Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT Sensor Data Acquisition and Storage System Using Raspberry Pi and Apache Cassandra

In this paper, we introduce an alternative solution to the many existing IoT data acquisition and storage systems. We present a self-designed and developed prototype electronic circuit extension for Raspberry Pi development board used for collecting sensor data. There is also presented a Pi4Java API based Java application used for sensor data collection and storage. We set up an Apache Cassandra database cluster, to stores large amounts of sensor data on low-cost servers, providing high availability. In addition, a web application is also presented, that allows different data visualization operations to be performed on the stored data. The presented system is a full IoT data acquisition, storage and visualization solution
Hand Electromyography Circuit and Signals Classification Using Artificial Neural Network

Electromyography (EMG) is the study of electrical activity of muscles signals. This technique can be used for the control of prosthetic for amputees or for medical purposes in muscular disorders. Major challenge faced in this domain is high cost of the devices to control the prosthetic. In addition to the cost of the device, number of parameters used for classification is large for studies in this domain. In this study we propose a low cost circuit for EMG signal extraction. We used 4 channels of proposed EMG circuit to classify 6 different motion that includes individual finger motions and fist motion. Despite being low cost, our circuit provides the signals that can be classified with high accuracies comparable to other studies. For classification, we used artificial neural network with less number of parameters to achieve accuracies comparable to other studies using higher number of parameters. We collected data from 5 healthy subjects using our proposed circuit. Behavior of EMG signal varies from subject to subject depending upon different factors. We used six features from time and frequency domains, gave an accuracy of 98.8% and 96.8% for all combined subjects with two different algorithms and an average accuracy of 99% with standard deviation of 0.6 for all individual subjects.
A Novel Monocular-Based Navigation Approach for UAV Autonomous Transmission-Line Inspection

This paper proposes a unique and robust UAV autonomous navigation approach along one side of overhead transmission lines for inspection. To this end, we establish a perspective model and develop a novel Pan/Tilt monocular-based navigation scheme. Simultaneously, the following three key issues are addressed. First, to locate the effective landmark - transmission tower timely and reliably, we customize a neural network for tower detection and combine it with a fast and smooth tracking. Second, to provide UAV with a robust and precise heading, we detect the transmission lines and compute and optimize their vanishing point. Third, to keep a safe distance from transmission lines, we optimize a homography matrix to restore the parallel nature of transmission lines and perceive the distance variation by a point set registration model. Finally, by the designed UAV platform, we test the whole system in a real-world transmission-line inspection scenario under different weather condition and achieve an encouraging result11https://youtu.be/mZjD5x-kTDM, Our approach provides great flexibility for refined inspection and effectively improves inspection safety.
Roadmap for Design of Surgical Equipment for Safe Surgery Worldwide

Safe and affordable surgery is not accessible for five billion people when they need it. Multiple surgical capacity studies have shown that hospitals in low-and-middle income countries do not have complete coverage of basic surgical equipment such as, theatre lights, anesthesia machines and electro surgical units. Currently, almost all equipment is designed and manufactured with a main focus on the context in high income countries. The context in low-and-middle income countries in which surgical equipment is used, differs from high income countries, especially in terms of financial resources and access to maintenance, spare parts and consumables. The aim of this study is to present a roadmap for design of surgical equipment for worldwide use. The roadmap consists of four phases: before the start of a design project a clear need for certain surgical equipment should be identified (Phase 0). During Phase 1 the context should be researched thoroughly by determining the barriers encountered by patients to surgical care, the structure of the health care system and if the aspects required for safe surgery are in place. In Phase 2 the implementation strategy and design requirements should be determined and in phase 3 prototyping starts in close interaction with local end-users. We believe that designers should strive for design that is of the same quality and complies with the same safety regulations as equipment designed for HICs. In this way user and patient safety can be assured in any setting worldwide. And we advocate for surgical equipment that fits the context optimally and that will be applicable in comparable settings globally.
Flexible Fabric Actuator Realizing 3D Movements Like Human Body Surface for Wearable Devices

A new flexible fabric actuator realizing three dimensional movements for wearable devices is proposed in this paper. Such actuators must be lightweight and highly flexible, producing movements with high degree of freedom to assist/follow human natural motions. Improving the structure and control system of a fabric actuator from the previous research, the flexible fabric actuator with continuous control is developed. This paper presents new configuration of thin artificial muscles on a flexible rubber swath, a continuous control system to control the fabric actuator smoothly, and control methods to realize six basic movements of human body (forward and backward bends, left and right bends, left and right twists)with less number of muscles. The experiment results indicate the possibility that the proposed fabric actuator woven thin McKibben artificial muscles is a viable technology for use in wearable devices.
Grid-Array Rectenna With Wide Angle Coverage for Effectively Harvesting RF Energy of Low Power Density

It is difficult to effectively rectify or convert low power in the circuit stage of harvesters. The high-gain antenna can offer a higher level of power and is beneficial to RF power harvesting in a low power density environment. To extend the narrow beam of the conventional high-gain rectenna, the multiport and multibeam antenna is promising. To pursue a simple configuration and avoid extra beamforming networks, a traveling-wave grid-array antenna (GAA) with two isolated ports and two symmetrically tilted beams is proposed. A prototype is designed and fabricated after a detailed analysis of the GAA with tilted beams and the coplanar stripline-based rectifier. The measured results show that the proposed rectenna is sensitive and effective in a wide-angle range. The harvesting angle range is extended by combining two tilted beams and can be greater than 70°, where the level of dc power exceeds <inline-formula> <tex-math notation="LaTeX">$100~\mu \text{W}$ </tex-math></inline-formula> when the power density is as low as <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{W}$ </tex-math></inline-formula>/cm<sup>2</sup>. A maximum dc output of 3.6–<inline-formula> <tex-math notation="LaTeX">$203.8~\mu \text{W}$ </tex-math></inline-formula> and a maximum RF-to-dc conversion efficiency of 16.3%–45.3% are available at 2.45 GHz, under the condition that the power density ranges from 0.052 to <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{W}$ </tex-math></inline-formula>/cm<sup>2</sup>.
After You: Doorway Negotiation for Human-Robot and Robot-Robot Interaction

We propose and test an autonomous robot behavior for socially-compliant navigation of doorways with both human and robot interlocutors. Building on previous work for “aggressive” interaction between robots to resolve navigation deadlocks in corridors, we demonstrate an “assertive” robot that negotiates right-of-way when faced with a human or other robot. The negotiation is implemented using only motion and common navigation sensors, without explicit message-passing. Our goal is for the correct agent to take priority, as decided both by time-efficiency and as judged subjectively by naive human participants. Our contribution is a practical method for doorway negotiation, and a study of human users' responses to a robot that appears to participate in existing social customs surrounding doors. Our method is evaluated with robot-robot experiments and a human-robot interaction study with nonexpert users.
Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification

In recent studies in hyperspectral imaging, biometrics, and energy analytics, the framework of deep dictionary learning has shown promise. Deep dictionary learning outperforms other traditional deep learning tools when training data are limited; therefore, hyperspectral imaging is one such example that benefits from this framework. Most of the prior studies were based on the unsupervised formulation; and in all cases, the training algorithm was greedy and hence suboptimal. This is the first work that shows how to learn the deep dictionary learning problem in a joint fashion. Moreover, we propose a new discriminative penalty to the said framework. The third contribution of this work is showing how to incorporate stochastic regularization techniques into the deep dictionary learning framework. Experimental results on hyperspectral image classification shows that the proposed technique excels over all state-of-the-art deep and shallow (traditional) learning based methods published in recent times.
Energy Efficient Downlink Resource Allocation for D2D-Assisted Cellular Networks With Mobile Edge Caching

In this paper, a downlink device-to-device (D2D)-assisted cellular networks with mobile edge caching, where most popular video files are independently cached in D2D users and cellular base station (BS), are studied. In the considered system model, each user may obtain the requested video from the cache of BS or/and D2D users surrounding them. According to the different collaborative schemes of BS caching and D2D caching, it can be divided into two different resource allocation schemes. In the hybrid caching transmission scheme, users could adopt the BS caching mode or alternatively the D2D caching mode. In the joint caching transmission scheme, each user may obtain the requested files from the BS server and the adjacent D2D users, simultaneously. By taking the required data rate and the interference constraint into account, we formulate two joint resource allocation problems integrating link selection, channel allocation, and power control to maximize the system energy efficiency (EE). Leveraging on the Dinkelbach method, the EE optimization problems are transformed into mixed-integer nonlinear programming problems and can be decomposed into three subproblems: link selection, channel allocation, and power control. To solve these complicated problems, we propose two optimization algorithms that consist of a modified branch and bound method as well as Lagrange dual decomposition approach. The simulation results demonstrate the superiority of these two proposed algorithms in improving system throughput and EE compared with other algorithms.
Event-triggered differentially private average consensus for multi-agent network

This paper investigates the differentially private problem of the average consensus for a class of discrete-time multi-agent network systems ( MANSs ). Based on the MANSs, a new distributed differentially private consensus algorithm ( DPCA ) is developed. To avoid continuous communication between neighboring agents, a kind of intermittent communication strategy depending on an event-triggered function is established in our DPCA. Based on our algorithm, we carry out the detailed analysis including its convergence, its accuracy, its privacy and the trade-off between the accuracy and the privacy level, respectively. It is found that our algorithm preserves the privacy of initial states of all agents in the whole process of consensus computation. The trade-off motivates us to find the best achievable accuracy of our algorithm under the free parameters and the fixed privacy level. Finally, numerical experiment results testify the validity of our theoretical analysis.
Stralingsbelasting in nieuwbouwwoningen - voorlopige resultaten. VERA-survey 2006

Uit een landelijk onderzoek naar de stralingsbelasting in nieuwbouwwoningen, gebouwd tussen 1994 en 2003, blijkt dat de concentratie van het radioactieve edelgas radon en de hoeveelheid straling vanuit bodem en bouwmaterialen gedurende die periode niet zijn toegenomen. Dit is de belangrijkste conclusie uit een voorlopige rapportage van meetgegevens van de derde nationale radonsurvey. Met de uitkomst van dit onderzoek, dat uitgevoerd werd in opdracht van het ministerie van VROM, lijkt te worden voldaan aan de beleidslijn om het stralingsniveau in woningen niet verder toe te laten nemen. In eerdere surveys was namelijk gevonden dat de radonconcentratie in woningen gebouwd in de periode 1970 - 1990 met ongeveer de helft was toegenomen. Blootstelling aan straling in de woning is verantwoordelijk voor bijna de helft van de stralingsbelasting die Nederlandse burgers gemiddeld door het jaar heen oplopen. Een groot deel van de dosis straling die we binnenshuis ontvangen is het gevolg van het inademen van radioactieve vervalproducten van radon en thoron. Radon en thoron worden van nature gevormd in bodem- en bouwmaterialen. En omdat ze gasvormig zijn, komt een deel ervan in de woning terecht. Een ander deel van de stralingsdosis is het gevolg van gammastraling vanuit bouwmaterialen en bodem. De radonconcentraties blijken in de huidige survey lager dan de waarden die tien jaar geleden zijn gerapporteerd voor woningen uit de periode 1984 - 1993. Het vermoeden bestaat dat deze verschillen worden veroorzaakt door verschillen in meettechnieken. Mogelijk is in de vorige survey de aanwezigheid van thoron wel meegemeten en in de laatste survey niet. Als deze veronderstelling klopt, dan zou dat kunnen betekenen dat thoron een belangrijkere bijdrage levert aan de stralingsbelasting dan tot nu toe werd aangenomen. Nader onderzoek naar de meettechnieken en de bijdrage van thoron aan de metingen is inmiddels gestart. Door de onzekerheid over de mate waarin thoron bijdraagt aan de stralingsbelasting kunnen er op dit moment geen definitieve conclusies getrokken worden over trends in het gezondheidsrisico als gevolg van straling in de woning.
SIW-Integrated Parasitic DRA Array: Analysis, Design, and Measurement

The modeling and design of a new substrate integrated waveguide (SIW) series-fed dielectric resonator antenna (DRA) parasitic array are presented. Parasitic DRAs are added on both sides of each active element of a DRA fed by longitudinal slot on SIW. The E-coupling mechanism between a driven DRA and two parasitic DRA elements with low dielectric constant is discussed. Also, the impact of the coupling gap on reflection coefficient, impedance bandwidth, and gain and radiation pattern is presented. A four-element antenna array operating at the millimeter-wave band (36–39 GHz) is designed and fabricated. The measured radiation pattern demonstrates a broadside beam with a maximum gain of 12 dBi over an impedance bandwidth (with |S11| < −10 dB) of 3.3 GHz.
Personal Authentication and Hand Motion Recognition based on Wrist EMG Analysis by a Convolutional Neural Network

Recent years, EMG has attracted much attention as a tool of human interface. In hand motion recognition and personal authentication using wrist EMG, we have obtained good results. However, there has been no way to establish them at the same time. Therefore, in this paper we measure EMG by attaching dry type sensors to wrist, and carry out hand motion recognition and personal authentication. The conventional method used EMG of movement Japanese Janken. We use a multi-input and multi-output model of a Convolutional Neural Network (CNN). The average accuracy of hand motion recognition is 94.5%. The average accuracy of personal authentication is 94.6%. In the conventional method, personal authentication was classified into two classes. However, we carry out multi-class classification in the proposed method. In feature extraction, we obtain 128×8 input data from the measuring unit. Then, a filter size of the convolution layers is 3×3. CNN does not contain pooling layers in this paper. In the proposed method, the average accuracy of hand motion recognition is 94.6%. The average accuracy of personal authentication is 95.0%.
Cable Actuated Dexterous (CADEX) Glove for Effective Rehabilitation of the Hand for Patients with Neurological diseases

Neuroplastic changes in motor cortex is essential for the recovery motor function of patients with neurological diseases. To enlarge neuroplastic change, various movements should be provided to stimulate larger motor cortical area, and because hands occupy the largest area, it is especially important. Many wearable robotic devices have been developed for rehabilitation of the hand, and soft robotic devices in particular have drawn attention for their compact design. However, most soft devices provide simple thumb motions, which flex or extend all joints without assistance of opposition/reposition of the carpometacarpal joint although the importance in producing various grasps. In this study, the design of a cable actuated dexterous (CADEX) glove is proposed. For dexterous motion, the structure and orientation of major finger tendons were replicated with exotendons (actuated cables), and four exotendons were used for the thumb with the path optimized to provide flexion/extension of the thumb and decoupled opposition/reposition of the carpometacarpal with other joints. To provide consistent motion, silicon was used for stable anchoring of exotendons while preventing slippage and reducing deformation. The motion generated by the CADEX glove was experimentally evaluated for a single healthy subject. The result shows that the CADEX glove could flex and extend the finger with various ratios among joints, and the opposition/reposition of carpometacarpal joint of the thumb could be achieved consistently with minimal effect on the other joints. The CADEX glove is expected to help providing various tasks which is expected to enhance the functional recovery of patients with neurological disease.
Return on Investment for Three Cyberinfrastructure Facilities: A Local Campus Supercomputer, the NSF-Funded Jetstream Cloud System, and XSEDE (the eXtreme Science and Engineering Discovery Environment)

The economics of high performance computing are rapidly changing. Commercial cloud offerings, private research clouds, and pressure on the budgets of institutions of higher education and federally-funded research organizations are all contributing factors. As such, it has become a necessity that all expenses and investments be analyzed and considered carefully. In this paper we will analyze the return on investment (ROI) for three different kinds of cyberinfrastructure resources: the eXtreme Science and Engineering Discovery Environment (XSEDE); the NSF-funded Jetstream cloud system; and the Indiana University (IU) Big Red II supercomputer, funded exclusively by IU for use of the IU community and collaborators. We determined the ROI for these three resources by assigning financial values to services by either comparison with commercially available services, or by surveys of value of these resources to their users. In all three cases, the ROI for these very different types of cyberinfrastructure resources was well greater than 1 - meaning that investors are getting more than $1 in returned value for every $1 invested. While there are many ways to measure the value and impact of investment in cyberinfrastructure resources, we are able to quantify the short-term ROI and show that it is a net positive for campuses and the federal government respectively.
Hybrid Architecture for Integrating Solar PV Irrigation Pumps with Existing Rural Indian Microgrid

This paper presents a hybrid architecture for a typical rural Indian microgrid with integrated irrigation system. A stand-alone microgrid system named Dharnai Rural Microgrid (DRMG) serves the community loads in a small village called Dharnai in Jehanabad district, Bihar (25.0125° N, 84.9792° E). The load demand of solar PV irrigation pump system (MG1) is modeled for a period of 20 years based on crop pattern and rainfall. The Capacity Shortage (CS) of MG1 is varied between 5% and 20% to achieve high operational efficiency. These solar operated pumps are not integrated even though they are situated in and around 2 km radius from the DRMG. Thus, devising solar PV and battery independently for DRMG and MG1 results in two individual microgrid systems. The excess energy generated in MG1 during rainy seasons results in poor utilization of resources. Therefore, in this paper, the architecture of the DRMG system has been improved - MG1 and DRMG are architected together to operate as a single microgrid MG2 resulting in better resource utilization. This leads to an increase in the operational efficiency up to 6% while generating cost savings between 5.37% and 22.6%.
Hybrid Analog/Digital Linearization of GaN HEMT-Based Power Amplifiers

In this paper, we describe a hybrid analog/digital linearization scheme for GaN high-electron-mobility transistor (HEMT)-based power amplifiers that consists of a novel analog feedforward circuit and a conventional generalized memory polynomial (GMP) digital predistorter (DPD). The analog circuit implements a nonlinear filter that compensates the long-term memory effects observed in GaN HEMTs due to the self-biasing behavior caused by electron-trapping phenomena. Experimental tests demonstrate that this linearization scheme achieves a level of intermodulation distortion 6.8 dB better than what can be achieved with just the use of the GMP DPD. This level of distortion is in compliance with the linearity specifications for multicarrier Global System for Mobile communications base station transmitters.
Optimal Power Equipment Sizing and Management for Cooperative Buildings in Microgrids

We present a novel model for the optimal design and power management of a group of buildings with diverse load patterns that are able to exchange energy through a common dc bus. The determination of the optimal sizes of the photovoltaic arrays, energy storage systems and inverters, and the optimal scheduling of power exchanges are achieved through the formulation of a mixed integer linear programming problem. Furthermore, the Nash bargaining method is used in order to fairly distribute the cooperation profits among the participants. The proposed approach achieves the reduction of the microgrid's (MG's) cost and carbon emissions compared to noncooperative approaches, and promotes the enhancement of the MG's energy sufficiency by allowing energy exchanges among buildings with energy surplus and buildings with energy deficit. Our model also takes into account the case where additional buildings join the MG after the initial coalition establishment.