Miracle-dz/UniFAR-FTUs huggingface dataset:
IQSon: A Context-aware Negotiator Agent with Enhanced Utility and Decision Making Speed

In an automated negotiation, software agents try to gain the best possible utility on behalf of their owners. The main challenge in these negotiations is not knowing the opponents’ preferences. Although methods such as opponent modeling can be used to solve this problem, modeling generally has a computational overhead and requires time. In areas such as perishable goods, energy markets and fashion industry, we need to negotiate quickly which hinders the use of modeling. In this paper, an agent named IQSon is presented. Using a multi-policy strategy, this agent negotiates in a multilateral environment without creating a model but by considering a short history of opponents’ offers. Experimental results show that the agent gains good utility while negotiating with state of the art agents. Furthermore results show the effectiveness of the bidding policies. IQSon also participated in the International Automated Negotiating Agents Competition (ANAC2018) and was eventually ranked 4th out of 22 international participants in the individual utility rankings and 5th in the social welfare rankings.
A 5G Multibeam Antenna Including Rotman Lens and Slot Array Antenna

in this paper, a multibeam network is designed and simulated in 28 GHz which consists a Rotman lens and a planar slot array antenna. The slot array antenna is implemented on Substrate Integrated Waveguide (SIW) and is fed by a microstrip Rotman lens. The planar slot array antenna and Rotman lens are connected to each other using a transition from microstrip to SIW. Five distinct beams are realized through the use of Rotman lens with gain of about 16 dBi. The efficiency of multibeam antenna fed by each of the five beam ports is in the range of 60% to 70% which is higher than that of presented in other works due to better design of dummy port configuration.
Text-Independent Speaker Identification by Combining MFCC and MVA Features

Feature extraction is one of the most crucial stages of Speaker Identification (SI), which significantly influences the performance of SI system. There are many feature extraction approaches. The most popular and the most commonly used in this area are Mel-Frequency Cepstral Coefficients (MFCCs). The MFCC features give a good performance in the same environmental conditions (train/test). However, they are very sensitive in the presence of background conditions, which significantly decrease SI system performance. To overcome this problem we have proposed to use the mean and variance normalization then we apply auto-regression moving-average filtering (MVA) to MFCC features, and then we combine the resulting features with MFCC features. Our Text-independent SI system is based on Gaussian Mixture Model (GMM), and the experiments were conducted using TIMIT database. Our method proves to be promising, achieving up to 28% accuracy at signal to noise ratio (SNR) 5 dB.
Differential Phase Doppler Radar With Collocated Multiple Receivers for Noncontact Vital Signal Detection

A differential phase Doppler radar sensor with multiple receivers (RXs) to remotely detect human vital signals is proposed, which can reduce common motion artifacts as well as common noises to RXs. This is achieved by differentiating two-phase signals from a pair of collocated RXs. Random motions are decomposed into several kinds of motions with respect to the transmitted beam direction. Six differential phase signals are obtained from the four RX combinations, among which at least one pair can get vital signals with reduced common motion artifacts. A weighted-sum method is proposed to combine six differential signals effectively, which allows vital signals to be clearly detected even with large random body motions. A sensor is implemented with a commercially available 24-GHz radar front-end chip with one transmitter and four RXs to verify the differential phase radar concept. An experiment demonstrates that body motion noises are reduced by over 20 dB with the proposed differential phase radar with multiple RXs.
Wideband Slot Antenna Backed by Cylindrical Substrate-Integrated Waveguide Cavity

A cylindrical substrate-integrated waveguide cavity-backed slot antenna is presented in this paper, which is devised for wideband operation. The fundamental and critical approach to generate a wide antenna bandwidth performance, in this case, is to merge three basic resonant modes (TM<sub>010</sub>, TM<sub>110</sub>, and TE<sub>111</sub> modes) of the cylindrical cavity. In this paper, a fractional impedance bandwidth of 60% with S<sub>11</sub> < −10 dB from 8 to 14.8 GHz has been achieved through this scheme. This antenna has a compact size of <inline-formula> <tex-math notation="LaTeX">$0.33\lambda $ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> denotes the space wavelength corresponding to the operating center frequency <inline-formula> <tex-math notation="LaTeX">$f =11.25$ </tex-math></inline-formula> GHz in this demonstration), which is found suitable for many wireless applications. Its resulting simple structure is definitely of benefit to the fabrication and integration with other front-end parts. The proposed antenna exhibits a broadside radiation pattern, which is stable within the operating frequency band of interest. The simulated radiation efficiency is above 85%. The antenna has been fabricated and experimentally verified. Measured results show a good agreement with simulated counterparts.
Identification of brain‐targeted bioactive dietary quercetin‐3‐O‐glucuronide as a novel intervention for Alzheimer's disease

Epidemiological and preclinical studies indicate that polyphenol intake from moderate consumption of red wines may lower the relative risk for developing Alzheimer's disease (AD) dementia. There is limited information regarding the specific biological activities and cellular and molecular mechanisms by which wine polyphenolic components might modulate AD. We assessed accumulations of polyphenols in the rat brain following oral dosage with a Cabernet Sauvignon red wine and tested brain‐targeted polyphenols for potential beneficial AD disease‐modifying activities. We identified accumulations of select polyphenolic metabolites in the brain. We demonstrated that, in comparison to vehicle‐control treatment, one of the brain‐targeted polyphenol metabolites, quercetin‐3‐O‐glucuronide, significantly reduced the generation of β‐amyloid (Aβ) peptides by primary neuron cultures generated from the Tg2576 AD mouse model. Another brain‐targeted metabolite, malvidin‐3‐O‐glucoside, had no detectable effect on Aβ generation. Moreover, in an in vitro analysis using the photo‐induced cross‐linking of unmodified proteins (PICUP) technique, we found that quercetin‐3‐O‐glucuronide is also capable of interfering with the initial protein‐protein interaction of Aβ1–40 and Aβ1–42 that is necessary for the formation of neurotoxic oligomeric Aβ species. Lastly, we found that quercetin‐3‐O‐glucuronide treatment, compared to vehicle‐control treatment, significantly improved AD‐type deficits in hippocampal formation basal synaptic transmission and long‐term potentiation, possibly through mechanisms involving the activation of the c‐Jun N‐terminal kinases and the mitogen‐activated protein kinase signaling pathways. Brain‐targeted quercetin‐3‐O‐glucuronide may simultaneously modulate multiple independent AD disease‐modifying mechanisms and, as such, may contribute to the benefits of dietary supplementation with red wines as an effective intervention for AD. —Ho, L. , Ferruzzi, M. G. , Janle, E. M. , Wang, J., Gong, B. , Chen, T. ‐Y. , Lobo, J., Cooper, B. , Wu, Q. L. , Talcott, S. T. , Percival, S. S. , Simon, J. E. , Pasinetti, G. M. Identification of brain‐targeted bioactive dietary quercetin‐3‐O‐glucuronide as a novel intervention for Alzheimer's disease. FASEB J. 27, 769–781 (2013). www.fasebj. org
Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm

Recommender systems are intelligent data mining applications that deal with the issue of information overload significantly. The available literature discusses several methodologies to generate recommendations and proposes different techniques in accordance with users’ needs. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. The biggest challenge for a recommender system is to produce meaningful recommendations by using contextual user-item rating information. A context is a vast term that may consider various aspects; for example, a user’s social circle, time, mood, location, weather, company, day type, an item’s genre, location, and language. Typically, the rating behavior of users varies under different contexts. From this line of research, we have proposed a new algorithm, namely Kernel Context Recommender System, which is a flexible, fast, and accurate kernel mapping framework that recognizes the importance of context and incorporates the contextual information using kernel trick while making predictions. We have benchmarked our proposed algorithm with pre- and post-filtering approaches as they have been the favorite approaches in the literature to solve the context-aware recommendation problem. Our experiments reveal that considering the contextual information can increase the performance of a system and provide better, relevant, and meaningful results on various evaluation metrics.
A Novel Photovoltaic Current Collector Optimizer to Extract Maximum Power during Partial Shading or Mismatch Conditions

Partial shading and mismatch between photovoltaic (PV) panels have significant negative effects on the power generated from PV array and total efficiency of the PV system. Both conditions cause a non-uniform aging of PV modules and may lead to hot spot phenomenon that destroys the PV panels. Under both conditions, the characteristics of the entire PV array are deformed and exhibit multiple peaks, including global and local maximum power points (MPPs) that need an intelligent MPP tracking algorithm to track the global MPP. This paper proposes a new photovoltaic current collector optimizer (CCO) to improve the power captured from PV array in case of partial shading or mismatch conditions. The PV modules are stacked by CCOs and then interfaced to the utility grid through an inverter, which introduces a new topology of centralized inverter technology. Simulation results show a remarkable increase in output generated power for the proposed centralized topology compared to conventional ones.
Design and Experimental Development of Hexapod Robot with Fiberglass-Fibercarbon Composite Legs

Over the last few years, the universal number of legged robots has increased astonishingly in outdoor environments. In this paper, the design, and control of a new RHex robot named IRHex with innovative Fiberglass-Fiber carbon composite legs are described. IRHex is a six-legged robot_one actuator located at each hip_achieving mechanical uncomplicatedness that provides a reliable performance. In this investigation, the new material used for building IRHex legs was discovered after several tests on the effect of sequence and orientation of laminates in the composite. IRHex achieves robust locomotion using a computer board analyzing the data sent by the sensors and communicating to the ground station even from far distances.
Robust Adaptive H∞ Control for Attitude Control of a Quadrotor

This paper proposes a new concept of adaptive robust H∞ control strategy for attitude control of a quadrotor flying robot. The system modeling is obtained by Euler-Lagrange formulation. New proposed control system which is developed based on nonlinear H∞ has a promising performance in terms of stabilization even in the presence of external disturbance, parametric uncertainties and unmodeled dynamics. Finally, in order to eliminate the effects of parametric uncertainties and external disturbance, parameters estimation is used to real time estimating of moment inertia parameters and wind. The parameter estimation is based on adaption rules of nonlinear H∞ which feeds the estimates to linear parameters and closed loop stability was shown by Lyapunov stability theory.
Elaboration of a Conceptual Scheme of Municipal Units Classification According to the Level of Budgetary Provision on the Base of Cluster Analysis

The paper considers the problem of leveling of municipal units development structural disparities with the aim of evening-out of negative effects of fragmentary economic space formation, social contradictions accumulation, low efficiency of strategic goals and tasks achieving of territories’ socio-economic development. In this connection the paper describes a conceptual scheme of municipal units classification according to the level of a budget sector development. The scheme implements the methods of cluster and discriminant analysis and differs from the existing classification approaches in using of the most widely-spread seven agglomerative methods of hierarchical clusterization and the methods of measuring-in of the metric distance between objects in multidimensional space.
Automatic Fault Detection of Power Lines using Unmanned Aerial Vehicle (UAV)

Safety and automation are the two major challenges in the application of Unmanned Aerial Vehicle (UAV), commonly known as drone, to the power lines inspection and fault detection. While current state-of-the-art UAVs are equipped with collision avoidance features, there is less attention to the automatic and real-time fault detection of power lines using UAVs. This paper presents the architecture of three drone-oriented concept designs for automatic and real-time fault detection of power lines using UAVs. The proposed systems could be potential candidates for replacing traditional inspection methods of power lines, which are risky and costly. By incorporating a robust neural network, i.e., Artificial Intelligence (AI), and using appropriate and efficient sensors, the systems can automatically detect various faults and defects on power lines with high precision. We propose three concept design options comprised of different hardware/software components and their feasibility factors. For instance, FLIR Duo Pro R as a thermal sensor and Zenmuse XT for thermal vision have been proposed to be used in the concept designs. For data communication, the proposed designs use cloud-based virtual private network (VPN) for a secure connection between remote control (RC) of the UAV and the server. Based on the advantages and disadvantages of the three proposed design options, the most efficient design is also discussed. This design proposes a system with lightweight sensors, which could increase the flight time of the UAV. Further, the AI interface is coded on to the RC, making it economical, without any database for big data storage. The back-end of the neural network is stored in a cloud server. With the help of GSM antenna, the AI can run on the tablet if there is an available cellular network.
Fabrication and Characterization of Active-Matrix  $960\times540$  Blue GaN-Based Micro-LED Display

In this paper, we demonstrate a high-resolution <inline-formula> <tex-math notation="LaTeX">$960\times 540$ </tex-math></inline-formula> blue-emitting active matrix micro-display. The blue micro-display have a diagonal length of 0.55 inch, a pixel size of <inline-formula> <tex-math notation="LaTeX">$8~\mu \text{m}$ </tex-math></inline-formula> with a pixel pitch of <inline-formula> <tex-math notation="LaTeX">$12.8~\mu \text{m}$ </tex-math></inline-formula>, and high pixel density of 2000 pixels per inch. The single pixel of micro-display exhibits excellent characteristics including an extremely low-leakage current of 0.1 pA at −5 V, a low forward voltage of 2.73 V, a narrow full width at half maximum of 15.7 nm of electroluminescence spectra, a high external quantum efficiency of 13.3%, and a deep-blue emission with CIE coordinates of (0.1589, 0.0542) at <inline-formula> <tex-math notation="LaTeX">$11~\mu \text{A}$ </tex-math></inline-formula>. Besides, the single pixel of micro-LED also shows high brightness of <inline-formula> <tex-math notation="LaTeX">$4.2 \times 10^{6}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$7.4 \times 10^{6}$ </tex-math></inline-formula> cd/m<sup>2</sup> at <inline-formula> <tex-math notation="LaTeX">$500~\mu \text{A}$ </tex-math></inline-formula> and 1 mA, respectively. The complete <inline-formula> <tex-math notation="LaTeX">$960\times 540$ </tex-math></inline-formula> micro-display has a contrast ratio of 3600000:1, the power consumption with all on is 0.5 W, and the pixel yield rate of about 98.674% analyzed by the software ImageJ.
Opportunities for Natural Language Processing in Qualitative Engineering Education Research: Two Examples

This research full paper proposes opportunities to expand qualitative and textual data analysis using natural language processing (NLP), and demonstrates opportunities to use NLP in engineering education work in our presentation of two NLP-based projects as examples of how NLP can be used. The discipline of engineering education frequently employs qualitative data analysis techniques, but fundamentally, the analysis of large corpora of textual documents is limited by researcher’s time. In this paper, we present a brief review literature of how NLP has been used in other qualitative research fields. This portion of the paper is aimed to provide a clear description of NLP for those unfamiliar with machine learning and natural language processing methods. The second part of the paper will provide two brief examples of how NLP is being employed in our research group. Example 1 is a study of engineering résumés, with the intention of being able to calculate the “disciplinary discourse density” based off the engineering language presented in engineering résumés, a technique validated in prior qualitative studies by hand. Example 2 is a genre analysis of engineering literature reviews, seeking to understand the ways in which sentences linguistically build into arguments, such that the task of writing literature reviews might be demystified. This paper will have a methodological impact for researchers attempting to use NLP methods to analyze qualitative data, articulating the opportunities and barriers in using these methods for engineering education research.
Real-time Background Subtraction via L1 Norm Tensor Decomposition

Currently, background subtraction is being actively studied in many image processing applications. Nuclear Norm Minimization (NNM) and Weighted Nuclear Norm Minimization (WNNM) are commonly used background subtraction methods based on Robust Principal Component Analysis (RPCA). However, these techniques approximate the RPCA rank function and take the form of an iterative optimization algorithm. Therefore, due to the approximation, the NNM solution can not converge if the number of frames is small. In addition, the NNM and WNNM processing times are delayed because of their iterative optimization schemes. Thus, NNM and WNNM are not suitable for real-time background subtraction. In order to overcome these limitations, this paper presents a real-time background subtraction method using tensor decomposition in accordance with the recent tensor analysis research trend. In this study, we used the closed form TUCKER2 decomposition solution to omit the iterative process while retaining the L1 norm of the RPCA rank function. This proposed method allows for convergence even when the number of frames is small. Compared to NNM and WNNM, the proposed method reduces the processing time by more than 80 times and has a higher precision even when the number of frames are less than 10.
Operating Frequency Effect on LC-circuit Flat Circular Coils in Inductive Powering Unit for Implants

The interrelation between coil geometry and operating frequency in the inductive powering unit is complex and can be assessed in several ways. In this paper, a pair of LC-circuits with series compensation is investigated in the following manner. The flat circular coil is optimized in order to obtain self-inductance to provide resonance of the pair of LC-circuits. The operating frequency is changing in the range of 1…10 MHz. The dependence of the coil geometric parameters (outer and inner radii, the number of turns) on the operating frequency is evaluated. A method for reducing the size of the coils has been proposed.
Vertical LTCC Integrated Rectangular Waveguide and Transitions for Millimeter-Wave Applications

A new vertical guiding structure in low-temperature cofired ceramics (LTCC) substrates is proposed. This vertical LTCC integrated waveguide (V-LIW) allows transmitting signals and realizing components in the body of the LTCC substrate perpendicular to its surface. This vertical integration leads to considerable footprint reduction while opening the door to higher frequency coverage as well as easy implementation of $E$ -plane structures. Discontinuities in V-LIW and transitions between the proposed V-LIW and conventional horizontal guiding structures such as coplanar waveguides, standard rectangular waveguides, and substrate integrated waveguides are also introduced. Applications to the design of V-band and W-band filters, couplers, and power dividers are presented. Several prototypes are fabricated with measurement results showing a good agreement with simulation data.
Research of Reconstructed Wafer Surface Planarity on the Metall-Compound-Silicon Boundary for Multi-Chip Module with Embedded Dies

One of the most important problem for multi-chip module manufacturing technology with embedded in carrier cavities dies is ensuring surface planarity. Critical areas in this case are the gaps between the carrier and dies, filled with compound. Absence of planar surface in these areas can lead to defects in commutation layers, internal stresses and, as result, to significant decrease in assembly reliability. In this article, surface reliefs of carrier with embedded dies after molding with variety epoxy and silicone based polymers have been investigated. As a result, the minimum irregularity height of about 4 microns was achieved for optimal combination of molding compound and polyimide coating in the minimum gap with of about 250 microns.