Miracle-dz/UniFAR-FTUs huggingface dataset:
Emerging Trends in Pain Modulation by Metabotropic Glutamate Receptors

Pain is an essential protective mechanism meant to prevent tissue damages in organisms. On the other hand, chronic or persistent pain caused, for example, by inflammation or nerve injury is long lasting and responsible for long-term disability in patients. Therefore, chronic pain and its management represents a major public health problem. Hence, it is critical to better understand chronic pain molecular mechanisms to develop innovative and efficient drugs. Over the past decades, accumulating evidence has demonstrated a pivotal role of glutamate in pain sensation and transmission, supporting glutamate receptors as promising potential targets for pain relieving drug development. Glutamate is the most abundant excitatory neurotransmitter in the brain. Once released into the synapse, glutamate acts through ionotropic glutamate receptors (iGluRs), which are ligand-gated ion channels triggering fast excitatory neurotransmission, and metabotropic glutamate receptors (mGluRs), which are G protein-coupled receptors modulating synaptic transmission. Eight mGluRs subtypes have been identified and are divided into three classes based on their sequence similarities and their pharmacological and biochemical properties. Of note, all mGluR subtypes (except mGlu6 receptor) are expressed within the nociceptive pathways where they modulate pain transmission. This review will address the role of mGluRs in acute and persistent pain processing and emerging pharmacotherapies for pain management.
Strain Transfer and Creep in All-Quartz Packaged SAW Strain Sensors

Design of the all-quartz package (AQP) for a resonant SAW strain sensing element is discussed and strain transfer to the surface of the SAW substrate is studied. Analytical results are compared with the FEA results. Based on the analytical model, creep and relaxation of strain on top of the SAW substrate is simulated using a viscoelastic model for the bond and the AQP frame adhesives. An experimentally observed anomalous creep of the resonant frequencies under a constant load is explained theoretically.
Design of A CML Driver Circuit in 28 nm CMOS Process

CML driver circuit is an essential part of high-speed serial interfaces. This paper presents a low-voltage high-speed CML driver circuit at 1.05V supply voltage in UMC 28nm CMOS process. The design adopts four levels cascade main driver circuit and pre-emphasis circuit. The pre-emphasis circuit include emphasis circuit and adjust the emphasis range circuit. Designed and verified in Cadence IC615, the proposed circuit’s output swing is 500mV, the bit rate can reach 12.5Gbps, the power consumption is 28.9mW, emphasis can achieve 11.48 dB.
Design and Analysis of Wireless Ballastless Fluorescent Lighting

In this paper, a wireless ballastless lighting system for fluorescent lamps is proposed and implement-ted. The key is to utilize magnetic resonant coupling to achieve wireless power transfer (WPT) while igniting the fluorescent lamp without using any magnetic or electronic ballast. Meanwhile, series-parallel compensation is identified to be more suitable for this WPT system. Also, burst firing control is utilized to achieve efficient light dimming. In order to achieve the fully wireless feature, remote lighting control via Bluetooth is realized by a smart phone. As an example, an experimental setup, which can light up an 8 W fluorescent lamp with illuminance of 2330 lx, has been designed and developed. In particular, it can achieve the transmission efficiency of 57% and the operating frequency of 51 kHz, with distance up to 56 cm. Both theoretical analyses and experimental results are given to validate the feasibility and flexibility of the proposed system.
Harvesting Sunlight with Titanium Nitride Nanostructures

Titanium nitride (TiN) nanostructure shows broad optical resonance that covers the majority of solar spectrum. By taking advantage of this resonance, i.e., surface plasmon resonance, we demonstrate that TiN nanostructures can efficiently harvest sunlight for internal photoemission and photothermal conversion. Some of the applications of TiN nanostructures include visible active photocatalysis and solar water evaporation. Since TiN has been used in industries, the use of TiN is practical, motiving the application oriented research for solar energy harvesting.
Decentralized Public Key Infrastructure for Internet-of-Things

In many envisioned IoT applications, security is crucial. However, designing and/or deploying existing security techniques in IoT systems is not straightforward due to the inherent heterogeneity of IoT devices as well as their huge number. A critical security building block is represented by the Public Key Infrastructure (PKI) relying on Certificate Authorities (CAs). However, even a single-point-of-failure in a PKI may affect entire IoT systems due to its centralized nature. Failures have far reaching effects as the number of IoT devices increases. Furthermore, it is difficult for the owners of IoT devices to manage the certificates for their IoT devices since there are no standard protocols for retrieving, installing and updating the certificates. As a result, IoT device manufacturers often install certificates on the devices on behalf of the owners of the devices, which introduces the risk that the private keys of the devices are leaked by the manufacturers. In this paper, we propose a decentralized PKI for IoT, called IoT-PKI, which utilizes distributed nodes in a blockchain network instead of CAs, and thus addresses scalability. IoT-PKI protects against key leakages at device manufacturers since it allows the owners of IoT devices to manage the certificates of their IoT devices. Finally, we show the feasibility and efficiency of IoT-PKI through our prototype implementation and experiments.
Surface Plasmon Amplification in Transition Metal Nitrides based Gain Assisted Core-Shell Nanoparticles

Transition Metal Nitrides (TMNs) such as ZrN, and TiN has emerged as alternative plasmonic materials to the conventional coinage materials like Au, Ag, Al and Cu etc. The present work assesses the suitability of conventional core-shell nanoparticle for lasing action in SPASER (Surface Plasmon Amplification by Stimulated Emission of Radiation) application. Surface plasmon (SP) amplification is caused by incorporating critical optical gain (g) in the nanoshell core, which leads to the amplification characteristics with lower threshold value for lasing action/process. We report that, the critical optical gain requirement in ZrN is comparable to Au based core-shell nanopartcle. The properties of SPASER and switch able nano scatterer are analyzed using quasistatic approximation (valid for small size particles). By introduction of specific gain in the core medium, the drastic change of scattering efficiency (Qsca) from ultra high to ultra low value and negative absorption cross-section is reported. The feasibility of TMNs as gain assisted surface plasmon amplification properties for various applications like plasmonic laser and nano scatterer is assessed and comparison is made with the most popular plasmonic material, namely Au. Present study can led to the realization of efficient SPASER structures/devices based on TMNs.
3D-Gabor-Based Feature Selection Via Enhanced Fast Density-Peak-Based Clustering

Hyperspectral image(HSI) has hundreds of bands witch provide hyperspectral data to distinguish different ground objects. However, owing to the low number of samples and strong relativity between different bands, its always tough to get a precise classification result. In this paper, 3D-Gabor-based feature selection algorithm name 3DG-EFDPC is proposed to address this problem. 3D Gabor filter can preserve intrinsic information of hyperspectral image and extract distinguished features with joint spatial/spectral domains for increasing accuracy of classification, via capturing different orientation, scale, and wavelength properties of hyperspectral image data. However, a feature selection process must be taken due to redundance might exist in 3D Gabor feature. For this purpose, we introduce enhanced fast density-peak-based clustering(EFDPC) algorithm. Through calculating the normalized local density and the intracluster distance of each feature, EFDPC regard features which both values are anomalously large as cluster center which contain discriminative and valuable information. Furthermore, in order to confirm the number of chosen features decided by the cutoff threshold, the EFDPC algorithm propose an exponential-based learning rule. We demonstrate the performance of the 3DG-EFDPC features for classification on real hyperspectral remote sensing data sets.
Electron Injection Model for Linear and Parabolic 2D Materials: Noise as a Parabolic or Linear Band Detector

The proper modeling of the 2D materials requires a discussion on the boundary conditions between the device active region (open system) and the environment, to determine how and when electrons are injected. In devices with gapless materials, like graphene, the injection of electrons with positive and negative kinetic energies are needed for an accurate description of the band-to-band Klein tunneling. We develop a full quantum injection model for 2D materials based on quantum trajectories. Numerical results with the BITLLES simulator will be presented showing the successful application of the electron injection model for a full quantum treatment of DC, AC, transient and noise behaviors of 2D devices. We will also discuss how the noise (fluctuations) of the electrical current in ballistic devices, with parabolic or linear band dispersion have some peculiarities which can be used to discern between linear and parabolic band materials.
Real Time Object Detection and Tracking Using Deep Learning and OpenCV

Deep learning has gained a tremendous influence on how the world is adapting to Artificial Intelligence since past few years. Some of the popular object detection algorithms are Region-based Convolutional Neural Networks (RCNN), Faster-RCNN, Single Shot Detector (SSD) and You Only Look Once (YOLO). Amongst these, Faster-RCNN and SSD have better accuracy, while YOLO performs better when speed is given preference over accuracy. Deep learning combines SSD and Mobile Nets to perform efficient implementation of detection and tracking. This algorithm performs efficient object detection while not compromising on the performance.
Highly Tunable Band-Stop Filter Based on Silicon on Insulator Split Micro-Ring Resonators

In this paper, we proposed a band-stop silicon photonic filter based on the highly tunable silicon on insulator (SOI) split micro-ring resonators. The band-stop filter is made up of a silicon bus waveguide coupled to SOI split micro-ring resonators. The band-stop of the proposed silicon photonic waveguide structures can be easily manipulated by adjusting the geometrical parameters of the split micro-ring resonators. The numerical results are obtained by a finite-difference time-domain (FDTD) method. The proposed resonator would be a potential key component in the application of integrated optic circuits.
Semi-Automated System Based Defect Detection in Software Requirements Specification document

Software Requirement Specification is a document that defines capabilities of any software. Requirement phase plays an important role and it is the most critical phase among all SDLC phases as requirements about any software should be mentioned properly otherwise it will lead to incorrect product. Software requirement specification document consists of defects due to this and defects are need to be detected to save time and money. There are some reading techniques with the help of which defects can be identified. There are certain types of defects definitions on which basis defects are categorized accordingly. Previously defects are identified manually by reading document but this work is based on detecting defects semi-automatically which help in improvement of quality of document as well as developed software.
Development of Synthetic Barrier Dental Membrane for Guided Bone Regeneration

The barrier membrane three-layer construction is designed to attract, trap, and retain fibroblasts and epithelial cells while maintaining space around teeth for development of bone and periodontal support tissues. The scaffold is characterized by a highly interconnected macroporosity, with macropores of 0.3 −5 mm and improved mechanical properties with respect to the polymer alone, producing excellent dimensional stability.
A SDN-Based Hybrid Electrical Optical Architecture

As the high-bandwidth and data-intensive applications evolving rapidly, traditional electrical networks are no longer able to meet the increasing traffic requirements. In order to improve the communication performance of data center network(DCN), the hybrid electrical/optical architecture has become a new research topic. Moreover, traditional architectures are often too complicate to manage. As a new technology, Software Defined network (SDN) addresses this issue effectively. In this paper, we propose a hybrid architecture based on SDN. The control manager is used to monitor and allocate traffic, then configure the network by SDN. There is a hybrid network platform under the implementation of virtual machine migration. The experiment shows that the proposed scheme reduces the total time of the virtual machine migration effectively compared to that running on the electrical architecture. In addition, our scheme can configure the network topology flexibly and achieve load balancing.
Adversarial Examples Against the Deep Learning Based Network Intrusion Detection Systems

Deep learning begins to be widely applied in security applications, but the vulnerability of deep learning in front of adversarial examples raises people's concern. In this paper, we study the practicality of adversarial example in the domain of network intrusion detection systems (NIDS). Specifically, we investigate how adversarial examples affect the performance of deep neural network (DNN) trained to detect abnormal behaviors in the black-box model. We demonstrate that adversary can generate effective adversarial examples against DNN classifier trained for NIDS even when the internal information of the target model is isolated from the adversary. In our experiment we first train a DNN model for NIDS system using NSL-KDD database and achieve a performance matching the state-of-art literature, then we show how can an adversary generate adversary examples to mislead the model without knowing the internal information.
Scattering Characteristics of Vortex Electromagnetic Waves for a Wedge

Vortex electromagnetic (EM) waves with orbital angular momentum (OAM) offer a new degree of freedom for radar detection. Although to date few researches on target scattering characteristics of vortex EM waves have been reported. In this paper, the backscattering characteristics of vortex EM waves for a perfectly conducting wedge is investigated experimentally using the radar cross section (RCS) calibration measurement method. The experimental results show that for vortex EM waves, due to the specific spatial distribution, the RCS fluctuation of vortex EM waves is different from that of plane EM waves, and it varies with the OAM mode of the incident wave. The RCS of OAM beams is higher than that of the plane wave in some incident direction. This phenomenon indicates that vortex EM waves are more effective than plane EM waves to detect the low RCS target in some direction. And vortex EM waves of different OAM modes may be helpful to detect the low RCS target by increasing the RCS of the target.
A Privacy Enhanced Crowdsourcing Architecture for Road Information Mining Using Smartphones

The digitization of our road and traffic systems enables evermore advanced location based services to support us in our everyday tasks with prominent examples being navigation applications like Google Maps or speed camera directories like TomTom Speed Cameras. The information collection that lies at the base of these applications however is often either done behind closed doors, or relies on the goodwill and time investment of voluntary community members providing such information as best they can. In this paper we present a new crowdsourcing architecture for voluntary road and traffic system data collection, that on the one hand values and protects the privacy of the participating community members and on the other hand significantly eases their manual workload by detecting and inferring applicable information through the sensors of their mobile phones using a self-created Android application. Our approach shows reliable results for the road system properties we defined. We propose an enhancement for the route navigation process by including the acquired road information.
A Study on the Gm Based Current Mode Capacitance Multipliers Implementation

This paper presents a detailed study of a current mode capacitance multiplier architecture that uses transconductance amplifiers. A frequency analysis carried on the structure built with ideal transconductors reveals an equivalent input impedance model given by a series of a capacitor and a resistor. A second analysis, considering non-ideal effects such as input and output capacitances or non-zero output conductance, shows that the initial model has to be completed with three other elements. It is demonstrated that the parallel input conductance determines the low frequency resistive behavior of the structure. The model resulting from this analysis is validated by simulations of two implementations using transconductance amplifiers that have similar transconductance characteristics but non-equal output conductance.
Fire Detection in Infrared Video Surveillance Based on Convolutional Neural Network and SVM

In this paper, a novel algorithm based on convolutional neural network (CNN) and support vector machine (SVM) for fire detection in infrared (IR) video surveillance is proposed. To improve the performance of IR fire detection, we develop a 9-layer convolutional neural network named IRCNN instead of traditional empirically handcrafted methods to extract IR image features. Then, a linear support vector machine is trained with extracted features to achieve fire detection. Our network adopts data augmentation technique and Adam optimization to deal with problems caused by the insufficient dataset, and accelerate the training process. Experimental results show that our method achieved both high precision (98.82%) and high recall (98.58%) on our IR flame dataset and real-time detection on the ordinary infrared surveillance cameras.
[WiP] HTU: An Efficient Hash-Trie Data Structure for URL Matching for Programmable Web Resources

The world-wide-web is growing at an alarming rate. The number of websites and web-resources available on the internet is growing following the axioms of Moore-law. Such enormous growth is due to the addition of static website content and a large number of dynamic web-resources. A significant portion of it is the addition of REST based web-services which allows creating programmable web-resources that can be added, modified and removed using the ubiquitous HTTP protocol. As a result, the current generation of web-servers not only has to provide access to static resources but also of dynamic nature. We propose a new data structure that can be embedded in HTTP servers. The data structure is optimized to handle REST based services. It reads REST based service declarations, organizes the various methods in an optimized way for faster access and scale to support thousands of concurrent users.
Analysis and Compensation of Sampling Errors in TPFS IPMSM Drives With Single Current Sensor

The current reconstruction strategies using single current sensor in the motor drives are widely researched. However, the sampled currents do not represent the average values within the modulation period, especially for the asymmetric modulation or asymmetric sampling solutions. This is due to the following two reasons: 1) The detected current values obtained at different sampling points are utilized to calculate the three-phase currents; and 2) the detected instantaneous currents are applied to estimate the average current values. In this letter, the current change rates under different action vectors are estimated to compensate these errors in a three-phase four-switch interior permanent magnet synchronous motor (IPMSM) drive with single current sensor. The effectiveness of the proposed strategy is verified by experimental results on a 5-kW IPMSM motor prototype, which shows that the current reconstruction errors are reduced after compensation.
Tuning of Passband in Compact Substrate Integrated Waveguide Bandpass Filter by Controlling Higher Order Modes

This paper presents a standard process for tuning the center frequency and bandwidth of a dual mode passband by controlling the higher order orthogonal modes of a compact substrate integrated waveguide (SIW) cavity. The bandpass filter is based on a cascaded dual cavity structure, derived from circular SIW and metallic vias are introduced to control the passband generating modes. Dimension and position of the tuning vias are varied to control the orthogonal modes and tune the center frequency of passband within a range of 8 GHz to 10 GHz. More than 3% change in resonance frequency and 3% change in bandwidth are shown in the simulated results with 1.62 dB minimum insertion loss and nearly 15 dB return loss in passband.
Deviation-Sparse Fuzzy C-Means With Neighbor Information Constraint

This paper introduces sparsity in the traditional fuzzy clustering framework and presents two novel clustering methods. The first one is called deviation-sparse fuzzy c-means (DSFCM). When spatial correlation is encountered, the second method is proposed, which is called deviation-sparse fuzzy c-means with neighbor information constraint (DSFCM_N). The contributions of this paper are threefold. First, the theoretical values of data, estimated from the measured values, are utilized in the clustering process. This could acquire more accurate cluster centers than the traditional fuzzy c-means. Second, by imposing sparsity on the deviations between measured values and theoretical values, DSFCM and DSFCM_N could identify noise and outliers. Finally, with the constraint of neighbor information, the estimation of the deviations between measured values and theoretical values of data would be more reliable than only considering the data itself. Experiments performed on artificial and real-world images show that DSFCM_N is effective and efficient, and thus more competitive than other fuzzy clustering methods.
Current Sensing Delay in Three-phase Inverter

The performance of a three-phase power conversion system can be largely affected by the inverter’s current regulation. In closed-loop current regulation, the current sensing quality is critical to the overall regulation accuracy and bandwidth. This paper investigates the current sensing delay in a three-phase inverter in an aviatic traction application. By comparing the current sensing of point field detection-based current sensors and shunt resistor-based current sensors in three-phase power conversion, the effect of current sensing delay are presented under different operating conditions.