Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a pressure sensitive mat using proximity sensors for vital sign monitoring

This paper presents the concept, design and development of a novel flexible pressure sensitive mat using infrared proximity sensors and a compressible material layer. The system consists of sensors arranged in a matrix, on a flexible printed circuit board, covered with an elastic layer of silicone rubber including a cavity for each individual sensor. Finite element simulations suggest a linear deformation with increasing applied pressure. Experiments on a prototype implementation confirm these results and illustrate a linear response with low noise. Finally, it is demonstrated that the system is able to recover breathing rate, indicating its suitability for patient monitoring applications.
Application of a dual arm robot for automated sample preparation for cholesterol determination in biliary stent incrustations

Automation and robotics are increasingly being used in the field of life science due to its various advantages such as minimizing human effort, efficient utilization of time, standardization of processes and low rates of contamination. Dual arm robots, with their multiple degrees of freedom and increased flexibility, allow them to be used for a wide number of applications making it possible to automate complicated tasks such as pipetting or handling of multiple labware simultaneously. The Yaskawa CSDA10F dual arm robot was used to automate a sample preparation process for measuring cholesterol present in bile samples. The robot prepared calibration samples with four different concentrations and multiple identical samples with a known concentration in order to test the repeatability and accuracy of the automated method. Prepared samples were fed to the gas chromatography module where they were analyzed and cholesterol levels were measured. This paper gives an overview of the robotic system and describes the automated sample preparation process in detail. Comparative analysis of the measurements between samples prepared manually and those prepared robotically are also included, thus validating the method.
Data storage in smart grid systems

Transition from classical networks to smart grid (SGs) is important both in terms of performance and failure intervention. When this transition is made, the existing network structure must be transformed into a structure suitable for the SGs. The transformation in the physical structure is also required to make the collected data a single centered. One of the most important factors that will improve performance in reading and writing data in systems used in SGs or power plants is the database system. However, when considering the size of the existing grids, the multiplicity of collected data and the speed at which data is read and written are major problems. In order to solve this problem, wind energy based smart grid scenario has been established. In the created system, relational and non-relational databases are discussed. In order to test the databases, different scenarios have been applied in the developed system and the most suitable database structure has been tried to be determined.
Multiple Constrained  $\ell_2$ -Norm Minimization Algorithm for Adaptive Beamforming

This paper presents an effective adaptive beamforming method based on an <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula>-norm minimization problem with multiple constraints, named as MC-<inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula>-M algorithm. In the proposed method, the output power minimization problem can be formulated as an <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula>-norm minimization problem of the weight vector, with multiple constraints including distortionless response constraint and zero-forcing response constraints toward the desired signal and the interference signals, respectively. Furthermore, via an interference subspace estimation, the presented <inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula>-norm minimization problem can be rewritten as an equivalent constrained convex optimization problem of the weight vector, which is readily to be solved. Finally, the closed-form solution of the proposed optimization problem is given. Compared with the previous works, the proposed MC-<inline-formula> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula>-M approach provides better beamforming performance, for example: 1) it reduces the sensitivity to the variation of signal-to-noise ratio (SNR) so that it has better output array gain, even if in the large dynamic SNR changing circumstances; 2) the better sidelobe and nulling levels are maintained, since it weakens the component of the desired signal in the covariance matrix of the array snapshot vector by a new defined blocking matrix; and 3) it can not only effectively suppress the interference signals but also ensure that the response of the desired signal is distortionless. Simulation results demonstrate the efficiency of the presented approach.
Hybrid Energy Sharing for Smart Building Cluster With CHP System and PV Prosumers: A Coalitional Game Approach

In order to facilitate the energy sharing in Smart Building Cluster, a hybrid energy sharing framework with combined heat and power (CHP) system and photovaltic (PV) prosumers is proposed. A coalitional game model with a utility function that captures the benefit of coalition is built, in which CHP is a thermal and electrical energy producer, and PV prosumers act as role-exchangeable energy sellers or buyers. To minimize the operation cost of the coalition, the demand response is integrated with the coalitional game, in which prosumers can adjust their flexible thermal and electrical loads simultaneously. To support the coalitional game, a two-level reward allocation scheme is designed for fairness of participants and reduction of computational complexity. Moreover, a distributed optimal algorithm based on alternating direction method of multipliers is used to solve the hybrid energy sharing problem. Finally, the effectiveness of the method is verified via a practical case. Scheduling results show that the energy consumption behaviors with both heat and power can be adjusted by prosumers to minimize the coalitional cost, including a heat-power interactive characteristic in particular. The costs of prosumers are reduced while the profit of CHP is improved.
A testing approach for a configurable RMPI-based Analog-to-Information Converter

Compressive Sensing (CS) has recently received a lot of attention due to the fact that it takes advantage from the sparse representation of some natural signals achieving acquisition and energy efficient by using Analog-to-Information Converter (AIC). In short, an AIC receives an analog input signal and outputs a digitized and compressed of this signal. Even though there are several AIC architectures, there is still a lack of testing approaches for these converters. In this paper, a testing method is adapted and applied to evaluate a physical implementation of a configurable architecture AIC based on the Random Modulation Pre-Integrator (RMPI). By means of this testing method, it can be obtained the Signal-to-Noise and Distortion Ratio (SINAD) directly from the AIC output without using signal reconstruction algorithm. This test method has been validated for this architecture by simulations and by means of experimentation in hardware. Results show that some features can be extracted by means of SINAD, such as the best range of input signal amplitude to be used and which configurable parameters best fit the converter.
Direct-Detection PDM-OFDM RoF System for 60-GHz 2×2 MIMO Wireless Transmission Without Polarization Tracking

This work experimentally demonstrates a 60-GHz direct-detection polarization division multiplexing and orthogonal frequency division multiplexing (PDM-OFDM) system that does not require polarization tracking. The frequency offset of un-modulated optical carriers and a novel training symbol design enable the estimation of optical polarization mixing and wireless multi-input-multi-output (MIMO) channels for signal demodulation. We also adopted an empty subcarrier to mediate noise enhancement. Simulation and experiment results both demonstrate the feasibility of this approach. With pre-compensation, we achieved a BER of 1 × 10−3 under all polarization states over 10-km fiber and 3-m wireless MIMO transmission. The maximal allowable transmission data rate of up to 52 Gb at the worst case of states of polarization can be achieved.
Performance Metric on the Best Achievable Accuracy for Hybrid TOA/AOA Target Localization

This letter proposes a general closed-form formula of the Cramer–Rao lower bound (CRLB) for hybrid time-of-arrival/angle-of-arrival (TOA/AOA) target localization. The necessary and sufficient conditions on the existences of the CRLBs for hybrid TOA/AOA, TOA, and AOA schemes are all presented. For a given localization scenario, the best achievable accuracy of the hybrid TOA/AOA scheme has been theoretically proved to be better than that of any one of the TOA and AOA schemes. With this hybrid scheme, the stringent time synchronization requirement of the localization system can be relaxed and the number of receivers can be reduced compared to the TOA scheme.
The Comparison between FTF-VO and MF-VO for High Accuracy Mobile Robot Localization

In this paper, we design and develop two SLAM (simultaneous localization and mapping) methods: FTF-VO (frame-to-frame visual odometry) and MF-VO (multi-frame visual odometry) for robotic localization based on stereo cameras. In both VO methods, we proposed an ARFM-based 3D reconstruction method and calculated the position of the camera using PNP, after that BA (bundle adjustment) can be used to optimize the final camera position. We tested FTF-VO and MF-VO using KITTI dataset, with BA and without BA respectively. The result demonstrates that MF-VO with BA has the highest precision so that it is suitable for the scenarios requiring high precision. Compared with FTF-VO with BA, MF-VO achieves better localization precision while requires less running time, so FTF-VO with BA is recommended for medium localization precision. FTF-VO is the best choice for low precision.
A Fast Matrix Majorization-Projection Method for Penalized Stress Minimization With Box Constraints

Kruskal's stress minimization, though nonconvex and nonsmooth, has been a major computational model for dissimilarity data in multidimensional scaling. Semidefinite programming (SDP) relaxation (by dropping the rank constraint) would lead to a high number of SDP cone constraints. This has rendered the SDP approach computationally challenging even for problems of small size. In this paper, we reformulate the stress optimization as an Euclidean distance matrix (EDM) optimization with box constraints. A key element in our approach is the conditional positive-semidefinite cone with rank cut. Although nonconvex, this geometric object allows a fast computation of the projection onto it, and it naturally leads to a majorization-minimization algorithm with the minimization step having a closed-form solution. Moreover, we prove that our EDM optimization follows a continuously differentiable path, which greatly facilitated the analysis of the convergence to a stationary point. The superior performance of the proposed algorithm is demonstrated against some of the state-of-the-art solvers in the field of sensor network localization and molecular conformation.
Cognition-Inspired 5G Cellular Networks: A Review and the Road Ahead

Despite the evolution of cellular networks, spectrum scarcity and the lack of intelligent and autonomous capabilities remain a cause for concern. These problems have resulted in low network capacity, high signaling overhead, inefficient data forwarding, and low scalability, which are expected to persist as the stumbling blocks to deploy, support and scale next-generation applications, including smart city and virtual reality. Fifth-generation (5G) cellular networking, along with its salient operational characteristics—including the cognitive and cooperative capabilities, network virtualization, and traffic offload—can address these limitations to cater to future scenarios characterized by highly heterogeneous, ultra-dense, and highly variable environments. Cognitive radio (CR) and cognition cycle (CC) are key enabling technologies for 5G. CR enables nodes to explore and use underutilized licensed channels; while CC has been embedded in CR nodes to learn new knowledge and adapt to network dynamics. CR and CC have brought advantages to a cognition-inspired 5G cellular network, including addressing the spectrum scarcity problem, promoting interoperation among heterogeneous entities, and providing intelligence and autonomous capabilities to support 5G core operations, such as smart beamforming. In this paper, we present the attributes of 5G and existing state of the art focusing on how CR and CC have been adopted in 5G to provide spectral efficiency, energy efficiency, improved quality of service and experience, and cost efficiency. This main contribution of this paper is to complement recent work by focusing on the networking aspect of CR and CC applied to 5G due to the urgent need to investigate, as well as to further enhance, CR and CC as core mechanisms to support 5G. This paper is aspired to establish a foundation and to spark new research interest in this topic. Open research opportunities and platform implementation are also presented to stimulate new research initiatives in this exciting area.
A Cooperation Strategy Based on Bargaining Game for Fair User-Centric Clustering in Cloud-RAN

Coordinated multi-point (CoMP) has emerged as a promising concept for improving the overall network throughput, where each user is attached with a group of radio heads called a cluster. In this letter, a user-centric clustering scheme in cloud radio access network is proposed to consider the fairness between users while choosing the serving clusters. The CoMP clustering strategy is formulated as a cooperative bargaining game problem and resolved using Nash bargaining solution (NBS). First, the users are grouped into coalitions using a Hungarian method. Then, a two-band partition algorithm is developed for the two users in each coalition to bargain their attached radio heads so that the NBS utility is maximized. Simulation results show that the proposed algorithm can achieve a considerable rate gain and fairness improvements compared to the existing schemes.
Bispectrum-based gecture recognition technique

A new technique for recognition of human gestures modulating electromagnetic field of high-frequency (HF) radio emission is suggested. Proposed technique is based on the extraction of informative features by estimation the bispectrum of the envelope of a HF radio signal the parameters of which change under the influence of gestures in the interference field of radio emission in a close indoor environment. Results of experimental study performed by measuring device and using developed algorithm and designed software are represented. Robustness of bispectrum-based informative features is experimentally studied. The results obtained for number of test gestures indicate that proposed approach does not depend on the amplitude variations of the signals and signal delay time. It has been demonstrated that suggested technique is considerably sensitive to the signal waveform variations, and it does not depend on the duration of processed signal.
Constraint-Based Pattern Retargeting for Reducing Localized Power Activity During Testing

Highly compact as well as compressed test pattern generation may result in the aggregation of high power activity in specific areas on a manufactured circuit during testing. These hotspots can lead to electromigration and IR-drop in local blocks of the chip resulting in wrong test results. This is due to the circumstance that the effect of localized high switching activity is not precisely taken into consideration during ATPG and pattern simulation. Discarding such patterns may result in test coverage loss. Low power test generation methods typically reduce the switching activity globally across the pattern and not locally in specific areas. Additionally, these methods typically increase the test data volume as well as the testing time. In this paper, a test pattern retargeting methodology is proposed which takes pattern- specific, dynamically identified hotspots into account. Critical patterns and their corresponding critical regions are identified. Based on this data, constraints are used for pattern retargeting preventing the previously identified local hotspots. In contrast to previous methods, the proposed retargeting technique ensures a high test coverage without a large pattern inflation.
Real-Time Deformation Sensing for Flexible Manipulators With Bending and Twisting

Wire-driven flexible manipulators are increasingly used in medical applications, such as minimally invasive surgeries, due to its ability to operate in confined space by deforming the backbone. Existing flexible manipulators are mostly controlled in open loop, as there lacks a proper real-time deformation sensing method. The deformation of a flexible manipulator includes both the backbone shape bending and backbone twist. Current sensing methods for flexible manipulators only consider the shape and neglect the twist. However, the twist is of equal importance as it affects the end-effector orientation. In this paper, we propose a real-time deformation sensing and reconstruction method for flexible manipulators. By employing the real-time position and orientation information of part of the backbone, e.g., the base and tip, we can estimate the deformation of the flexible manipulator with adequate accuracy. What’s more, a vision-based high-precision off-line shape and posture sensing method for twisted flexible manipulators is also proposed to verify the proposed electromagnetic real-time shape and posture sensing method. Results show that the proposed method could well sense the deformation of the backbone with average position error root-mean-square (rms) 1.9 mm and average twist error rms 0.61°.
Directional Modulation With Cooperative Receivers

Directional modulation (DM) refers to a class of efficient techniques for enhancing the physical layer security of wireless communications. However, conventional DM approaches cannot guarantee the transmission security when eavesdroppers are located close to the legitimate user. In this paper, to mitigate this problem, a new DM scheme with cooperative receivers (DM-CR) is proposed for secrecy communications. Specifically, the legitimate user employs multiple cooperative receivers, and a predefined distortion factor set is introduced to the beamforming vector. The proposed DM-CR scheme is able to guarantee transmission security regardless of the direction or location of the eavesdroppers. Furthermore, the average bit error probability of the proposed DM-CR scheme is investigated. Both analytical and simulation results are presented to show that the proposed DM-CR scheme significantly outperforms the benchmark in terms of the bit error rate, so as to provide secure transmission toward future wireless communications.
Cloud system ArcGIS online as a managerial decision-making tool in agricultural production

Agriculture has always contributed to the development and accelerated implementation of geoinformation technologies in the section of national economy and the economy of the country as a whole. Official release of the ESRI product — ArcGIS Online opens up new opportunities for “cloud” GIS infrastructure for agribusiness in the area of management decision-making. The publication of vector and raster materials in the form of data services is called to increase the efficiency of making managerial decisions, which in turn should provide a solution to a wide range of tasks for scientifically-based commodity agricultural production implementing.
Identification the differences of phase and group birefringence of different polarization-maintaining fibers with Brillouin dynamic grating and Sagnac interferometer

Measurement of the birefringence of polarization-maintaining fiber (PMF) make difference for fiber optical gyro (FOG) improvement. In this work, the phase birefringence and group birefringence of four kinds of different commercial gyro-used PMFs are theoretically calculated by finite element method and experimentally measured by Brillouin dynamic grating (BDG) and Sagnac method. The difference between the phase birefringence and group birefringence in shape-induced PMFs are confirmed by comparing the calculation and experiment results; and BDG is proved to be a distributed measurement of phase birefringence, while Sagnac is for group birefringence average measurement. From this work, it can be found that when concerning the polarization properties of FOGs, BDG should be used to measure the phase birefringence rather than the Sagnac and group birefringence.
A Novel Signal Acquisition System for Wearable Respiratory Monitoring

In this paper, a novel human respiration detection method based on angular velocity is proposed for continuous acquisition and analysis of respiratory signals. Using patient-specific information derived from a wearable sensor, our proposed system is capable of monitoring human respiration and assists in identifying potential respiratory disorders by relying on pre-defined simple procedures. Specifically, our signal verification platform is equipped with a carbon dioxide concentration detection device in order to acquire a synchronous signal. In addition, a median filter method is used to extract the respiratory waveform from the original signal. The obtained angular velocity waveform pertaining to human respiration is then compared to the respiratory carbon dioxide concentration waveform, and the validity of our designated parameters is verified. The test results demonstrate that our implemented system is appropriate for unobtrusive respiratory signals acquisition and analysis; therefore, it can be considered a potential alternative for physiological monitoring and respiratory disorders screening.
Light-Weight Security and Data Provenance for Multi-Hop Internet of Things

Due to the limited resources and scalability, the security protocols for the Internet of Things (IoT) need to be light-weighted. The cryptographic solutions are not feasible to apply on small and low-energy devices of IoT because of their energy and space limitations. In this paper, a light-weight protocol to secure the data and achieving data provenance is presented for the multi-hop IoT network. The Received Signal Strength Indicator (RSSI) of communicating IoT nodes are used to generate the link fingerprints. The link fingerprints are matched at the server to compute the correlation coefficient. Higher the value of correlation coefficient, higher the percentage of the secured data transfers. Lower value gives the detection of adversarial node in between a specific link. Data provenance has also been achieved by comparison of packet header with all the available link fingerprints at the server. The time complexity is computed at the node and server level, which is $O(1)$ . The energy dissipation is calculated for the IoT nodes and overall network. The results show that the energy consumption of the system presented in this paper is 52–53 mJ for each IoT node and 313.626 mJ for the entire network. The RSSI values are taken in real time from MICAz motes and simulations are performed on MATLAB for adversarial node detection, data provenance, and time-complexity. Experimental results show that up to 97% correlation is achieved when no adversarial node is present in the IoT network.