Miracle-dz/UniFAR-FTUs huggingface dataset:
Change in local blood flow in the kidney cortex of the rabbit in prolonged hypotension

The local blood flow in the renal cortex was studied on rabbits of the puberty age by means of the hydrogen clearance method during a prolonged bleeding. It has been established that at the beginning of bleeding the changes of systemic arterial pressure and the cortical blood flow of the Kidney have the same direction, though reduction of the latter passes ahead of the reduction of arterial pressure, which indicates the centralization of blood circulation. During the reduction of the systemic arterial pressure to 32-53% of the normal level equilibration a stabilization of the renal blood flow was observed. Microcirculation in the renal cortex ceases when the arterial pressure is 20-25 mm Hg/min.
Monitoring Sake Brewing Processes with Compact Wireless Sensors

This paper shows the results of monitoring Japanese sake brewing processes utilizing IoT technologies. A monitoring system used in this work includes compact wireless sensors. By using the wireless sensors, all the koji/moromi temperatures and their environment can be monitored. The system was practiced for all the products of this year in a brewery. Especially, the results show that the system enables to monitor inside a koji box which cannot be monitored by our previously proposed system.
Quantification of Indoor Ocular Exposure to Solar Ultraviolet Light With Four Room Orientations: Using a Model Monitor Embedded in a Manikin Head

Solar ultraviolet (UV) radiation is an unavoidable physical environmental factor that contributes to a variety of eye diseases. Exposure to indoor solar UV radiation is an essential component of individual ocular UV exposure. However, few previous studies have attempted to quantify the exposure in term of indoor solar UV irradiance reaching the surface of the human eye under different exposure conditions. Hence, in the current study, we measured the UV exposure in rooms with four main orientations in Sanya (18.4° N, 109.7° E, the lowest-latitude city in mainland China) and Lhasa (29.7° N, 91.14° E, the highest-elevation city in mainland China) to obtain the general and maximum ocular exposure to indoor solar UV in China. A monitoring model consisting of a dual-channel spectrometer that implanted into the manikin eye was used to quantify the UV exposure at a range of times. This study revealed basic diurnal variations under different indoor exposure conditions. In summary, on a sunny summer day, if a person gazes out of an open window at a distance of 0.5 m away from the window, some danger to the eyes may exist in the solar elevation angle (SEA) range of 12° to 37° in an eastward-facing room and in the SEA range of 34° to 9° in a westward-facing room under the maximum open window UV exposure conditions in Lhasa. Similarly, the accumulated UV exposure in a southward-facing room in Sanya was in the SEA range of 67° to 88°. This study attempted to determine the maximum individual accumulated ocular unweighted UVA dose and biologically effective UV dose (UVBE) to serve as a reference for exposure to Sanya and Lhasa. These results can strengthen the understanding of human ocular health and further clarify the possible risks posed by continuous UV exposure.
Pulmonary Nodule Segmentation in Computed Tomography with an Encoder-Decoder Architecture

Precise pulmonary nodule segmentation from computed tomography (CT) images can contribute a lot for both clinical and research purposes while the variability of pulmonary nodules can make it difficult to perform an accurate and robust segmentation. We propose an encoder-decoder architecture which takes great advantages from convolutional neural networks and attains state-of-the-art results on the public LIDC-IDRI dataset. Our proposed method achieves an average Dice-coefficient score of 90.56% and an average surface distance of 0.13 millimeters on a five-fold cross-validation, which surpasses all the present pulmonary nodule segmentation methods on the LIDC-IDRI dataset. Besides, our method is extremely time-efficient with an average time of less than 20 milliseconds segmenting a single nodule using a modern GPU.
Polymer Microwave Fiber: a New Communication Concept That Blends Wireless, Wireline and Optical Communication

This paper provides an overview of the recent developments in the research on Polymer Microwave Fiber (PMF) communication. PMF is a new communication concept, which combines millimeter-wave (30-300 GHz) transceiver chips, small antennas and a cheap plastic fiber. This concept offers unique benefits over copper wireline and optical implementations and can provide a robust, cost-effective, low-weight, high-speed communication link for various applications.
Waiting or Moving? A Crossroad Network-Based Markov Decision Process Approach to Catch Vacant Taxis

Taxi services play a critical role in the public transportation system in our cities. However, we usually find it difficult to catch vacant taxis based on our experience alone in the random taxi-waiting mode, especially on the streets unfamiliar to us, which may greatly influence users’ taxi service experience. Therefore, how to recommend appropriate waiting locations for passengers becomes meaningful, and the available large-scale taxi trajectory data have helped with the right recommendation. Recent researches have focused on the one-location recommendation for the passengers without considering the recommendation failure situation where they find no vacant taxis available after waiting a long time. In response to this deficiency, we designed a Crossroad Network-based Markov Decision Process (CN-MDP) scheme to recommend a waiting location sequence for a current passenger whose cumulative probability of catching a vacant taxi getting close to 100%. Further, our scheme changes the recommended locations from the road segments to the crossroads, as we discovered that passengers are more likely to catch vacant taxis at the crossroads connected to multi-road segments. In addition, the multi-passengers competing strategy for vacant taxis at the same location and in the same time slot is also involved in our scheme by dynamically updating the pass rate of vacant taxis at each crossroad and road segment. Some evaluations on a real taxi data set from a major city in China have shown that our recommendation scheme works well and has a higher probability of catching vacant taxis than that of our previous approach and other ones. Our scheme further improves the user experience of taxi services.
A Contextual Approach Protecting Online Privacy, A Crucial Need

In the era of the global world, everyone thrives to share their personal life and credentials online through different sites. The Internet has become an essential use of our daily life in order to email, sharing of documents, research, bank transactions and even basic life activities on different social media mediums. Since the rapid evolution in the of technology is taking over the globe in last few decades in making human's life easier through easy and rapid actions, there is dire need to be sure of how much our credentials are safe with the parties that we are sharing the data. Internet frauds, cyber-attacks and third parties monitoring the data is the cause of serious concern of the first-hand user of the internet. The basic internet users do not even follow the basic safety precautions or are even unaware of the risk that the internet caring while sharing personal details. This paper suggests the basic internet users minimize the threats and risks that internet caries while sharing the data. Different tools and techniques are discussed in this study that can be used to protect the privacy of user from unauthorized, attackers and third-parties. It is the need of the hour to motivate and educate internet users to safe browsing and to enhance the internet user experience. This paper elaborates the maximum number of precautions the internet user can take to secure and enjoy safe browsing.
Effective and Simple Control of Grid-Connected Three-Phase Converter Operating at a Strongly Distorted Voltage

Three-phase power electronics converters (PECs) are commonly used as an interconnection of DC loads/sources with the electrical AC power system. Their important goal is to ensure three-phase symmetrical and pure sinusoidal grid current waveforms. Unfortunately, frequent adverse of grid voltage conditions like the occurrence of higher order harmonics and dips/sags leads to problems in maintaining high-quality parameters of current and stable operation of PEC. Therefore, a more complex control algorithms are usually used to solve those problems. In this paper, effective and simple control is proposed to mitigate grid current distortions caused by grid-voltage harmonics. This improvement of current quality can be easily obtained in control of grid-connected PEC using only band notch filtered grid voltage as a feedforward without any additional control loops parallel connected to linear current controllers (tuned to fundamental frequency). Theoretical analysis with simulation and experimental investigations are presented, which prove very good properties and advantages of the proposed control idea.
Point-of-Interest Recommendation in Location-Based Social Networks

This article investigates the problem of proposing new locations to users who have previously been informed about location-based social networks. In the study, user profiles (past location information), the popularity of the place and the time of preference of the place were obtained. Using this information, a different place is offered to the users. However, when users have a different location (a different city or country), it is provided to recommend one of the point of interests designated by categories according to the pre-made check-in details. Social media has been a component of daily life of users. Applications such as Foursquare/ Swarm constitute the data source of the communication that geographically contains location data. This study analyzes the personal behaviors of the users using social networks for the spaces and reveals the characteristics of the spaces and people by past notification activities through the applications made in the past. The experiments on real foursquare data showed the usability of the proposed method and achieved results with high accuracy.
Error Correction System using Stochastic Numbers in Symmetric Channels and Z Channels

In fields of image processing and neural network, increasing amount of data and complex functions are making circuits precise. This makes errors in circuits relatively large. When upper bits of binary signals flip due to noise, the value will increase or decrease drastically. On the other hand, if stochastic numbers are used, the changes on their values are the same since all the bits have the same weight. Therefore, stochastic computing, a computation method based on stochastic numbers, is attracting interest. Stochastic computing does have error tolerance, but cannot restore the bit stream if the bits are erroneous. Here, we focus on evaluating the error-free value from the bit error rate and the erroneous value. We have proposed a method to correct errors of stochastic numbers by measuring the bit error rate and filtering the values properly in symmetric channels. In this paper, we propose a method that can also correct errors in Z channels as well as symmetric channels. From experimental evaluations, in environment with high bit error rate, this proposal will give a better peak-signal-to-noise ratio compared with a conventional error correction coding.
Fuzzy Vault Scheme Based on Fixed-Length Templates Applied to Dynamic Signature Verification

As a consequence of the wide deployment of biometrics-based recognition systems, there are increasing concerns about the security of the sensitive information managed. Various techniques have been proposed in the literature for the biometric templates protection (BTP), having gained great popularity the crypto-biometric systems. In the present paper we propose the implementation of a Fuzzy Vault (FV) scheme based on fixed-length templates with application to dynamic signature verification (DSV), where only 15 global features of the signature are considered to form the templates. The performance of the proposed system is evaluated using three databases: a proprietary collection of signatures, and the publicly available databases MCYT and BioSecure. The experimental results show very similar verification performance compared to an equivalent unprotected system.
Efficiency Evaluation for Triangle/Ray Intersection Algorithms on Hardware

Ray Tracing is one of the most popular graphics algorithms due to its realism. The algorithm has a high computational complexity, which makes it impossible to implement in real time until today. The Triangle/Ray intersection is an specific problem, which has several implementation options, including the barycentric transformation. The state of the art diverges respect to the efficiency of the barycentric transformation compared against other algorithms. This research addresses the problem from a theoretical implementation point of view, demonstrating an efficiency of at least 53% with the barycentric transformation, applicable for any architecture or implementation.
May I Draw Your Attention? Initial Lessons From the Large-Scale Generative Mark Maker

Everyday robots are emerging in contexts from household chores to entertainment, and an accompanying need arises to understand perceptions of these systems. We propose a sleek 2D hanging pen plotter as a useful tool for both furthering art and studying human responses to ambient everyday robots. Aspects of this system and the pieces it produced were grounded in ideas from conceptual and generative art. We installed our robotic system in three different spaces in the wild, recorded video footage of the installations, consulted with expert artists, and interviewed a subset of exhibit visitors. Key findings included visitor assumptions of robot responsiveness and differences in user behaviors between performing arts and engineering environments. The exploratory products of this work can benefit artists who employ generative concepts in their work, as well as roboticists who seek further understanding of human-robot interaction in the wild.
Competition Code And LBP Palm Vein Feature-Level Fusion Using Canonical Correlation Analysis

Biometrics is a discipline in computer science that uses biometrics to identify people and control access. With the development of technology, a variety of biometric recognition technologies are widely used, such as palm vein recognition technology. However, there are deficiencies in the feature extraction of the palm vein in one way. It is difficult to classify and identify. Therefore, the palm vein features are extracted in two different ways to obtain two different feature sets. And the two feature sets have complementary characteristics when expressing the palm vein features. Then, to improve the classification effect, we used the Canonical Correlation Analysis (CCA) to fuse the two feature sets. The palm vein features are extracted using a Competition Code and a Local Binary Pattern (LBP) to obtain two different palm vein features sets in this paper. The Competition Code uses the local orientation information of the image to extract the palm vein feature, and the LBP utilizes the local texture feature of the image to extract the palm vein feature. These two features can achieve complementarity. CCA is a feature-level fusion technique. CCA projects two feature sets into the same spatial domain through linear transformation, and achieves effective feature fusion in the same spatial domain. A good classification effect can be achieved by the two feature sets of the CCA fusion palm vein. Our experiments are carried out in a public database of Hong Kong Polytechnic University. Compared with the single palm vein competition code feature or LBP feature, the palm vein feature after CCA fusion shortens the classification time in some degree. And the recognition rate is increased to 98.14% when the ratio of the training sample to the test sample is 9:3.
Quality Tunable Approximate Adder for Low Energy Image Processing Applications

In this paper, we present energy-efficient Tunable Approximate Adders (TAAs) based on power-gating. We propose two TAA designs: TAA1 and TAA2. TAAs are runtime configurable and are bit-tunable to support varying degrees of approximation with maximum bounded error. TAA designs have been implemented using UMC 65nm technology. On average, a single-bit TAA1 and TAA2 consume 15% and 47% lesser energy as compared to a single-bit exact mirror adder respectively. TAA1 is relatively lesser erroneous but consumes more energy as compared to TAA2. We also applied TAA1 and TAA2 on image processing applications: image addition, mean filtering and Laplacian filtering on multiple images. We have studied the trade-off between energy consumption and output quality by varying the number of approximate bits for the aforementioned applications. For TAA1 at 4-bit approximation, the average PSNR and average SSIM were 32.79 and 0.875 respectively with an energy saving of 6.67%. For TAA2 at 4-bit approximation, the average PSNR and average SSIM were 28.37 and 0.88 respectively with an energy saving of 28.9%.
Big Data Mining: Managing the Costs of Data Mining

The amount of data collected and stored in various industries has grown exponentially in the last decade. Data is collected and stored from industries consisting of large consumers such as telecommunications, banking or financial sectors. Further, given the advent of cloud computing and software availability in the cloud being cheaper, smaller industries are utilizing data storage for competitive advantage. Companies increasingly rely on analysis of huge amounts of data to gain a strategic advantage, improving on product quality and providing better services to their end users be it the employee, consumer or customer. A combination of statistical techniques and file management tools once sufficed for analyzing mounds of data. The costs of analysis are often charged out at very high rates for companies that require data analysis and the output is dependent very much on analyzing the correct attributes within large databases to ensure the data analyzed provides the relevant result. The most known technique or tools are the subject of the growing field of knowledge discovery in databases (KDD) [1]. Using business process data mapping (BPDM) to define the targeted data along with the process of knowledge discovery mapping in the database may provide a more targeted approach with much lest costs expended.
Implementation of Yolo-v2 Image Recognition and Other Testbenches for a CNN Accelerator

An implementation of Yolo-v2 image recognition and other testbenches for a deep learning accelerator is presented. This chip is the initial version of our on-going effort for a higher performance accelerator development. The accelerator is based on a systolic array and can handle convolution and max-pooling layer in a combined way or separately using 16 bit floating-point data. It also supports inner-product and LSTM layers. For demonstration and as one of the design verification testbenches, we implemented Yolo-v2 image recognition for 80 object classes. We converted the Yolo-v2 software to 16 bit floating point version and used it in the simulation and FPGA experiment during the chip development. Several other testbenches were designed and used to test various networks. In this paper, the accelerator’s architecture and the Yolo-v2 implementation on our FPGA board including camera and LCD is explained, including the software implementation. Some analyses on the measured and estimated performance is shown.
Refinement of Parapapillary Atrophy Segmentation Based on Conditional Random Field

Parapapillary atrophy (PPA) is a crescent atrophic abnormality adjacent to optic disk (OD). In clinical findings, PPA is highly related to glaucoma and high myopia, which makes it a potential indicator for ophthalmic diseases. Therefore, it is significant to develop an algorithm for automatic segmentation of PPA so as to aid the treatment and the prevention of eye diseases. A method that combines the confidence map with conditional random field (CRF) model is proposed in this paper to achieve the optimization of initial segmentation results from neural network. Holistically-Nested Edge Detection (HED) is applied to the segmentation of PPA and a confidence map is obtained. The CRF model is constructed with the confidence map and Maximum a posteriori (MAP) inference is implemented to conduct the segmentation. Post-processing is utilized to further refine the segmentation result. The proposed method has the advantage of robust performance when there is difference between training set and test set. The experimental results show that the average F-score is increased from 0.57 to 0.67, which shows a fair improvement effect.
An Efficient Model for Automatic Number Plate Detection using HOG Feature from New North Iraq Vehicle Images Dataset

the increasing number of vehicles in all over the world day by day, have made traffic control becomes a serious and major problem. Sometimes, it becomes difficult to detect the plate license number of a vehicle that violates traffic rules. In this paper, an efficient model is proposed to detect and locate number license plate of a vehicle that is given in color images. The model is constructed by using different features instead of single feature to improve its performance based on multi descriptors (texture features). The work use multi- boosting model (AdaBoost) based on HOG descriptors, which precisely localize the ROI of the given vehicle images. A new North Iraq Vehicle Images (NI-VI) dataset, which is used to test the proposed model, is introduced. The license number plates of North Iraq uses Arabic font. All images of this dataset, are taken under various circumstances and different weather conditions to simulate all realistic examples about vehicles. The developed model produced 89.66% accuracy score.
Methods for Improving the Accuracy of the Virtual Assessment of Autonomous Driving

Assessing the safety of an autonomous vehicle is an open problem within the research domain for autonomous vehicles. Next to real-world driving tests, simulation and reprocessing of recordings play a crucial role in validating the correct and safe behavior. Current state-of-the-art methods for function reprocessing suffer from several sources of error and hence, might lead to incorrect results. In this work, an overview of the most recent reprocessing methods is given and their shortcomings are described. We suggest the derivation of explicit sensor models and the learning of behavior models for traffic objects. An overview of different levels of sensor and different kinds of agent models is given along with a discussion for the need for statistical and machine learning based models. Furthermore, a novel method, based on infrastructure sensors, to collect the data needed for the derivation of the models is presented.