX">$\delta =$ </tex-math></inline-formula> 0.0012</italic>, <italic>thickness = 0.254 mm</italic>). The proposed antenna retains its performance when bent along the x-axis and y-axis. The performance of the antenna in term of s-parameters and radiation properties is studied in free space as well as on a human phantom. Good impedance matching of the antenna at the resonating frequency (24 GHz) is observed when it is bent and when worn on the body. The introduction of the EBG improves the gain by 1.9 dBi, reduces the backward radiation by 8 dB, reduces the power density on the back towards the body from > 200 W/m<sup>2</sup> to < 10 W/m<sup>2</sup>, and also enhances the 10 dB bandwidth by 100 MHz. The antenna possesses a low envelope correlation coefficient (ECC) of 0.24, high diversity gain (DG) of 9.7 dB, reasonable multiplexing efficiency of −0.684 dB and a good peak gain of 6 dBi at 24 GHz. The proposed antenna is suitable for wearable applications at mm-Wave range due to its simple geometry and good performance in bending and on-body worn scenarios.