Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low-Profile Antenna With Omnidirectional and Unidirectional Radiation Patterns Over Two Operation Bands

A compact low-profile dual-band antenna with a single circular radiation patch is presented. The circular radiation patch is top-loaded by a metal strip, leading to a zeroth-order resonant (ZOR) mode with monopole-like radiation pattern at the lower operation band, and the ZOR mode is not affected by the position change of the probe feed. Thereby an off-center probe feed can be used to excite a TM01 mode with patch-like radiation pattern at the upper operation band, and its performance remains good stability when the size of the metal strip is varying. The equivalent circuit, electric field and current distributions of the proposed antenna are studied to investigate the principles of the ZOR and the TM01 modes, and all the results show that the two operation bands can be effectively and independently controlled. As a proof-of-concept, a circular patch antenna prototype with low profile of about $0.023~\lambda _{0}$ at 2.35 GHz is fabricated and tested. Good agreements between the measured and simulated results are achieved, showing monopole-like and patch-like radiation patterns within 2.28 - 2.40 GHz and 3.30 - 3.60 GHz, respectively, and the average realized gains are about 2.5 dBi and 8.5 dBi for the two operation bands.
Three Phase Transformerless Cascaded H- Bridge Solar PV Multilevel Inverter

This paper presents fell H-connect staggered inverter topology for fixed DC source and sun oriented PV application. The topology referenced in this paper represents justifies over the current customary inverters topologies. The proposed topology step-up the low PV voltage, and yet checks the most extreme power through SPV cluster with the help of MPPT controller. The topology offers low present and voltage THD; likewise DC component is in reasonable breaking points. The consequences of the proposed H-Bridge staggered inverter topology have been checked utilizing MATLAB Simulink model.
Research on Fault Diagnosis Method for Speed Sensor of High-Speed Train

Speed sensors installed on the axes of high-speed train will lead to faults due to the vibration and electromagnetic interference during train operation. At present the braking system can't detect all faults of speed sensor but misdirect the axle lock fault, which affects the safety of train operation. Therefore, this paper proposes an integral intelligent fault diagnosis method for speed sensor of high-speed train brake system, which realizes real-time detection of speed sensor anomalies and accurate location of the axis of the speed sensor fault. Firstly, the traditional principal component analysis method is improved by proposing a comprehensive monitoring statistic to realize real-time fault detection of speed sensor. Then, the modified reconstruction based contribution plot based on the idea of combination maximization is adopted to achieve accurate fault location of speed sensor. In addition, the fault injection experiments are conducted, the results prove the method can diagnose the fault of speed sensor accurately and effectively, and solve the hidden trouble of high-speed train operation.
Research on Driver Fatigue Detection Method Based on Parallel Convolution Neural Network

With the complexity of traffic avement and the increase of vehicles, the traffic accidents of fatigue driving still account for a large proportion of the total traffic accidents. Drivers will wear glasses that meet their needs in order to reduce the incidence of accidents, but the occlusion of glasses and the changes and jitter of light during driving can significantly affect the accuracy of facial information detection. Aiming at the above problems, in order to extract the facial features accurately, an adaptive compensation infrared acquisition system is used, and a fatigue state detection model of parallel convolution neural network is proposed. Based on the different detection characteristics of the same image, the convolution neural network is used to automatically complete the feature learning, so as to obtain a more comprehensive description of the fatigue driving characteristics. The support vector machine is used to train the characteristics and establish the classifier to judge whether the driver is tired or not. Compared with other existing algorithms, this method obtains higher accuracy, meets the requirements of real-time detection and has high robustness to complex driving environment.
Design of Memristor Based Controller for Speed Control of DC Motor

This paper proposes a memristic PID controller for speed control application of separately excited DC motor. The integral square error of the speed is considered as the objective function in this case. Due to automatic update of the weight of the memristor it is used as a tunable parameter in PID controller. The derivative and integral gain of the PID controller is tuned automatically with the help of memristor. The behaviour of the memristor with different input voltage is also shown in this paper. Also the characteristic of memristic PID controller is compared with conventional tuning techniques based PID controller and PSO based PID controller.
Prediction Based Smart Farming

The world population is expected to grow by over a third by 2050. Market demand for food will continue to grow. Automated drones and different robots in savvy cultivating applications offer the possibility to screen ranch arrive on a for each plant premise, which thus can diminish the measure of herbicides and pesticides that must be applied. There is a gap between current food productivity growth and needed growth. To boost the yield, farmers switched to extensive use of chemical fertilizers. Excessive fertilizer usage has its negative impact like decreased yield, wastage of fertilizer, damage to soil, and groundwater contamination. Currently, farmers mostly rely on guesswork, estimation, experience when deciding the crop that should grow, and the fertilizer that should be used. In this paper, we have proposed a solution that uses technologies like Machine Learning, Image Processing, and the Internet of things to improvise farm productivity and at the same time, decrease the fertilizer usage. This paper describes the outcomes of a prototype implemented in Rajasthan, India.
Research on the Critical Factors of Adopting Smart Hotel

With the rapid development of the economy, the development of the hotel industry has become increasingly prosperous and the competition has become increasingly fierce. Therefore, the smart hotels provide opportunities for innovation and development in the hotel industry. Based on the perspectives of innovation adoption, internal organization and external competitive pressures, this research proposes an integrated framework to explore the critical factors in the adoption of smart technology in the hotel industry.
Aviation Search&Rescue and Target Detection Technology Based on Intelligent Information

By studying the multi-platform collaborative search and rescue mission planning in complex dynamic enviroment and the task supervision and control of people on the loop, the capability of multi-platform collaborative operation in battlefield is improved. And then vives the sea surface high-precision target detection technology based on the improved Mean-shift clustering method, which lays a foundation for the establishment of a special aviation joint search and rescue command application platform and the provision of search and rescue technology for people in distress.
A Reuse-Degree Based Locality Classifier for Locality-Aware Data Replication

The last level cache (LLC) in shared configuration is widely used in the tiled chip multiprocessors (CMPs), which reduces the off-chip miss rate but incurs the long on-chip access latency. The state-of-the-art Locality-Aware Data Replication (LADR) scheme provides an effective tradeoff between capacity and latency through an in-hardware structure named locality classifier. However, the best Limited3 locality classifier (Limited3) in LADR equally preserves locality information of 3 cores for all cache lines indiscriminately that is superfluous for some lines reused by less than 3 cores but incomplete for other lines reused by more than 3 cores, which not only wastes the storage space but also limits the performance improvement. In this paper, we propose a novel concept of Reuse-Degree (RD) for each LLC line, since the line is loaded into LLC, to represent the number of cores that have reused the line. Then, we divide cache lines into Not Reused Line (NRL, RD = 0), Single Reused Line (SRL, RD = 1) and Multiple Reused Line (MRL, RD >= 2) based on their RDs and find that a significant fraction of LLC lines are NRLs or SRLs at any time. Based on this observation, we design a Reuse-Degree based Locality Classifier (RD_LC) for LADR. Specifically, RD_LC decouples the locality classifier from the LLC tag array and introduces two kinds of locality information arrays, single locality information array (SLIA) and complete locality information array (CLIA). Besides, RD_LC allocates a locality information entry only for the reused cache lines (SRLs or MRLs) instead of all cache lines, and assigns an SLIA entry to SRLs and a CLIA entry to MRLs. Our proposal avoids a waste of the storage space and also maintains enough locality information for the accuracy of data replication decisions. Experimental results show that our RD_LC for LADR saves 51% of the storage overhead than that of the baseline Limited3 locality classifier with a performance improvement and a network traffic reduction by 7.56% and 3.33 % respectively.
Resource Efficient Personalized ECG Beat Classification via Temporal Logic Synthesis

According to the World Health Organization, cardio-vascular diseases accounts for 31% of all deaths worldwide in 2016. Detecting the onset of heart irregularities can potentially save many lives. The ubiquity of wearable devices opens up the possibility of having a heart disease detection at everyone's disposal. To enable this, low energy ECG classification is needed. Unlike previous methods of using signal processing or even deep learning networks, this paper is the first to propose a low cost means to detect abnormal ECG beat signals by first synthesizing temporal logic formulas from training signals, and then checking if the synthesized formulas by the input signal at runtime. Our results show that the method has a high accuracy in detecting abnormal ECG beats while requiring significantly lower computation resource. Compared to It takes only a state-of-the-art convolutional neural network approach, our method achieves a comparable accuracy but with 0.3% of memory, and millions of computation operations, hence energy, saved.
Optimizing Query Times for Multiple Users Scenario of Differential Privacy

Differential privacy is the state-of-the-art for preserving privacy and differential privacy mechanism based on Laplace distribution with mean 0 is common practice. However, privacy budget is exhausted so quick that the number of queries is not enough. In this paper, a differential privacy mechanism is proposed to optimize the number of queries for application scenario of multiple users. We isolate different users by assigning various noise distribution with non-zero mean to different users. First, in terms of privacy guarantee, the proposed mechanism is better than common practice. Second, for the utility aspect, the accuracy of proposed mechanism is analyzed from the view of data distribution’s distortion and the view of noise’s absolute value.
Complex Brain Networks and Simulated Military Reactions using a Virtual Reality System

considering the strategic direction of the Colombian National Army, the need to increase training effectiveness using technological developments in biomedical engineering is highlighted. This study evaluates brain electrical activity via complex networks in virtual reality situations which simulate military reactions. Results suggest that a high network degree may be related to an appropriate decision-making process, whereas a lower value may be associated with poor performances according to military doctrine. While not entirely significant, some difference is appreciated, mainly between the base period and the event related to subject elimination (p=0.058). The authors also noted the burst suppression pattern when the subject was eliminated. As this is a work in progress, more research subjects are being recruited and more complex networks descriptors are being explored.
Analysis of Acoustic Impedance of Duct Wall Based on Finite Element and Adaptive Evolutionary Algorithm

For acoustic model of the finite length duct, in order to make the duct noise achieve the maximum attenuation in a wide frequency band, the acoustic finite element method and the adaptive evolutionary algorithm are used to optimize the impedance parameters of the acoustic liner of duct. The numerical simulations of the optimal values of the acoustic impedance and the acoustic impedance of the acoustic liner under the rectangular cross-section duct is studied. The influence of the grazing flow, the length of the acoustic liner and the laying way on the optimal acoustic impedance and the transmission loss of duct are discussed, which provides a theoretical basis for the realization of hybrid active/passive sound absorption.
Intelligent Trainer for Athletes using Machine Learning

International professional athletes are looked after and trained by a team of professionals consisting of trainers and medical professionals among other. They make sure that the athlete is physically and mentally prepared to compete in a competition, and often train for years for the perfect results. Sri Lankan athletes however do not have the same luxury of being taken cared by a team of such professionals since they are young due to the lack of adequate resources in the country. ‘Optio’ mobile application aims to provide a solution for this problem by creating a mobile application that the athlete constantly has access to, which will provide him/her with dietary, exercise and health related advice catered and customized to each individual athlete’s needs. Consequently, this will provide a method which will let the athlete’s trainer monitor their athletes easily as well as let them pick the most suitable athlete for a competition.
Room Style Estimation for Style-Aware Recommendation

Interior design is a complex task as evident by multitude of professionals, websites, and books, offering design advice. Additionally, such advice is highly subjective in nature since different experts might have different interior design opinions. Our goal is to offer data-driven recommendations for an interior design task that reflects an individual's room style preferences. We present a style-based image suggestion framework to search for room ideas and relevant products for a given query image. We train a deep neural network classifier by focusing on high volume classes with high-agreement samples using a VGG architecture. The resulting model shows promising results and paves the way to style-aware product recommendation in virtual reality platforms for 3D room design.
A Low Profile Ultrawide Band Antenna for WLAN/Wi-Max/C-Band Wireless applications

In this communication a low profile ultrawideband microstrip antenna has been proposed to cover Wi-Max (3.5 GHz/ 5.5 GHz), WLAN (5 GHz, 5.2GHz, 5.8 GHz) and C-band wireless applications suitable for a single multifunctional device. The Wide band operation have been achieved by combining resonant modes corresponding to three rectangular slots and a C-shaped horizontal strip on the ground plane. The proposed antenna is designed on 4.4 permittivity FR-4 substrate with the help of Ansoft HFSS simulator and has been experimentally verified. The Measured and simulated results are well agreed with each other.
Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone’s energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device’s energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone’s power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones’ batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed.
A new approach for rolling bearing fault diagnosis based on EEMD hierarchical entropy and improved CS-SVM

The fault diagnosis of CNC machine tools has become an important area of Prognostic and Health Management (PHM). The failure of rolling bearings on spindle is main cause of machine tool faults. Therefore, the significant focus of health management of CNC machine tools and other rotating machines is fault diagnosis of rolling bearings. In terms of the fault diagnosis, it is the most critical task to extracting bearing fault characteristics from vibration signals of rolling bearings. As a result, a new fault diagnosis method for bearing fault classification is proposed in this paper, which is built on the hierarchical entropy and improved Cuckoo Search-Support Vector Machine(CS-SVM). Firstly, ensemble empirical mode decomposition(EEMD) is adopted to decompose time domain vibration signals, aiming at eliminating modal confusion in empirical mode decomposition(EMD) method. Afterwards, the hierarchical entropy is chosen as fault feature parameters compared with sample entropy to construct feature vectors. In addition, the classification algorithm of multiple SVM optimized by the improved CS algorithm is utilized to identify rolling bearing fault modes. Finally, the proposed method is verified through the data taken from the Case Western Reserve University (CWRU) Bearing Data Center. The result demonstrates that the proposed method has promising performance and achieves accurate fault classification accuracy in rolling bearing fault diagnosis in comparison with other methods.
Towards a Flexible Wrist-Worn Thermotherapy and Thermoregulation Device

Body temperature is one of the vital parameters and the most common measurand for physiological monitoring. Heat has been used from ancient times as a medicinal and healing modality. It can be used generally for pain relief and can be particularly useful to arthritis patients, while it can be beneficial for battling dermatological and surgical site infections. Thermoregulation is essential for homeostasis and for sustaining body temperature. In this paper we present a wrist-worn flexible device integrating temperature sensing and heating capabilities for localized temperature measurements, heating for thermotherapy and closed-loop thermoregulation in the hands. The latter is useful for sustain hand temperature within healthy limits in extreme environments. The temperature sensing and heating capabilities of the device are characterized and presented in detail. We finally present a precise study of the effect of bending to the proposed device.
Incremental Weighted Support Vector Data Description Method for Incipient Fault Detection of Rolling Bearing

Incipient fault detection is a key technical link in the field of rolling bearing prognostic and health management. The traditional incipient fault detection models are generally built on offline data and unable to update timely for matching the online data of target bearing. Moreover, when using the anomaly detection algorithm represented by support vector data description (SVDD) for incipient fault detection, it is easy to cause high false alarm rate due to slight and anomalous fluctuation of online data. To solve the above problems, an incremental weighted support vector data description (IW-SVDD) is proposed for incipient fault detection of rolling bearing. First, we train an initial SVDD detection model based on a small amount of online data that exists at initial stage, and use this model to pre-detect the sequentially-arrived online data. Second, in order to adapt the detection model to the anomalous fluctuations of online data, we design a strategy to determine the sample state. This strategy divides the fluctuation of online data into four states: abnormal appears, abnormal appears in succession, abnormal disappears and abnormal re-appears. Then we assign proper weights on the corresponding samples in each state. Finally, we update training set repeatedly by replacing the earliest samples in the training set with the same amount of samples which violates KKT condition in pre-detection results. In this way, the detection model is re-trained in incremental mode. Experiment results on IEEE PHM Challenge 2012 show that the proposed IWSVDD model can effectively reduce false alarm rate with ensuring detection accuracy.
Octagonal Shaped Flexible Chipless RFID Tag for Internet of Things

In this paper, a compact, miniaturized, and flexible chipless RFID tag is proposed. The realized tag possesses the encoding capacity of 9-bits. The tag is made up of slotted octagonal resonant elements that are placed in a nested loop manner to analyze the electromagnetic performance of the tag. The proposed tag is investigated for three different substrates from non-flexible to flexible, i.e. Taconic TLX-0, PET and Rogers RT/duroid®/5880. With Taconic TLX-0 as a substrate, the tag operates in the RF band of 4.3 GHz - 12 GHz while PET-based tag covers RF band of 4.7 GHz - 13.7 GHz and Rogers RT/duroid®/5880 based tag operates in the squeezed frequency spectrum of 4 GHz - 12.3 GHz. The presented tag is suitable to be deployed as an enabler of the Internet of Things.
A Reinforcement Learning Based Low-Delay Scheduling With Adaptive Transmission

As modern communication systems become indispensable, the requirements for communication systems such as delay and power get more stringent. In this paper, we adopt a Reinforcement Learning (RL) based approach to obtain the optimal trade-off between delay and power consumption for a given power constraint in a communication system whose conditions (e.g., channel conditions, traffic arrival rates) can change over time. To this end, we first formulate this problem as an infinite-horizon Markov Decision Process (MDP) and then Q-learning is adopted to solve this problem. To handle the given power constraint, we apply the Lagrange multiplier method that transforms a constrained optimization problem into a non-constrained problem. Finally, via simulation, we show that Q-learning achieves the optimal policy.