Miracle-dz/UniFAR-FTUs huggingface dataset:
A 10-Way Power Divider Based on a Transducer and a Radial Junction Operating in the Circular TM01 Mode

This work presents a 10-way Ku-band power divider using a mode transducer and a radial junction connected by an overmoded circular waveguide operating in the TM<sub>01</sub> mode. The circular symmetry of this mode has been exploited to obtain a power divider with the rectangular output ports radially distributed along the broad wall of the waveguides in <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>-plane configuration. This topology provides the same amplitude and phase for all the output ports. At the same time, a compact profile has been obtained, introducing a simple manufacturing for the two components of the divider. The first component is a mode transducer converting the TE<sub>10</sub> mode in the rectangular waveguide to the TM<sub>01</sub> mode in the circular waveguide. It is based on a novel topology providing a very high purity in the mode conversion with an attenuation for the other propagating mode, the <inline-formula> <tex-math notation="LaTeX">${\mathrm {TE}}_{11c}$ </tex-math></inline-formula>, higher than 60 dB. The second component is a 10-way radial junction that must work under the excitation of the TM<sub>01</sub>, whose special features, since this mode is not the fundamental one of the circular waveguide, will be highlighted. The final design has been validated with an experimental prototype, proposing a manufacturing based on four simple parts. This has been the key to obtain an experimental prototype with specifications in the state-of-the-art. The measured efficiency is better than 96.5% in a 16.7% relative frequency bandwidth from 11 GHz to 13 GHz, with return losses better than 25 dB in the common port. The measured difference between the signals at the output ports of the prototype is ±0.3 dB for the amplitudes and ±0.45° for the phases. A comparison of the obtained results with another divider based on the TE<sub>01</sub> mode shows the potential of the presented design for becoming an alternative to the more extended TE<sub>01</sub>-based power dividers.
Research of a Data Flow Test Method for Attribute-Based Access Control Systems

this paper proposes a data flow test model for attribute-based access control (ABAC) systems. First, the data flows of attribute values are described by formal forms and then the optimized histogram technique is used to analyze the range and the trend of the data flows. Then, an improved test coverage standard which is suitable for ABAC is used to analyze the error types and the causes of the test results. Finally, in order to verify the model's coverage rate and the effectiveness of generated test cases, the attribute mutation technique is used to compare the proposed method in this paper with related random algorithms and generation algorithms of simple test cases. The results of a large number of experiments show that the model is superior to the related random algorithms and simple test cases generation algorithms in terms of coverage rate and the effectiveness of test cases.
ATOS: Adaptive Program Tracing With Online Control Flow Graph Support

Program tracing solutions (i.e., tracers) can faithfully record runtime information about a program’s execution and enable flexible and powerful offline analysis. Therefore, they have become fundamental techniques extensively utilized in software analysis applications. However, few tracers have paid attention to the size of traces and corresponding overheads introduced to offline analysis, as well as the Control Flow Graph (CFG) support. This paper presents ATOS, an efficient tracing solution, to address these issues. It adaptively adjusts the granularity of tracing while conservatively preserving the essential execution information. We implement a prototype of ATOS and evaluate it on several benchmarks. The results show that ATOS can greatly reduce the size of a trace and accelerate offline analysis, while preserving the execution states and supporting existing applications seamlessly. For example, using ATOS, the trace produced by the application CryptoHunt is reduced by 46 times, while the analysis time is reduced by 34 times.
Learning Deep Dynamical Models of a Waste Incineration Plant from In-furnace Images and Process Data

This paper presents an approach for predicting in-furnace images and sensor signal readings for a waste incineration plant, utilizing a deep dynamical model based on Kalman Variational Auto-Encoders that considers a range of process signals, control inputs, and time-series sequences of infurnace image data. This is motivated by the need for automatic control systems to be able to anticipate abnormalities in incoming waste to prevent potential instabilities during and after combustion. Experimental results with real plant data show that the proposed strategy provides an improved prediction accuracy for both process signals and in-furnace images compared to a Long Short-Term Memory neural network.
Dark-Silicon Inspired Energy Efficient Hierarchical TDM NoC

In this paper, we propose a dark-silicon inspired hierarchical Time-Division-Multiplexing (TDM) network-on-chip (NoC) with online distributed setup-scheme for slots allocation. In addition to the normal mesh routers, we propose hierarchical routers, making use of the dim silicon parts of the chip, to hierarchically connect quad-routers units. Normal routers operate at full-chip-frequency at supply of 1. 2V, while hierarchical routers operate at half-chip-frequency and supply of 0.8V with double datawidth and half slot-size. Routers follow a proposed architecture that distinguishes between data-path and control-setup sub-routers. This allows separate clocking and operating supplies between data and control and to keep the control as a single-slot-cycle design independent of the data slot size. The proposed NoC architecture as well as a base NoC architecture from state-of-the-art are evaluated under uniform random traffic using Synopsys VCS and synthesized using Synopsys Design Compiler for SAED90nm technology. With the same power budget of the base NoC, the proposed architecture provides up to 74% improved setup latency, 32% increased NoC saturation load, and 21% higher success rates. The proposed hierarchical quad is based on leveraging the dim silicon parts of the chip for an energy efficient design. Though it consumes 1.78 times the area of the base quad, however with 56% under-clocked area operating at half the maximum chip frequency; thus reducing the power density to 52% of the base NoC.
Analysis on E-commerce Purchase Intention and Decision in Java and Sumatra

The rapid development of internet in Indonesia as a developing country has increased consumer’s intention to use internet for shopping online using e-commerce websites. This study aims at better understanding the factors affecting e-commerce intention and decision of consumers in Java and Sumatera island as the most dominant e-commerce consumers in this country. This paper modified constructs in UTAUT2 model based on literature studies and interview results with e-commerce experts, companies and consumers which are suitable with Indonesian actual condition. A model of consumers purchase intention and decision in e-commerce was developed which incorporates factors such as convenience, perceived website quality, social influence, facilitating condition, hedonic motivation, economic reason/price, security, variety, and goods delivery. Surveys data from 340 ecommerce consumers in Java and Sumatera island used in this study and analysed using covariance based structural equational modelling (CB-SEM). The result from the survey shows that hedonic motivation, convenience, and economic reason are significant influence purchase intention while purchase intention and facilitating condition are significant influence the purchase decision for consumer in Java island. On the other hand, only social influence and facilitating condition which are significant influence purchase intention for consumer in Sumatera island. Overall, the results help to increase our understanding of purchase intention and decision using e-commerce websites based on consumers characteristic in Java and Sumatera as part of Indonesia. It also can be very useful to determine strategies to lead internet users to become online purchaser.
On the Performance of Two-Way Multiple Relay Non-Orthogonal Multiple Access-Based Networks With Hardware Impairments

In this paper, we investigate the performance of two-way multiple relay non-orthogonal multiple access (NOMA) based networks with hardware impairments (HIs). Especially, opportunistic relay selection scheme is applied to enhance the spectral efficiency along with the spatial diversity. With the increasing of relay’s processing capacity, decode-and-forward (DF) protocol is considered in the networks. Based on the proposed relay selection scheme and NOMA protocol, the closed-form expressions for the outage probability (OP) of the system are derived. In order to study the energy efficiency (EE) of the system, simulation results for EE are also obtained. Furthermore, for the purpose of observing the impacts of HIs’ level, NOMA protocol and the number of relays on the system performance at high signal-to-noise ratios (SNRs), the asymptotic expressions of the OP are also derived. Finally, Monte Carlo (MC) simulation results are presented to verify the correctness of the analytical results.
An Automatic Car Accident Detection Method Based on Cooperative Vehicle Infrastructure Systems

Car accidents cause a large number of deaths and disabilities every day, a certain proportion of which result from untimely treatment and secondary accidents. To some extent, automatic car accident detection can shorten response time of rescue agencies and vehicles around accidents to improve rescue efficiency and traffic safety level. In this paper, we proposed an automatic car accident detection method based on Cooperative Vehicle Infrastructure Systems (CVIS) and machine vision. First of all, a novel image dataset CAD-CVIS is established to improve accuracy of accident detection based on intelligent roadside devices in CVIS. Especially, CAD-CVIS is consisted of various kinds of accident types, weather conditions and accident location, which can improve self-adaptability of accident detection methods among different traffic situations. Secondly, we develop a deep neural network model YOLO-CA based on CAD-CVIS and deep learning algorithms to detect accident. In the model, we utilize Multi-Scale Feature Fusion (MSFF) and loss function with dynamic weights to enhance performance of detecting small objects. Finally, our experiment study evaluates performance of YOLO-CA for detecting car accidents, and the results show that our proposed method can detect car accident in 0.0461 seconds (21.6FPS) with 90.02% average precision (AP). In additionally, we compare YOLO-CA with other object detection models, and the results demonstrate the comprehensive performance improvement on the accuracy and real-time over other models.
Model Free Approach to Adaptive Parameters Optimisation using Adaptive Decaying Merit Score (ADMS): A Preliminary Study

Adaptive parameter control is a proven method to increase efficiency of any optimisation algorithm and have has been universally proven on various benchmark problems. In this research report, a Universal model free method of adaptive control of optimisation parameters was proposed. The goal of the research is to explore the method that can be used for any model of optimisation algorithm (‘Swiss army knife’ approach). The proposed method utilises a score system in which points are rewarded for improvement found on individual agent performance while non-improving will experience a score-decay. The proposed method was tested on two types of common optimisation algorithm, Truncated Lěvy flight and the Differential Evolution (DE). This Adaptive parameter strategy is solely based on search agent's performance and hence is not algorithm specific (model free). As a preliminary study, this report will only focus on Lower Dimensional problems (D=10) using CEC 2014 benchmark test sets. The results showed that it work well on DE but gave mediocre results for Le$\check{v}$y Flight when compared with their static parameter counterparts. This showed performance varies across algorithms types but verified the adaptive capability of the proposed method.
Analysis User Acceptance Of Wonderful Indonesia Application Using Technology Acceptance Model (case study: Indonesian Ministry Of Tourism)

Tourism is one of the most potential sectors in Indonesia. With the rapid growth of technology, Indonesia ministry of Tourism has developed Wonderful Indonesia applications as information system technology which intended to communicate and promote popular destinations in Indonesia. However, preliminary study shows that the usage of Wonderful Indonesia application is still low. Extended Technology Acceptance Model (TAM) is used to examine user acceptance of Wonderful Indonesia Application. This study surveyed 100 respondents who are the active users of Wonderful Indonesia Application during 12 previous months. The result shows that perceived ease of use has significant effect on perceived usefulness, attitude and intention of use. While, Entertainment has significant moderating effect of user’s attitude and intention of use. Lastly, Intention of use also has significant effect actual system use. The results will provide recommendations to Wonderful Indonesia application developers so that user’s acceptance and adoption of the application will be increase in the future.
Routing Table Decomposition in Line Cards with Different Capacity

Researchers in routing table decomposition have got many achievements but never considered the situation of line card with different capacity. Generally, decomposed storage algorithm pursues storing uniformity in order to enhance the utilization of storage. In line cards with different capacity, measuring the uniformity needs a new standard. Proposed Routing Table Decomposition of Line Cards with Different Capacity, namely RELI, decomposes the binary route tree created by all route entries to group the whole route table, making the stored saturation of line cards even and converging to a threshold value. It helps to decompose the routing table into line cards with different capacity uniformly, control the number of items in line cards, and reduce the number of locating route as possible. Furthermore, RELI algorithm can also decompose and store routing table into line cards with same capacity.
Malicious Domain Names Detection Algorithm Based on Lexical Analysis and Feature Quantification

Malicious domain names usually refer to a series of illegal activities, posing threats to people’s privacy and property. Therefore, the problem of detecting malicious domain names has aroused widespread concerns. In this study, a malicious domain names detection algorithm based on lexical analysis and feature quantification is proposed. To achieve efficient and accurate detection, the method includes two phases. The first phase checks an observed domain name against a blacklist of known malicious uniform resource locator (URLs). The observed domain name is classified as being definitely malicious or potentially malicious based on its edit distances to the domain names on the blacklist. The second phase further evaluates a potential malicious domain name by its reputation value that represents its lexical feature and is calculated based on an N-gram model. The top 100,000 normal domain names in Alexa are used to obtain a whitelist substring set using the N-gram method in which each domain name excluding the top-level domain is segmented into substrings with the length of 3, 4, 5, 6 and 7. The weighted values of the substrings are calculated according to their occurrence counts in the whitelist substring set. A potential malicious domain name is segmented by the N-gram method and its reputation value is calculated based on the weighted values of its substrings. Finally, the potential malicious domain name is determined to be malicious or normal based on its reputation value. The effectiveness of the proposed detection method has been demonstrated by experiments on public available data.
Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges

In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to their unmatchable performance in several applications, such as image processing, computer vision, and natural language processing. However, as DNNs grow in their complexity, their associated energy consumption becomes a challenging problem. Such challenge heightens for edge computing, where the computing devices are resource-constrained while operating on limited energy budget. Therefore, specialized optimizations for deep learning have to be performed at both software and hardware levels. In this paper, we comprehensively survey the current trends of such optimizations and discuss key open research mid-term and long-term challenges.
Heart Rhythm Abnormality Detection from PCG Signal

Cardiovascular diseases are the major cause of mortality and PCG provides a non-invasive way to monitor the heart. In this paper, we give a unique approach to classify Normal and Abnormal heart rhythms using Machine Learning. The heart sounds are digital signals recorded from electronic stethoscope. In the initial phase Signal Quality assessment and feature extraction is done after which we explore different data models to find the relation between the features and the results, achieving poor results. In the second phase, audio files are segmented into Systolic and Diastolic phases using a Logistic Regression-Hsmm These segments of systoles and diastoles are then individually analyzed and individual feature extraction is done. In the segmentation process a lot of de-noising is also done removing the background noises. This approach yields an accuracy of 79% which concludes that analyzing the heart signal at Systolic and Diastolic phase is a very essential step to solve this problem.
Comparative Study of Main Determinants of Entrepreneurship Intentions of Business Students

Entrepreneurship is a major driver of modern economy. Students can be seen as potential future entrepreneurs. It is therefore very important to examine the main determinants of intentions for entrepreneurial activity. The study is based on the Theory of Planned Behaviour. According to her, the main determinants that affect the intention of certain behavior are attitudes, subjective norms, and perceived behavioral control. The goal of the present paper is to analyze the influence of these three determinants on entrepreneurship intentions and career choice of business students in the three countries. The research encompasses a sample of business students trained in the Kazimierz Pulaski University of Technology and Humanities – Radom, Poland, Prof. Ass. Zlatarov – Burgas, Bulgaria and National University of Life and Environmental Sciences of Ukraine – Kiev, Ukraine.
Research on Deadbeat Current Prediction Vector Control System of Axial Flux Permanent Magnet Synchronous Motor for Electric Bus Based on Efficiency Optimal Torque Distribution Method

In order to improve the cruising range of electric bus, this paper studies the deadbeat current prediction vector control system of axial flux permanent magnet synchronous motor (AFPMSM) for electric bus based on the optimal torque distribution method. Firstly, the mathematical model of the three stators-double rotors AFPMSM is established. Secondly, in order to improve the high efficiency range, the efficiency optimal torque distribution method is proposed based on the average torque distribution method and the back propagation (BP) neural network is used to find the optimal torque distribution method. Then a current control strategy based on deadbeat current prediction control is proposed to improve the torque tracking characteristics. Finally, a drive control system is developed for the proposed control strategy, and experimental research and vehicle testing are carried out. The experimental results show that the BP neural network-based torque distribution method designed in this paper increases the high efficiency range of the drive system and improves the cruising range of the electric bus. The drive system using a current controller based on deadbeat current prediction control exhibits good dynamic and steady state performance.
An Improved Scheme of Victim Replication in Tiled Chip Multiprocessors

The last level cache (LLC) in the shared configuration increases the effective cache capacity by not allowing replication but causes a long on-chip access latency when the data is on a remote tile. The previously proposed victim replication scheme allowed replicating victims evicted from L1 to its local LLC slice in order to reduce the on-chip access latency of subsequent L1 misses. However, this proposal loses sight of the impact of locality in all levels of cache and the L1 victim re-reference interval on replication and replicates lots of useless replicas, which results in limited performance improvements. In this paper, we propose a novel victim selective replication scheme based on L1 temporal locality, LLC reuse locality and L1 victim re-reference interval (VSR_TRV). We selectively replicate a victim that is detected as a reuse in a short L1 victim re-reference interval or recognized as the first time LLC access but only receives one L1 access, and filter out the replication of a victim that is recognized as the first time LLC access. The experimental results show that VSR_TRV can improve performance by 4.67% on average and by 11.22% at best over the previously proposed VR scheme. In addition, our proposal only incurs 1.48% storage overhead compared to that of the baseline system.
On the Use of Equivalence Classes for Optimal and Sub-Optimal Bin Covering

Bin covering is an important optimization problem in many industrial fields, such as packaging, recycling, and food processing. The problem concerns a set of items, each with its own value, that are to be collected into bins in such a way that the total value of each bin, as measured by the sum of its item values, is not lower than a target value. The optimization problem concerns maximizing the number of bins. This is a combinatorial NP-hard problem, for which true optimal solutions can only be calculated in specific cases, such as when restricted to a small number of items. To get around this problem, many suboptimal approaches exist. This paper describes a formulation of the bin covering that allows to find the true optimum for a rather large number of items, over 1000. Also presented is a suboptimal solution, which is compared to the true optimum and found to come within less than 10% of the optimum.
A One-Cycle FIFO Buffer for Memory Management Units in Manycore Systems

We present an efficient synchronous first-in first-out (FIFO) buffer for enhanced memory management units and inter-core data communication in manycore systems. Our design significantly reduces hardware overhead and eliminates latency delays by using both the rising and falling clock edges during read and write, which makes our design suitable for increased processing element (PE) utilization by increasing the memory bandwidth in complex network and system on-chip solutions. Compared to prior work, our design can operate 5X faster at the same supply voltage, or up to 44X faster with a 2.5X increase in supply voltage. Our design's total power consumption is 7.8 mW with a total transistor count of 34,470.