Miracle-dz/UniFAR-FTUs huggingface dataset:
Block-Diagonal Zero-Forcing Beamforming for Weighted Sum-Rate Maximization in Multi-User Massive MIMO Systems

Beamforming is one of the most important transmission technologies to improve the quality of communication in cellular network systems. However, in massive multiple-input-multiple-output (MIMO) systems, conventional optimization-based iterative beamforming algorithms often suffer from high computational complexity and thus are not suitable for practical applications. This paper focuses on low complexity beamforming technique for multi-user massive MIMO systems. Specifically, a block-diagonal zero-forcing (BD-ZF) beamforming algorithm is proposed for achieving weighted sum-rate maximization. We show that the BD-ZF beamforming problems can be globally solved using water-filling algorithms. Extensive simulations demonstrate that the proposed BD-ZF beamforming method can offer better performance than the state-of-art low complexity beamforming technique ZF (even could sometimes coincide with the performance of the popular WMMSE algorithm) but with only an extra little bit computational overhead.
The role of c-Jun in Schwann cell morphology and migration

Schwann cells of peripheral nerves undergo significant changes in morphology and gene expression during development and regeneration of the peripheral nervous system (PNS) after nerve injury. This plasticity is associated with the greater regenerative capability of the PNS compared with the CNS. The transcription factor c-Jun is highly expressed in Schwann cells following nerve injury and is required for the reprogramming of mature Schwann cells to become repair Schwann cells that promote regeneration. Mice with a Schwann cell-specific c-Jun deletion fail to generate these repair cells after nerve injury and consequently have a severe failure of axonal regeneration. One striking phenotype of Schwann cells lacking c- Jun is that in culture they lack normal bi- and tri-polar morphology and in the distal stump of injured nerve they form irregular and flattened profiles. I assessed whether the loss of c-Jun affected their ability to migrate, an important property of repair Schwann cells in vivo after nerve injury. Using two complementary in vitro assays, I revealed a significant reduction in cell migration and found that c-Jun deletion caused changes to the actin cytoskeleton and reduced the formation of focal adhesions. Nevertheless, performing quantitative polymerase chain reaction on a number of genes already known to regulate the cytoskeleton failed to reveal differences between RNA from c-Jun knockout Schwann cells and control RNA. I next investigated whether Schwann cell deletion of c-Jun leads to a reduction of Schwann cell migration and axonal outgrowth across the nerve bridge of the transected nerve. I developed an assay to create a nerve bridge that can be replicated easily in size and proximal-distal location. I achieved this by transecting the peroneal branch of the sciatic nerve while leaving the tibial branch intact to support the two nerve stumps. This method provides a useful model of nerve transection. The results reveal that in the c-Jun cKO mouse at 7 days after injury Schwann cells succeeded in migrating across the nerve bridge, but axons show poor re-entry into the distal nerve stump. This requires further investigation.
End-to-end identification of pharmaceutical blister packages based on one-side handheld images

Image-based identification of handheld pharmaceutical blister packages occurred during prescription dispensing process is challenging in that the packages are held in arbitrary positions with partial occlusion. Previous solutions rely on combining two complementary images of the handheld packages taken in opposite views, which are then processed in two stages: one for cropping the packages and the other for classification. In contrast, this paper presents solutions that rely on only one side images and require only one end-to-end deep learning network. In particular, Mask R-CNN and FOTS are utilized. Our experiments show that, given a pool of about 230 types of pharmaceutic blister packages commonly found in adult lozenges prescription stations, these two end-to-end solutions attain F1 scores of 99% and 96% for identification performance, comparable to that by previous two-staged architectures. Additional advantages of the new solutions include more compact architectures, faster runtime performance, with less training data and training process involved.
Torque Improvement in Dual M-Phase Permanent-Magnet Machines by Phase Shift for Electric Ship Applications

This paper proposes a design technique for electric ship traction machines to achieve high torque density and low torque ripple by adopting dual m-phase windings with an appropriate phase shift. Firstly, the general relationship between the slot number and the phase number for dual m-phase windings is investigated. Secondly, the uniform analytical expressions of torque with different phase shifts are derived. Also, the optimal phase shift angle is summarized from the perspective of improving torque performance. Then, the 48-slot and 22-pole permanent-magnet machines for electric ship with different windings configurations are designed. The finite-element method is used to calculate their electromagnetic performances, such as back electromotive force, torque capability, stator magnetic motive force and radial force. Finally, some experiments on the prototype machine for electric ship are carried out for validation.
(Graph Database: A Survey)

The advantages of Relational Database Management System (RDBMS) model and design methodology are being utilized by industry/institutions for any software design and implementation. The future of RDBMS certainly will be the Graph Databases with NoSql methodologies, which is emerging as beyond of relational model. In this paper, there is a need to highlight all the databases evolved after RDBMS. They couldn’t stay in market for so long period and survey has been made to highlight those databases after RDBMS. Relational Database Management System has certain advantages like (i) Storing in Tables, Column and Rows (ii) Data Storing in Normal Form (iii) Easy to use via SQL to retrieve information via complex join operators (iv) Maintainability via Reverse Engineering (v) Indexing and quick search. Due to these inherent features of RDBMS and SQL, it is necessary to explore and compare RDBMS with NoSQL methods to avoid complex join operation. Recently, numerous software industries and research institutions are trying their old RDBMS system to be re-engineered into some other architecture via nodes, edges and relationships where different type of information can be stored easily. So, it is a big challenges for any industry and institutions how quickly they can re-engineer their old RDBMS into Graph Databases which is also called now-a-days the future of databases. In this project, it is highlighted that the importance of the re-engineering work lies in three different directions such as (i) Comparison of RDBMS with GDBMS (Graph Database Management System) where face book, twitter, Amazon, Google are adopting (ii) Survey work of Graph Databases and (iii) Graph Database Models have increasingly become a topic of interest. The representation of data in the form of a graph lends itself well to structure a data with a schema. No standard system of query languages yet had been found to have been unique and stable for graph databases. Research and industry adoptions will determine the future direction of graph databases. (iv) Beyond RDBMS artifacts were established by industry and academics. It feeds a series of recycling collectives trying to eke out an existence of positive incentives and principles.
An Approach to Unsupervised Detection of Fully and Partially Destroyed Buildings in Multitemporal VHR SAR Images

In the presence of abrupt change events, multitemporal synthetic aperture radar (SAR) data represent a precious supporting tool for quantifying changes, in particular in urban areas. A large amount of SAR data also exists at very high resolution (VHR). Over urban areas, the introduction of the VHR imagery moves the analysis down to the single building scale. However, VHR imagery is also characterized by a large heterogeneity and a more complex representation of the building. In this work, we propose a geometrical model for describing partially destroyed buildings and derive the corresponding multitemporal backscattering signature by applying the ray-tracing method. The model is integrated into an unsupervised automatic approach for the detection of both fully and partially destroyed buildings. The strategy considers a hierarchical structure of the changes. Experimental results conducted on two multitemporal VHR SAR datasets show a large robustness of the approach and good accuracy in the detection of the classes for damaged buildings with different severity levels.
Using Machine Theory of Mind to Learn Agent Social Network Structures from Observed Interactive Behaviors with Targets

Human social interactions are laden with behavioral preferences that stem from hidden social network representations. In this study, we applied an artificial neural network with machine theory of mind (ToMnet+) to learn and predict social preferences based on implicit information from the way agents and social targets interact behaviorally. Our findings have implications for machine applications that seek to infer hidden information structures solely from third-person observation of behaviors. We consider that social machines with such an ability would have an enhanced potential for more naturalistic human-machine interactions.
Combination of Super-Resolution PSI and Traditional PSI by Identification of Homogeneous Areas

The performance of Persistent Scatterer Interferometry (PSI) depends heavily on Persistent Scatterer (PS) density. In order to increase PS density, we can apply Super-Resolution reprocessing algorithms in PSI. Involving the reprocessing algorithms and the peak-detection-based Persistent Scatterer Candidate points (PSCs) selection method, the full PSI chain is referred to as Super-Resolution PSI (SR-PSI). The implementation of the Super-Resolution reprocessing algorithm, however, is computationally intensive, which makes SR-PSI time-consuming. In this work, we propose to improve the efficiency by constraining the Capon-based reprocessing to the non-homogeneous areas (e.g., urban areas). We notice that the Capon algorithm performs similarly as the Fourier-based algorithm for homogeneous regions (e.g., grassland), thus we can use Single Look Complex (SLC) images for these areas. With the Coefficient of Variation (CV) as the index, we divide the full image into two classes: homogeneous areas, for which we select PSCs from the original stack, and non-homogeneous areas, for which we extract PSCs from the Capon-based reprocessed images. Then we combine the PSCs of both cases for further PSI processing. We applied the combination method to a stack of TerraSAR-X data. The results show that the proposed approach is more computationally efficient than the original SR-PSI with the effectiveness uncompromised, especially for applications aiming at the urban deformation.
Development of a Pseudo-Random Sequence Generation Function Based on the Cryptographic Algorithm "Kuznechik"

The paper deals with the main component of a deterministic random sequence generator – a generation function based on the algorithm "Kuznechik" (Russian encryption standard GOST R 34.12-2015). A procedure of round keys generation that uses the features of the algorithm has been developed. It allows to use the algorithm in a mode that combines the advantages of well-known OFB and CTR encryption modes. In addition, some enhancements of the algorithm that improve the generator security while maintaining its performance are implemented. The quality of the generator was evaluated by means of statistical tests (NIST SP 800-22), Pear-son's criterion, and min-entropy (NIST 800-90B tests). According to the analyzed characteristics, the enhanced generator and its ordinary version based on "Kuznechik" algorithm are comparable, but the proposed generation function has a higher security level.
EEG–Based Emotion Classification Using Convolutional Neural Networks

Research in recognizing emotion has been done with many methods, one of them by using brain wave or electroencephalography (EEG). The main benefit of using EEG is because of its ability to track events within brain in millisecond accuracy. Psychologist proposed a model to help classify human emotions, which can be divided into four quadrants using two dimensional emotions of arousal and valence. In this paper, Convolutional Neural Network was proposed as a method for recognizing emotion in the EEG data due to its advantages, such as: local connections, shared weights, pooling and using series of layer to handle high dimensional data of EEG. The overfitting problem in EEG dataset arising from insufficient sample data has been successfully addressed using data augmentation process with effective window size of 4 seconds. The best model is achieved with the accuracy of 72% for arousal and 71% for valence.
Multiple-Robot Mediated Discussion System to support group discussion *

Deep discussions on topics without definite answers are important for society, but they are also challenging to facilitate. Recently, advances in the technology of using robots to facilitate discussions have been made. In this study, we developed a multiple-robot mediated discussion system (m-RMDS) to support discussions by having multiple robots assert their own points and lead a dialogue in a group of human participants. The robots involved the participants in a discussion through asking them for advice. We implemented the m-RMDS in discussions on difficult topics with no clear answers. A within-subject experiment with 16 groups (N=64) was conducted to evaluate the contribution of the m-RMDS. The participants completed a questionnaire about their discussion skills and their self-confidence. Then, they participated in two discussions, one facilitated by the m-RMDS and one that was unfacilitated. They evaluated and compared both experiences across multiple aspects. The participants with low confidence in conducting a discussion evaluated the discussion with m-RMDS as easier to move forward than the discussion without m-RMDS. Furthermore, they reported that they heard more of others' frank opinions during the facilitated discussion than during the unfacilitated one. In addition, regardless of their confidence level, the participants tended to respond that they would like to use the system again. We also review necessary improvements to the system and suggest future applications.
SBR for Near-Field Scattering of PEC Objects Under Far-Field Antenna Radiation

In this paper, the scattered near-fields from electrically large complex PEC objects illuminated by far-field antenna sources are calculated by shooting and bouncing ray (SBR) method. In order obtain accurate near-field scattering results, an physical-optics (PO) near-field integral representation is proposed and reduced to closed form expressions to improve computing efficiency by applying locally expanded Green function approximations. By introducing the proposed PO near-field integral expressions, SBR can be used to analyze near-field scattering from a PEC target under far-field antenna sources radiation. The method is applicable to analyze the near-field scattering problem under arbitrary far-field source incident. Numerical example is presented to demonstrate the efficiency and accuracy of the proposed method.
Research on load distribution control technology for parallel operation of power source with different rated capacity

In order to solve the Load Distribution Control (LDC) problems when power sources are operating in parallel with different rated capacities, a LDC strategy based on rated capacity is proposed in this paper. Taking the defined output coefficient as the control object, and combining with the LDC circuit of common signal bus, it can realize the LDC target in accordance with the size of power source capacity. On the basis of the LDC strategy, a flexible switching control method for parallel operation of power sources is designed, a LDC technology based on residual capacity and used in parallel operation of energy storage devices is designed too. These not only can improve the operation security of power source, but also the energy utilization rate of the energy storage devices can be further improved.
Optimal Allocation of Distribution Automation Terminals Considering Incremental Benefit

Distribution automation terminals play a crucial role in distribution automation systems to deliver real-time monitoring and fault detection. It is of significance to determine where and how many terminals should be allocated in distribution networks for the best benefit. This paper proposes an optimal allocation model of distribution automation terminals. Firstly, the reliability calculation of distribution networks with distribution automation terminals is performed. Then, the ratio between the reliability improvement degree and the comprehensive cost of newly installed distribution automation terminals is defined as the incremental benefit evaluation index. The optimal allocation model aims to consider a tradeoff between distribution system reliability and economy by not only achieving maximum incremental benefit but also satisfying the reliability requirement. This model also plans the installation types, positions and quantity of distribution automation terminals. Finally, cuckoo search algorithm (CSA) which can efficiently obtain the global optimal solution is used to solve the model. The rationality and validity of the proposed model are verified by IEEE RBTS BUS-2 system.
A Radial Basis Function Technique for the Early Detection and Measurement of Hip Implant Loosening

Hip implants are extremely common procedures that are performed to relieve pain and restore function to the hip joint. Historically, most implants would last for the duration of the patient’s life, approximately 15 years after implantation; however, due to medical advancements patients are living longer and outliving the life of their implant. The most common cause for implant failure is the failure of the bond between the implant and the bone, occurring due to the translation or rotation of the implant. Once an implant has failed, a very invasive, costly, and painful revision surgery is required. There is significant advantage in the early detection of implant loosening. This paper presents a radial basis function based image processing technique to detect minute 3-D rotations of a hip implant from 2-D X-ray images. Comparing these rotations for a particular hip implant over time can alert orthopedic surgeons of trends that might lead to impending gross loosening of the implant and enable early correction.
Development of a wearable motion capture system to evaluate the knee joint angle during stair-climbing in hemiplegics

An Inertial Motion Capture System (IMCS) with wireless communication was developed to evaluate the knee joint angle during stair climbing. Two tests were performed, the first one had 10 young subjects (age=20-25 years) in healthy conditions and the second test had only one hemiplegic older adult subject (age=75 years). As a first phase, the knee joint angle was measured from the 10 young subjects using the IMCS developed in order to visualize the movement patterns and analyze the maximum, minimum and average values obtained. In the second phase, the IMCS developed was tested in parallel with the AKTOS IMCS (commercial equipment) to monitor a different activity (walking) and validate the data obtained. The third phase are the results; the knee joint angle was measured in the hemiplegic older adult subject using both IMCS devices to obtain the typical movement pattern. The data was provided by the graphical user interface of the IMCS denoting an average right and left knee angular amplitude of 73.44° and 75.77° respectively, compared to the 10 healthy subjects who presented an average amplitude of 109.73°±8.03° (right knee) and 108.74°±12.18° (left knee). In addition, a different movement curve is denoted and the typical pattern of this hemiplegic subject occurs between the "Pull-up" and "Forward continuance" phases of the stair-climbing cycle.
A Stochastic Analysis of the One Compartment Pharmacokinetic Model Considering Optimal Controls

First-order 1-compartment pharmacokinetic model for extravascular administered drugs can be used to derive many useful quantities by comparing the predicted values with actual data. However, less research has been done in actually formulating them as optimal control problems. Moreover, real pharmacological processes are always exposed to influences that are not completely understood or not feasible to model explicitly. Ignoring these phenomena in the modeling may affect the estimation of PK/PD models’ (pharmacokinetic/pharmacodynamic models’) parameters and the derived conclusions.Therefore there is an increasing need to extend the deterministic models to models including a stochastic component. In our study, we modify the 1-compartment pharmacokinetic model to a stochastic differential equation model based on an optimal control problem. A schedule of optimal dosing and timing has been given from our proposed model.
Hexagonal Shaped Near Zero Index (NZI) Metamaterial Based MIMO Antenna for Millimeter-Wave Application

A single-layered multiple-input multiple-output (MIMO) antenna working at 28 GHz loaded with a compact planar-patterned metamaterial (MTM) structures is presented in this paper for millimeter-wave application. A combination of a split square and hexagonal shaped unit cell is designed and investigated with a wide range of effective near-zero index (NZI) of permeability and permittivity, along with a refractive index (NZRI) property. The metamaterial characteristics were examined through the material wave propagation in two main directions at y and x-axis. For wave propagation at the y-axis, it demonstrates mu-near-zero (MNZ) with more than 6 GHz bandwidth, near-zero refractive index (NZRI), and epsilon-near-zero (ENZ) properties. However, it indicates a wide negative range of single mu metamaterial (MNG) from 27.6 to 28.9 GHz frequency span at x-axis wave propagation. A single antenna with $3\times 3$ metamaterial unit cells is proposed to operate at a frequency band (24 – 30) GHz. Furthermore, MIMO antenna with only 4 mm space between antenna elements provides high isolation of more than 24 dB. The measured results show that the MIMO antenna is satisfied with 6 GHz bandwidth, and maximum peak gain of 12.4 dBi. In addition to that, the proposed MIMO antenna loaded with MTM has also shown good performances with high diversity gain (DG > 9.99), envelope correlation coefficient (ECC) lower than 0.0013, channel capacity loss (CCL) < 0.42, total active reflection coefficient (TARC) <−7 dB, total efficiencies of higher than 98%, with an overall antenna size of 52 mm $\times23$ mm.
A New Hybrid Scheme Of FEBI And PO For Analyzing Inhomogeneous Objects With PEC Plate

A new hybrid scheme of finite element boundary integral (FEBI) method and physical optics (PO) is presented for analyzing scattering problems of inhomogeneous structures above large-scale PEC plate. In each iterative step, the excitation is updated with solutions in FEBI and PO regions of preceding one step and two problems in FEBI and PO regions with latest excitation can be solved in parallel. In addition, the iteration steps can be reduced compared with iterative FEBI-PO. Therefore, the new scheme can sufficiently make use of computational resource and effectively reduce the simulation time cost. Validity and efficiency of the hybrid scheme are also displayed with some numerical results.