Miracle-dz/UniFAR-FTUs huggingface dataset:
2+2 Switched-Current Delta-Sigma Modulator with Digital Noise Cancellation Circuit

This paper proposes a 2+2 switched-current (SI) multi-stage noise-shaping (MASH) delta-sigma modulator (DSM) with a digital noise-cancellation circuit (DNCC) by using a TSMC 0.18 μm 1P6M CMOS process. In view of area-efficiency, the current-mode sample-and-hold circuit (S/H) is designed to reduce the chip area considerably. It plays a vital role in the performance of the DSM. Note that the input impedance of the modified current-mode feedback memory cell (FMC) is decreased by [2 + (g'm3/gml-1) x A] times relative to a traditional FMC and the input current is being processed more quickly. However, it suffers the transmitted error particularly for small input currents. The MASH architecture inherited a superior signal-to-noise-and-distortion ratio (SNDR) by using an effective digital noise cancellation circuit (DNCC) and a low-pass filter varied from 10 Hz to 20 kHz. The designed current-mode DNCC is composed of six delay components using master-slave D flip-flop and a logic circuit using the karnaugh map. Post-layout simulations reveal that the simulated SNDR was 90.4 dB and the ENOB was 14.73 bits. The designed IC consumes 18.19 mW at a chip area of 0.13 mm2 and a simulated FoM of 24.5 pJ/conv. The advantages of our modulator are its small chip area and high processing speed at all input currents.
Improving Pathfinder Algorithm Perfomance for FPGA Routing

The paper presents some algorithmic improvements to accelerate the routing stage for FPGA. One of the main advantages of FPGA is the high development speed; thereby the importance of efficient computer-aided design tools for modern FPGA is hard to overestimate. The placement and routing stages are crucial for the FPGA performance, and meanwhile they take up a significant part of the design time. The investigated base routing algorithm is the modified Pathfinder for the mixed routing resources graph. Pathfinder is a popular iterative negotiation-based router acting on the principle of eliminating nets congestions. Due to the proposed algorithmic improvements, such as ignoring the uncongested nets while rip-up and rerouting stage, and some other, the summary routing time is reduced significantly. The observed average time decrease is from 1.3X to 2.6X depending on the FPGA architecture. The benchmarks ISCAS’85&89, LGSynth’89 and several real industrial circuits were used for testing.
Time Difference of Arrival Based Indoor Positioning System Using Visible Light Communication

Visible Light Communication (VLC) is a novel optical wireless communication technology which uses Light Emitting Diodes (LEDs) and Photodiodes for coherent detection and very-high-data rate data communication system. The stringent Line of Sight (LoS) requirement in VLC makes it very suitable for Indoor Positioning System (IPS), to be used for autonomous and smart city infrastructure. The current work aims to implement a real time IPS system using VLC link in Network Simulator (NS-3). The VLC module is implemented by modelling real-time attributes of LEDs, optical channel, and the photodiodes. The localization is carried out using trilateration schemes which measures the Received Signal Strength Indication (RSSI) and the Time Difference of Arrival (TDoA) for position estimation of the target. The project is further extended to obtain a comparative analysis between VLC link and other existing technology, Wi-Fi, as far as positioning accuracy and other important performance metrics are concerned. The simulation results show significant improvement for the VLC link over the Wi-Fi link specially for the TDoA scheme accompanied by increasing number of beacon nodes.
Optimizing Endurance of an Autonomous Vertical Profiler (AVP)

Autonomous Vertical Profiler (AVP) being one of the Unmanned Underwater Vehicles has been primarily designed and developed to observe the properties of the ocean such as variation of salinity, temperature, conductivity or dissolved oxygen along a predetermined depth profile. In the last decade, substantial modifications have been introduced in the design of the AVP to enhance its endurance and durability. This paper illustrates a novel strategy that can improve and control the performance of AVP in terms of endurance through controller modeling. Taking advantage of pseudo-dynamic models of the vehicle, environment, and the electronic systems, better endurance control has been achieved. The maximum number of dives and the power consumption is controlled with the aid of integrated pseudo-mathematical models and neural networks. The endurance controller has been simulated in MATLAB.
Identification and Classification of Industrial Elements using Artificial Intelligence and Image Processing Techniques

This paper presents a model which identifies various industrial elements with the help of Image Classification algorithms and the concepts of image processing. The proposed model provides a solution where the industry is hazardous and wants to evolve in automation field to make the process safer and easier. The performance analysis of popular Convolutional Neural Network is implemented in order to identify the objects of a plant. The concepts of Deep Learning Algorithms are applied with the help of Tensor Flow software and Keras API. As first step the CNN architecture was decided manually. After analyzing the results, the next step is implementation of Transfer Learning techniques to improve the accuracy. Some of the most powerful Transfer Learning algorithms are being used like ResNet, Inception, Mobile Net, DenseNet, VGG16 and Xception etc. Each of these algorithms is evaluated and the output accuracy is tested. The dataset is collected by taking problem statement into consideration. The models are verified and the experimental results exhibited an accuracy of almost 90%.
Resource Allocation in Device to Device Communication using Dynamic Programming

Device to device communication is an emerging technology and considered as a significant part of fifth generation wireless networks. It provides better communication in terms of improvement in system throughput and resource utility with shortened communication delay. Even though device to device communication improves energy and spectral efficiency by skipping the direct connection through base station, it suffers with some technical challenges. This paper summarizes the implementation challenges of spectrum allocation, and mainly focuses on resource allocation and spectrum sharing using dynamic programming approach. In addition, both single and priority based queuing models are employed to satisfy the user request. Finally, performance of the proposed approach is compared with greedy, brute force, successive and fractional approaches.
Power Losses Calculation in Wind Power Plant based on a Vector-Controlled Permanent Magnet Synchronous Generator

mathematical model of a wind turbine is investigated, which includes mathematical descriptions of a horizontal axis small- scale power wind turbine with permanent magnet synchronous generator with vector control and a three-phase rectifier connected to a stand-alone voltage source inverter. The maximum power point tracking of the wind turbine, which calculates the optimal rotation turbine speed for each wind speed is used. Vector control system provides the calculated optimal rotation turbine speed. The mechanical losses in the gearbox and in the synchronous machine are determined, as the iron losses and copper losses in the permanent magnet synchronous machine and commutation losses (switching and conduction losses) in stand- alone voltage source inverter are taken into account.
High Order PSK Modulation in Massive MIMO Systems With 1-Bit ADCs

Massive multiple-input multiple-output (MIMO) systems with 1-bit analog-to-digital converters (ADCs) are promising to reduce the energy consumption. However, the serious quantization error caused by 1-bit ADCs will potentially limit the feasibility of high order modulations. This paper focuses on the analysis of the high order phase-shift keying (PSK) signal transmission in the 1-bit ADC massive MIMO system. Firstly, assuming ideal channel estimation and a single mobile station (MS), we theoretically prove that with an asymptotically large number of antennas at the base station, PSK signals with arbitrary modulation order can be recovered in the 1-bit ADC massive MIMO system. Secondly, we analyze the impact of pilot based channel estimation on the recovery of the high order PSK signals, which leads to a periodic asymptotic detection phase error (ADPE) at high signal to noise ratio (SNR). Furthermore, we also propose to optimize the pilot sequence for minimizing the cumulative absolute ADPE. Finally, the analysis is extended to the multi-MS case and the performance with different pilot patterns is discussed. Simulation results validate our analysis and show that using our proposed optimized pilot sequence can significantly improve the detection performance for both single-MS and multi-MS 1-bit ADC massive MIMO systems.
Device Scaling roadmap and its implications for Logic and Analog platform

In the 22nm node, FinFET has been introduced to continue CMOS Logic scaling. The continuous device shrinking needed to reach node 3nm and beyond bring us into the post FinFET era, which requires new device architectures. In this paper we review the device evolution to vertically stacked Nanosheets, Forksheet, and CFET in conjunction with buried power rails and wrap around contact. The impact of variability at scaled dimensions and the requirement for a complete CMOS platform including I/O are discussed. We then review how these elements affect the analog/RF performance of advanced devices in a holistic view.
A Dynamic 3-D Wideband GBSM for Cooperative Massive MIMO Channels in Intelligent High-Speed Railway Communication Systems

Coordinated multipoint (CoMP) and massive multiple-input multiple-output (mMIMO) are two of promising technologies in future intelligent high-speed railway (HSR) communication systems, whose performance is fundamentally determined by the characteristics of cooperative mMIMO channels. This article proposes a dynamic three-dimensional (3-D) wideband geometry-based stochastic model (GBSM) for HSR cooperative mMIMO channels. The proposed GBSM employs a sphere model and two elliptic-cylinder models to describe common and uncommon clusters for different links, and integrates the dynamic cluster evolution in both time and array domains. Statistical properties of the cooperative mMIMO model are investigated, such as multi-link spatial cross-correlation function and sum rate capacity. A corresponding dynamic 3-D wideband simulation model for the HSR cooperative mMIMO channel is also proposed. Finally, numerical results of the statistical properties are analyzed, and the proposed model is validated by realistic HSR channel measurement data, in terms of dual-link spatial cross-correlation and channel capacity. This model is more practical and will be helpful for facilitating the design and performance evaluation of future intelligent HSR communication systems.
Antarctic Deep Field Deployments and Design of the Icefin ROV

Icefin is a custom designed remotely or autonomously operated vehicle (ROV/AUV) for sub-ice deployments in polar environments with a primary focus on modularity for deploying multiple payloads throughout a season to characterize the under-ice environment. Over the past six years the vehicle has undergone numerous design improvements to become a stable and robust platform. The current primary vehicle design was proven during the 2018 field campaign. With the success of that season, a second vehicle was built to accommodate two field campaigns in austral summer 2019–2020, wherein the vehicle dual Icefin vehicles were deployed through ~600m hot water drilled boreholes to investigate the grounding zones of Thwaites Glacier and the Ross Ice Shelf. Presented here is an overview of the engineering design updates that were incorporated from the 2017 season. Also presented are results and lessons learned from the 2019 deployments.
Rapid screening of agrochemicals by paper spray ionization and leaf spray mass spectrometry: which technique is more appropriate?

Paper Spray Ionization (PSI) and Leaf Spray (LS) are two new sources of ambient ionization mass spectrometry. In PSI, the sample is deposited on a triangular paper and ions are generated by applying a high voltage on a moistened paper with an organic protic solvent. The LS technique is a variant of PSI, where the paper is now substituted by the matrix itself to be studied, a triangular leaf. Both methods allow rapid and inexpensive analyses compared to traditional API techniques. In this work, both sources were applied to identify and quantify agrochemicals which are widely used in fruits and vegetables. The analytical performance of PS and LS techniques was evaluated from the construction of calibration curves in the positive ionization mode (PSI(+) and LS(+)) using atrazine, diuron and methomyl agrochemicals in arugula, basil, cabbage, lettuce and kale vegetable samples. The concentration ranged from 10 to 1000 ppb. Atrazine and diuron were used as internal standards, not requiring the use of deuterated standards. Both analytical methods demonstrated similar performance for the three agrochemicals with R2 > 0.99, and values of LOD and LOQ ranging from 1.23 to 25.00 and 4.12 to 83.33 ppb for PSI(+)-MS and from 0.03 to 36.00 and 0.11–120.00 ppb for LS(+)-MS. In almost all cases, these values were lower than the established MRLs (Maximum Residue Limits), which are 250 ppb for atrazine and 100 ppb for diuron and methomyl.
A research on the Taipei MRT passenger traffic prediction model

Tour traffic prediction is an important reference for the transport planning organization of capacity and transportation planning as well as for the new-built and expansion of the transport device made in accordance with the basic needs. The common ways to forecast the tour traffic demand are regression analysis, econometric modeling, time series model, artificial neural network, and gray theory from limited literature reviews. In this study, artificial neural network is used as a tool to build prediction models. This study further uses regression analysis to find the correlations between two or more variables and their associated directions and strength, and uses the regression function to predict future changes. Finally, the study makes the result comparison and assessment of the regression analysis and artificial neural network methods. The artificial neural network has more accurate prediction value than regression analysis. The study results can provide useful references for transportation organizations to develop the strategy of business operation.
Prototyping CRISP: A Causal Relation and Inference Search Platform applied to Colorectal Cancer Data

We introduce CRISP, a Causal Research and Inference Search Platform. It is designed to assist biological and medical research by applying a variety of causal discovery methods to heterogeneous and high-dimensional observational data. CRISP aims to identify a small set of input variables which are most likely to have a causal effect on a target variable. The output of CRISP, thus, highlights the most promising candidates for further targeted research. We illustrate the utility of CRISP with a case study in oncology, using a multi-omic colorectal cancer data set to identify causal drivers differentiating two subtypes of colorectal cancer.
Harmonic Suppression and Torque Ripple Reduction of a High-Speed Permanent Magnet Spindle Motor

In this paper, the method of harmonic suppression and torque ripple reduction is studied for the structure characteristics and design requirements of a high-speed permanent magnet spindle motor. According to the magnetic circuit analysis and calculation theory of the permanent magnet synchronous motor (PMSM), magnetic reluctance slots are arranged at the two ends of the rotor yoke to change the external magnetic field distribution of the permanent magnet in order to optimize the air gap magnetic density waveform. To obtain air gap flux density waveforms that are close to a sinusoidal distribution and not destroy the permanent magnet under the pre-pressure, several reluctance slots with different widths and depths arranged at two ends of the rotor yoke are used to achieve the sinusoidal distribution. Based on the selection of the pole-slot proportion, skewed-slot and a decrease in the end effect, the effects of the harmonic suppression of the harmonic electromotive force (EMF) and the reduction in the torque ripple are discussed. It is concluded that the additional loss and torque ripple of the high-speed permanent magnet spindle motor can be reduced through a reasonable design.
Fuzzy Adaptive Impedance Control for Deep-Sea Hydraulic Manipulator Grasping Under Uncertainties

This paper presents a position-based fuzzy adaptive variable impedance control architecture for an end-effector of deep-sea hydraulic manipulators. To obtain the contact force information in the deep-sea environment, pressure sensors are used to take the place of force sensors on deep-sea hydraulic manipulators. The stiffness and position information of underwater objects are can not be known in most cases, in order to get the reference trajectory, a method of on-line parameters estimation by the Lyapunov equation is proposed. To deal with the stiffness variation of objects and improve the robustness, a position-based fuzzy adaptive impedance controller is designed. Simulation and experiment studies are carried out to evaluate the effectiveness of the proposed control method applied for the end-effector of deep-sea hydraulic manipulators.
Modeling the Formation of a Multi-Agent System as a System of Points of a Rigid Body

This paper discusses what is formation control in a multi-agent system. The concept of a multi-agent system, its advantages and problems are given. The difference between centralized and decentralized management is explained. The concept of management of formations is given. The main tasks of the formation and the used models of agent management were described. A mathematical model with one integrator is considered.
Exploiting Distributed Tactile Sensors to Drive a Robot Arm Through Obstacles

Robots operating in unstructured environments must be capable of safely handling unexpected collisions with objects existing in the surrounding area, possibly without stopping the task execution. This letter proposes a tactile feedback control law allowing the robot to apply bounded contact forces in reaction to physical collisions while performing a task. As a use-case scenario, the problem of driving a robot arm through obstacles to reach a known target position in the space is considered. Intensities and locations of multiple and simultaneous contacts between the robot and the environment are detected using large area tactile sensors covering the robot body. It will be shown that the robot is capable of controlling the end-effector position and of reacting to unexpected collisions by regulating the interaction forces applied to the environment. The method has been validated on a Baxter robot partially covered with 3495 distributed tactile elements, operating in an unknown environment.
Two-Dimensional Channel Parameter Estimation for Millimeter-Wave Systems Using Butler Matrices

In this paper, a novel two-dimensional parameter estimation method is proposed for frequency-selective millimeter-wave (mmWave) channels by probing a limited number of Kronecker products of discrete Fourier transform (DFT) beams, which are efficiently implemented in the analog domain by a novel combination of Butler matrices. The proposed strategy firstly estimates the channel parameters by using a modified parameter estimation via interpolation based on a DFT grid (PREIDG) algorithm. In a second step, high-resolution channel parameter estimation is achieved even in the low signal-to-noise-ratio (SNR) using the space-alternating generalized expectation-maximization (SAGE) algorithm. The proposed modified PREIDG algorithm outperforms state-of-the-art methods, e.g., the auxiliary beam pair (ABP) method while the SAGE algorithm achieves the derived Cramér-Rao lower bound (CRLB). Numerical results demonstrate that excellent estimation performance can be achieved for angle of departure (AoD) azimuth and elevation with addition to delay and complex path gain of each path even in the low SNR regime.
Synthesis of Centralized Adaptive Excitation Control System by Reinforcement Learning Approach

The paper is aimed at the study of existing approaches to creation of centralized PSS based on artificial neural networks, as well as the synthesis of such control systems and assessment of their effectiveness in four-machine Kundur scheme. Existing approaches applying adaptive and neural net the technologies in creating control systems for large power interconnections are analyzed. Test data sets for training a neural network using traditional excitation regulators (AVR+PSS) are created in a four-machine Kundur power system based on optimal parameters obtained for various operating conditions. Local and global adaptive control systems based on artificial neural networks and reinforcement learning are obtained and effectiveness of proposed approaches is evaluated by calculating transients with various disturbances in power system.
Neuro-Fuzzy and Particle Swarm optimization based Hybrid Approach for Stock Price Forecasting

This study, investigates the applicability of adaptive network based fuzzy inference system (ANFIS) and discrete particle swarm optimization (DPSO) based hybrid approach for forecasting the close price of two major stock indices of India viz. Nifty 50 and Sensex using optimum number of technical indicators. The primary focus of this work is to create a systematic approach based on swarm intelligence based neuro-fuzzy model where DPSO attempts to search automatically the optimum number of features to overcome overfitting problem and subtractive clustering determine the compact rule-base of neuro-fuzzy system. Experimental results reveal that the proposed hybrid model shows the superior forecasting performance in comparison with the model uses complete set of features.
Virtual reality and feeling of falling: a physiological wearable tool for virtual reality sickness

Virtual reality (VR) approaches have recently found promising applications in the field of rehabilitation. However, prolonged immersion in a VR environment is known to occasionally cause sickness and discomfort, in both patients and healthy subjects. In the present study we assessed changes in Electrodermal Activity (EDA) as an estimate of Sympathetic Nervous System (SNS) activity in 16 healthy subjects performing 3 different tasks in a VR environment. Our aim is to collect preliminary evidence on the use of SNS activity as a potential marker to detect discomfort during VR experiences. SNS activity was quantified using a wearable device during the different conditions presented in the experiment. Results showed that EDA, more specifically Maximal Phasic activity and skin conductance differentiate among tasks, thus quantifying the SNS activity load of a task. In conclusion SNS activity markers derived from EDA have the potential to be used as a biomarker for VR experiences. Trial registration ID: NCT03425877