nils-herrmann/clean_scirepeval huggingface dataset:
Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application

A new classification approach for human body postures based on a neural fuzzy network is proposed in this paper, and the approach is applied to detect emergencies that are caused by accidental falls. Four main body postures are used for posture classification, including standing, bending, sitting, and lying. After the human body is segmented from the background, the classification features are extracted from the silhouette. The body silhouette is projected onto horizontal and vertical axes, and then, a discrete Fourier transform is applied to each projected histogram. Magnitudes of significant Fourier transform coefficients together with the silhouette length-width ratio are used as features. The classifier is designed by a neural fuzzy network. The four postures can be classified with high accuracy according to experimental results. Classification results are also applicable to home care emergency detection of a person who suddenly falls and remains in the lying posture for a period of time due to experiments that were performed.
Design of a High-Efficiency 2.45-GHz Rectenna for Low-Input-Power Energy Harvesting

This letter presents a high-efficiency 2.45-GHz rectenna that can harvest low input RF power effectively. A new antenna with a simple structure and high gain of 8.6 dBi is proposed for the rectenna. The antenna is designed to directly match the rectifying circuit at 2.45 GHz and mismatch it at the second and third harmonics so that the use of bandpass filter between the antenna and rectifying circuit can be eliminated. The rectenna shows a maximum conversion efficiency of 83% with a load resistance of 1400 Ω. Furthermore, the overall conversion efficiency can remain 50% for the low, -17.2 dBm (corresponding power density 0.22 μW/cm2 ) input power level.
A Circuit Modeling Technique for the ISO 7637-3 Capacitive Coupling Clamp Test

In this paper, we propose a transmission-line modeling technique for the ISO 7637-3 capacitive coupling clamp (CCC) test. Besides modeling the test bench, special attention is devoted to the CCC itself, for which an equivalent circuit is constructed based on the concept of surface transfer impedance and surface transfer admittance. The overall model is validated by means of measurements using a nonlinear circuit as device-under-test, as such demonstrating the appositeness to mimick the CCC test in simulations during the design phase.
Polymer Nanoparticles for Smart Drug Delivery

In the recent decades, polymers are widely used as biomaterials due to their favorable properties such as good biocompatibility, easy design and preparation, a variety of structures and interesting bio-mimetic character. Especially in the field of smart drug delivery, polymer played a significant role because it can deliver therapeutic agents directly into the intended site of action, with superior efficacy. The ideal requirements for designing nano-particulate delivery system are to effectively be controlled particle size, surface character; enhance permeation, flexibility, solubility and release of therapeutically active agents in order to attain the target and specific activity at a predetermined rate and time. The smart drug delivery systems have been successfully made by the advances in polymer science in the bio-nano‐ technology field. Recently, these advances have been found in various medical applications for nano-scale structures in smart drug delivery. The smart drug delivery systems should possess some important feature such as pre-scheduled rate, self controlled, targeted, predetermined time and monitor the delivery. The smart drug delivery system enhances the polymer nanoparticle better stage to their therapy regimen. They are drug carriers of natural, semi-synthetic, and synthetic polymeric nature at the nano-scale to micro-scale range. The polymeric particles are collectively named as spheres and capsules. The most of the polymeric nanoparticles with surfactants offer stability of various forms of active drugs and have useful to smart release properties. There are numerous biological applications have been reported for the nano-scale to micro-scale sized particles, such as site-targeted, controlled, and enhanced bioavailability of hydrophobic drugs [1-4]. Due to the nanoparticles size the drugs have been targeting into various applications, such as, various cancers targeting has been shown to be promising . Moreover, polymeric particles proved their effectiveness in stabilizing and protecting the drug molecules such as proteins, peptides, or DNA molecules from various environmental hazards degradation [2-4, 6, 7]. So these polymers are affording the potential for various protein and gene delivery. Numerous methods had been available to fabricate
EEG Classification of Different Imaginary Movements within the Same Limb

The task of discriminating the motor imagery of different movements within the same limb using electroencephalography (EEG) signals is challenging because these imaginary movements have close spatial representations on the motor cortex area. There is, however, a pressing need to succeed in this task. The reason is that the ability to classify different same-limb imaginary movements could increase the number of control dimensions of a brain-computer interface (BCI). In this paper, we propose a 3-class BCI system that discriminates EEG signals corresponding to rest, imaginary grasp movements, and imaginary elbow movements. Besides, the differences between simple motor imagery and goal-oriented motor imagery in terms of their topographical distributions and classification accuracies are also being investigated. To the best of our knowledge, both problems have not been explored in the literature. Based on the EEG data recorded from 12 able-bodied individuals, we have demonstrated that same-limb motor imagery classification is possible. For the binary classification of imaginary grasp and elbow (goal-oriented) movements, the average accuracy achieved is 66.9%. For the 3-class problem of discriminating rest against imaginary grasp and elbow movements, the average classification accuracy achieved is 60.7%, which is greater than the random classification accuracy of 33.3%. Our results also show that goal-oriented imaginary elbow movements lead to a better classification performance compared to simple imaginary elbow movements. This proposed BCI system could potentially be used in controlling a robotic rehabilitation system, which can assist stroke patients in performing task-specific exercises.
UAV borne real-time road mapping system

Road information is useful in many fields. In this paper, a real-time mapping system is presented to acquire the image and determine the geometry of the road. These include designs of platform and instruments, data transmission, processing and archiving. Compare with the traditional platforms, the UAV (Unmanned Aerial Vehicle) platforms offer greater flexibility, shorter response time and is able to generate very high resolution data. It is inexpensive to operate, and its operation is not limited by air traffic constraints. With autonomous flight control system, it is easy to navigate the aircraft along the planned lines strictly. In this system, an unmanned fixed-wing aircraft with double engine and double generator is used, which helps to improve the reliability and capability of the platform. A digital three-axis stabilized platform is developed, which performs an important role in camera attitude controlling. To ensure there is no coverage hole, a data manage system is adopted: after an instantaneous inspection of data integrity, the data will be archived as they are received. If any anomaly is detected, the mission planning will be adapted to re-image the concerned area. Vehicle information extraction from image sequence is also described in this paper. The experiment showed the presented system worked well in real-time road mapping and vehicle information extraction.
Forward Error Correction for DNA Data Storage

We report on a strong capacity boost in storing digital data in synthetic DNA. In principle, synthetic DNA is an ideal media to archive digital data for very long times because the achievable data density and longevity outperforms today’s digital data storage media by far. On the other hand, neither the synthesis, nor the amplification and the sequencing of DNA strands can be performed error-free today and in the foreseeable future. In order to make synthetic DNA available as digital data storage media, specifically tailored forward error correction schemes have to be applied. For the purpose of realizing a DNA data storage, we have developed an efficient and robust forwarderror-correcting scheme adapted to the DNA channel. We based the design of the needed DNA channel model on data from a proof-of-concept conducted 2012 by a team from the Harvard Medical School . Our forward error correction scheme is able to cope with all error types of today’s DNA synthesis, amplification and sequencing processes, e.g. insertion, deletion, and swap errors. In a successful experiment, we were able to store and retrieve error-free 22 MByte of digital data in synthetic DNA recently. The found residual error probability is already in the same order as it is in hard disk drives and can be easily improved further. This proves the feasibility to use synthetic DNA as longterm digital data storage media.
Series Connection of Snubberless IGBTs for 6.6 kV Transformerless Converters

We developed a 6.6-kV 1-MVA transformerless converter making use of a snubberless insulated gate bipolar transistor (IGBT) 8-series connection using an active-gate-control. The active-gate-control can balance the collector voltage of IGBTs connected in series enabling the IGBT series connection. It allows the converter to output a high enough AC voltage to directly connect to 6.6-kV distribution lines. Therefore, the converter does not need a step-up and step-down transformer, and is extremely small and compact.
, GT-GOLEM-2011-001 , 2011 Ach : IPC for Real-Time Robot Control

We present a new Inter-Process Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating perception, control drivers, and algorithms in real-time systems that sample data from physical processes. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems. Finally, the source code for Ach is available under an Open Source BSD-style license.
Design and deployment of Aqua monitoring system using wireless sensor networks and IAR-Kick

In Aquaculture, the yields (shrimp, fish etc.) depend on the water characteristics of the aquaculture pond. For maximizing fish yields, the parameters which are to be kept at certain optimal levels in water. The parameters can vary a lot during the period of a day and can rapidly change depending on the external environmental conditions. Hence it is necessary to monitor these parameters with high frequency . Wireless sensor networks are used to monitor aqua farms for relevant parameters this system consists of two modules which are transmitter station and receiver station. The data transmits through GSM to the Database at receiver station. The graphical user interface was designed, to convey the data in the form of a message to the farmers in their respective local languages to their Mobile Phones and alerts them in unhygienic environmental conditions, in order to take suitable actions.  aquaculture; wireless sensor networks; IAR-Kick;pH;
FPGA based Time-to-Digital Converter

High-resolution Time-to-Digital converter (TDC) is one of the crucial blocks in Highenergy nuclear physics time of flight experiments. The contemporary time interval measurement techniques rely on employing methods like Time-to-Amplitude Conversion, Vernier method, Delay Locked Loops (DLL), Tapped Delay Lines (TDL), Differential Delay Lines (DDL) etc. Recently, FPGA based TDC designs with merits of low cost, fast development cycle and re-programmability are reported . The TDC implementation in FPGA have design challenges and issues like, lack of direct control over the propagation delays, unpredictable Place & Route (P&R) delays and delay variations due to Process, Voltage & Temperature (PVT) variations. In the TDC design presented here, the resolution below the minimum gate delay is achieved by employing differential (Vernier oscillator) technique. The predictable P&R is achieved by manual P&R of the critical elements and avoiding the use of digital synthesis tools. Further, in order to compensate for the delay variations due to PVT, a calibration methodology is developed and implemented. By implementing the design in a Flash-based FPGA, the low power design objective is achieved.
An AST-based Code Plagiarism Detection Algorithm

In modern software engineering, software plagiarism is widespread and uncurbed, developing plagiarism detection methods is imperative. Popular technologies of software plagiarism detection are mostly based on text, token and syntax tree. Among these plagiarism detection technologies, tree-based plagiarism detection technology can effectively detect the code which cannot be detected by the other two kinds of technologies. In this paper, we propose a more effective plagiarism detection algorithm based on abstract syntax tree (AST) by computing the hash values of the syntax tree nodes, and comparing them. In order to implement the algorithm more effectively, special measurement is taken to reduce the error rate when calculating the hash values of operations, especially the arithmetic operations like subtraction and division. Results of the test showed that the measurement is reliable and necessary. It performs well in the code comparison field, and is helpful in the area of protecting source code's copyright.
Cloud-based monitoring and analysis of yield efficiency in precision farming

Yield mapping visualizes yield rate per geological distribution. It is frequently used as a baseline metric to measure yield efficiency in precision farming. A major challenge in mapping yield for specialty crops is how to collect accurate yield data without incurring substantial overhead to a farming operation. We design a yield efficiency analysis system that uses a cloud-based computing platform to acquire and analyze yield data. By reusing labor data collected by a cloud-based labor monitoring system that we developed earlier, our system calculates yield data from labor data, and computes yield map in real time and without the overhead for data acquisition. A distinctive feature of our approach is the introduction of a customizable yield distribution function that quantifies the probability of geographic distribution of fruits weighted at a Labor Monitoring Device. Practitioners may define yield distribution functions based on operational characteristics of an orchard, enabling our system adaptive for a variety of orchards with different harvesting operations and canopy architecture. Using a multi-tenancy software architecture, our system can support multiple orchards concurrently with improved scalability and data privacy. Our system has been deployed and tested on Amazon Web Services (AWS).
Wearable Multichannel Photoplethysmography Framework for Heart Rate Monitoring During Intensive Exercise

We propose a multichannel-PPG sensor that uses truncated singular value decomposition (SVD) for accurate estimation of heart rate (HR) during high-intensity exercise. We implemented a sensor to acquire nine channel signals simultaneously, and to enable real-time monitoring of PPG signals and HR. For denoising, we used SVD programmed in the microcontroller unit and showed that the pure pulse is concentrated mostly within the first largest singular value with the related two eigenvectors by extracting the significant feature components of the pulse signals. Our proposed sensor was tested in 32 subjects during exercise of various intensities: walking and medium- and high-intensity jogging. We compared our proposed method with the single-channel method, the multichannel weighting method, and the multichannel best signal selection. Furthermore, we incorporated acceleration signals and a HR tracking algorithm, into our proposed multichannel framework and evaluated its performance using the 12 data sets from the 2015  Our method showed the lowest absolute errors in HR estimation when compared to the single-channel, multichannel weighting, and multichannel best signal selection methods, irrespective of exercise intensity. We also found that our proposed multichannel framework incorporating acceleration signals and a HR tracking algorithm provided the lowest average absolute error of 0.94 beats/m. The results show that our proposed multichannel framework can be used for accurate HR estimation during high-intensity exercise.
Heart rate variability biofeedback increases baroreflex gain and peak expiratory flow.

We evaluated heart rate variability biofeedback as a method for increasing vagal baroreflex gain and improving pulmonary function among 54 healthy adults.



We compared 10 sessions of biofeedback training with an uninstructed control. Cognitive and physiological effects were measured in four of the sessions.



We found acute increases in low-frequency and total spectrum heart rate variability, and in vagal baroreflex gain, correlated with slow breathing during biofeedback periods. Increased baseline baroreflex gain also occurred across sessions in the biofeedback group, independent of respiratory changes, and peak expiratory flow increased in this group, independently of cardiovascular changes. Biofeedback was accompanied by fewer adverse relaxation side effects than the control condition.



Heart rate variability biofeedback had strong long-term influences on resting baroreflex gain and pulmonary function. It should be examined as a method for treating cardiovascular and pulmonary diseases. Also, this study demonstrates neuroplasticity of the baroreflex.
Detection of pneumonia clouds in chest X-ray using image processing approach

Finding ways to automate diagnostics from medical images, has continuously been one of the most interesting areas of software development. This article presents a novel approach for detecting the presence of pneumonia clouds in chest X-rays (CXR) by using only Image processing techniques. For this, we have worked on 40 analog chest CXRs pertaining to Normal and Pneumonia infected patients. Indigenous algorithms have been developed for cropping and for extraction of the lung region from the images. To detect pneumonia clouds we have used Otsu thresholding which will segregate the healthy part of lung from the pneumonia infected cloudy regions. We are proposing to compute the ratio of area of healthy lung region to total lung region to establish a result. The task has been performed using Python and OpenCV as they are free, opensource tools and can be used by all, without any legality issues or cost implication.
A Nonparametric Delayed Feedback Model for Conversion Rate Prediction

Predicting conversion rates (CVRs) in display advertising (e.g., predicting the proportion of users who purchase an item (i.e., a conversion) after its corresponding ad is clicked) is important when measuring the effects of ads shown to users and to understanding the interests of the users. There is generally a time delay (i.e., so-called delayed feedback) between the ad click and conversion. Owing to the delayed feedback, samples that are converted after an observation period may be treated as negative. To overcome this drawback, CVR prediction assuming that the time delay follows an exponential distribution has been proposed. In practice, however, there is no guarantee that the delay is generated from the exponential distribution, and the best distribution with which to represent the delay depends on the data. In this paper, we propose a nonparametric delayed feedback model for CVR prediction that represents the distribution of the time delay without assuming a parametric distribution, such as an exponential or Weibull distribution. Because the distribution of the time delay is modeled depending on the content of an ad and the features of a user, various shapes of the distribution can be represented potentially. In experiments, we show that the proposed model can capture the distribution for the time delay on a synthetic dataset, even when the distribution is complicated. Moreover, on a real dataset, we show that the proposed model outperforms the existing method that assumes an exponential distribution for the time delay in terms of conversion rate prediction.
Crime hotspot mapping using the crime related factors—a spatial data mining approach

The technique of Hotspot Mapping is widely used in analysing the spatial characteristics of crimes. The spatial distribution of crime is considered to be related with a variety of socio-economic and crime opportunity factors. But existing methods usually focus on the target crime density as input without utilizing these related factors. In this study, we introduce a new crime hotspot mapping tool—Hotspot Optimization Tool (HOT). HOT is an application of spatial data miming to the field of hotspot mapping. The key component of HOT is the Geospatial Discriminative Patterns (GDPatterns) concept, which can capture the differences between two classes in a spatial dataset. Experiments are done using a real world dataset from a northeastern city in the United States and the pros and cons of utilizing related factors in hotspot mapping are discussed. Comparison studies with the Hot Spot Analysis tool implemented by Esri ArcMap 10.1 validate that HOT is capable of accurately mapping crime hotspots.
ACE: An Efficient Asynchronous Corner Tracker for Event Cameras

The emergence of bio-inspired event cameras has opened up new exciting possibilities in high-frequency tracking, overcoming some of the limitations of traditional frame-based vision (e.g. motion blur during high-speed motions or saturation in scenes with high dynamic range). As a result, research has been focusing on the processing of their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream into frame-like representations, we are yet to harness the true power of these cameras. In this paper, we propose the ACE tracker: a purely asynchronous framework to track corner-event features. Evaluation on benchmarking datasets reveals significant improvements in accuracy and computational efficiency in comparison to state-of-the-art event-based trackers. ACE achieves robust performance even in challenging scenarios, where traditional frame-based vision algorithms fail.
Parallel Networks: Synthetic Biology and Artificial Intelligence

Synthetic biology has emerged as an important technology for engineering cells to behave in controllable and predictable ways. The promise of this modern technology is dependent on our understanding of cellular complexity to allow us to engineer cells with novel function. In this regard, the fields of computer science and synthetic biology are critical for accelerating both our understanding of biological systems, and our ability to quantitatively engineer cells. Thus, advances in biology and biotechnology are arising at the intersection of computer science and synthetic biology approaches. This review seeks to introduce the field of synthetic biology to the computer science community, and to ignite a curiosity and interest in fostering a unique synergy for possible collaborations between synthetic biologists and computer scientists.