Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Analysis of High Performance Multiplier Circuit

Multiplier is of the most important blocks of many VLSI application. So, it is required to design high performance multiplier to boost up the performance of those circuits and systems. In this work, a high performing Multiplier has been designed by using Wallace tree algorithm. The multiplier has been designed in three modules. Initially partial products are generated followed by partial products processing. Finally, final addition is computed in the 3rd module. Partial products have been generated by using AND gates. Partial products are computed by using Wallace tree logarithm. Final addition has been done by fast adder. The performance of the proposed multiplier circuit is evaluated by using 90nm CMOS technology at the Synopsys tool. The performance of the same is compared conventional multiplier circuits. The performance of the proposed adder has been found to be satisfactory.
Understanding Body Channel Communication : A review: from history to the future applications

Increasing requirements of convenient and higher energy efficiency near human body gives more attention to body channel communication (BCC) technology. BCC, utilizing human body as communication medium, can take advantage of both wireline and wireless communication in body area. In this paper, several underlying key considerations of BCC would be introduced from its fundamental basis to target applications. Paper will starts from the comparison with general RF technology and BCC, and introduce historical review of BCC researches. Two analogical perspective to understand BCC and several challenges in its design will be discussed. After that, with following recent transceiver IC design trends, design efforts and their achievements would be covered. After introducing future applications promising for BCC, this paper would be concluded.
LBCN: Load Balancing based on Congestion Notification in CRAN Networks for 5G Transport

The progress of modern smart cities has put more requirement on the development of mobile communication, especially the fifth generation (5G) network. Cloud Radio Access Network (CRAN) is an emerging network architecture designed to deal with the bursty and massive 5G traffic. In CRAN networks, where multipath is often the case, an efficient load balancing algorithm is needed to spread load among the links, to provide users promised quality of service (QoS). In the application of 5G communications such as smart cities, the delay of packets and flow completion time (FCT) are highly concerned because QoS is closely allied to these metrics. We propose a load balancing algorithm named LBCN, using two waterlines of buffer occupancy to detect congestion and taking both local and neighbor buffer congestion information into consideration. We simulate LBCN in a 3-tier Clos network. Simulation results shows that our algorithm achieves similar performance to CONGA, shorten the FCT of large flows with low priority, which is a main data source in 5G traffic. It also proves that LBCN balances the utilization of out ports better than ECMP and CONGA.
Research on the Effects of Educational WeChat Official Accounts

This study examined the effects of educational WeChat official accounts from five main aspects of users' behavior and learning process: recognizing, following, reading, sharing and learning/reflecting. Two factors important to the effects of educational WeChat official accounts were identified: users' gratifications and features of educational WeChat official accounts. The results of this study indicated that both the degree of users' gratifications and features of educational WeChat official accounts were positively related to the effects of educational WeChat official accounts.
Millimeter-Wave Transceivers for Wireless Communication, Radar, and Sensing : (Invited Paper)

Due to growing demand for higher data rates in wireless communication, high resolution requirement in radars, and emerging sensing applications, mm-wave frequency bands have become very attractive in recent years. Architectures and circuits of mm-wave transceivers are described and comparison of process technologies for mm-wave IC design is presented. Critical mm-wave circuit blocks and example implementations such as 60 GHz phased array, 28 GHz front-end, 79 GHz PMCW radar and 145 GHz FMCW radar are discussed and future trends are identified.
Towards the Creation of a Tool for Identifying Personality in Virtual Learning Environments

Nowadays, disorders and diseases associated with emotional states and personality are increasingly common. Studies indicate that the identification of the personality of an individual can help in the treatment of these disorders. This identification is often performed with the help of affective lexicons. The main objective of this work is to initiate a study to create a tool capable of identifying the personality of students in virtual learning environments with the help of an affective lexicon in Brazilian Portuguese. For this purpose, it is intended, initially, to perform the inference of personality from the texts of users of a Brazilian social network. The results achieved are considered relevant and promising.
A Fast Object Proposal Assessment Method Based on Edge Directions

Closed boundary is a powerful cue for objectness assessment in object proposal generation. We observe that a closed boundary should consist of as many as possible block edge directions when it is divided into small blocks, and the amount of different block edge directions should be roughly the same in consideration of the refl ectional symmetry of an object. Based on this observation, we propose a fast object proposal generation method, where a novel edge-directions-based objectness feature and a simple objectness measurement in terms of entropy are pro-posed for effi cient objectness assessment. The objectness feature is designed to describe the distribution of edge orientations. For computational reasons, each block edge direction is represented by using the non-normalized Haar transform (NHT) coeffi cients which requires only several integer addition and subtraction operations. Experiments on the Pascal VOC2007 dataset show that the proposed method takes the least running time which requires only 0.03 second to generate a set of high-quality object proposals for an image, and achieves comparable performance compared with state-of-the-art methods.
Effect of Enclosure on Path Loss of Long Range Wi-Fi Signal

Our research center has developed and deployed a low-cost communication infrastructure, OceanNet, for marine fishermen using long range Wi-Fi for backhaul. In the current deployments, the transceiver on the fishing vessel is not enclosed and is therefore exposed to the atmospheric salinity. Even though the equipment is built to last under these harsh conditions, it is a worthwhile exercise to assess the impact of keeping it inside an enclosure. This work analyses the effect of placing the transceiver inside one cm thick PVC enclosure in terms of the additional path loss. The variation in path loss is measured using an experimental setup and is also studied using a path loss model. The results show that an additional 2 dB path loss is incurred due to the enclosure. Therefore, it is decided not to use an enclosure in our deployments for now.
A Recommendation Method of Educational Resources Based on Knowledge Structure

With the development of online learning platform, network education resources become more and more rich. It makes online learners easier to choose the time, place and content of learning. Because of the huge number of educational re-sources in learning platform, it is difficult for learners to find learning re-sources quickly and efficiently. The user-based collaborative filtering recommendation algorithm is improved by knowledge structure. A new method of education resource recommendation is brought up in this paper. In this method, not only the recommendation result accords with the learner's interest, but also accords with the existing knowledge system of the learner. It better meets the learner's individualized learning needs and improves the learning efficiency. In this paper, the experiment was carried out by the method of controlled experiment. The experimental results are evaluated through two aspects of correlation score and learning process score, and good experimental results have been obtained.
DC Link Capacitor Voltage Balancing Method for Diode-Clamped Multilevel Converters

This paper proposes a new capacitor voltage balancing method for diode-clamped multilevel inverters. In this method, proper redundant switching state is selected according to clamp points' voltages. The proposed method is independent of the phase and level numbers and is computationally optimized for practical implementation. The method is further investigated using space vector modulation with various operating modes, balancing methods, and control method. The performance and effectiveness of the proposed method are confirmed through simulation studies.
Hardware Accelerators for IOTA Cryptocurrency

IOTA cryptocurrency is a distributed solution for secure messaging and micropayments between Internet-of Things devices. The authors introduced open source hardware accelerators for Curl hash function and proof-of-work computations to increase the processing rate of IOTA transactions. The structure and operation principle of both accelerators are described. Proposed accelerators have been implemented on FPGA and integrated into ARM-based System-on-Chip. The characteristics of the system have been evaluated. Compared to official software implementation the proposed hardware accelerators demonstrate x2100 speed up on the creation of IOTA transactions.
On the Occupancy Measurement and Analysis for Residential Applications

Analysis of buildings occupancy has become a relevant research field following energy management and smart grid trends. Occupancy can be associated with energy needs, and therefore a good knowledge of occupancy behavior can substantially help to manage better energy sources and to optimize overall consumption. In this work, we analyze the use of commonly employed occupancy measurements under the residential scenario. We present the design methodology, the implementation, the experimental tests and occupancy analysis of a multi-sensor measurement prototype. We propose a signal processing methodology based on Haar wavelets and multivariate analysis using the Normalized Cross-Correlation and Principal Component Analysis to improve the linear regression model of occupancy.
Unequal Clustering and Routing Algorithm Based on Dynamic Topology for WSN

For a large-scale wireless sensor networks (WSN), multi-hop communication will cause “energy holes” phenomenon. In order to improve the stability of WSN performance and balance the overall network load, a dynamic unequal clustering and routing algorithm for WSN (DUCR) is proposed in this paper. First, an unequal clustering algorithm is put forward based on WSN dynamic topology. Second, a WSN routing algorithm based on dynamic unequal clustering with fault tolerance mechanism is proposed to maximize parallel transmission. In the dynamic CH rotation phase, the base station is notified to re-clustering only when the number of cluster members is beyond the threshold, thereby adapting to the dynamic topology while reducing the overhead of frequent clustering. Extensive simulation experiments show that the DUCR can balance the network load, improve the stability of WSN performance and prolong the network lifetime.
Multi-modality fusion of floor and ambulatory sensors for gait classification

In a case study of gait classification from floor and ambulatory sensors, we compare results with data from each modality. The automatic extraction of features is achieved by Principle Component Analysis and Canonical Correlation Analysis, the latter performing better even with a reduced number of components used. Non-linear classifiers are most efficient for fused features. With a Kernel Support Vector Machine around 94% accuracy is demonstrated, improving over the 87% and 79% accuracies obtained with separate floor and ambulatory sensor data, respectively.
Small Object Detection Using Context Information Fusion in Faster R-CNN

Currently, most of the object detection research focuses on detecting a big object covering large part of the image. The problems of detecting the small object covering small part of the image are largely ignored. The difficulty of small object detection is that small objects have large quantity and less pixel (less information) and cover small part of the images. In this paper, we aim at improving the accuracy of small object detection. Firstly, we use a subset of the COCO [1] dataset to build a benchmark database. This benchmark database is specifically designed to evaluate the performance of object detection algorithms on small object detection. Secondly, we improve Faster R-CNN [2]. The improvements include a more flexible context information integration method. The experiments show that the improved Faster R-CNN algorithm has a good performance on the accuracy and recall rate of small object detection. Our small object detection algorithm is able to strike the balance between detection speed and detection accuracy.
Signal decoupling in digital holography via compressive sensing

Digital holography (DH) could realize three dimensional (3-D) imaging since it records the wavefront induced by an object volume. However, DH suffers from two inherent drawbacks for 3-D imaging: the twin images and cross talks between different depths. Both these two terms severely trouble DH because they couple with the reconstructed signal, particularly under microscopic configuration. In this work, we briefly review the signal decoupling techniques in DH and introduce the achieved micron-level depth-resolved uncoupled signal reconstruction via compressive sensing (CS). As an optimization strategy for signal reconstruction, CS is capable of filtering out the coupled signals by sparsity constraint. The CS based signal decoupling in DH is analyzed and verified by numerical simulations and optical experiments.
Harmonics Mitigation in Cascaded Multilevel PV Inverters During Power Imbalance Between Cells

This paper presents a grid connected multilevel topology for photovoltaic (PV) systems. Usually, multilevel converters for PV application suffer from a distorted output current and voltage when the submodules are not subjected to an even solar irradiance. The difference in submodules irradiance results in different submodules duty cycles when maintaining the maximum power point tracking (MPPT). The distortion of the output current is proportional with the difference of the cells duty cycles. To this regard, a multilevel topology for PV applications is proposed along with a control and modulation strategy. In this proposed topology, H6 bridge-based cell is used instead of an H-bridge one. In case of solar irradiance mismatch, the proposed converter injects power with less voltage from the shaded cells without altering the PV voltage, and hence, the MPPT. This modification allows retaining a tantamount duty cycle in all cells whatever the meteorological conditions are present. To test the effectiveness of the proposed idea, a detailed simulation model was set up. The results show that the proposed concept provides a significantly improved output current quality compared to the cascaded H-bridge topology.
SOSI: System of Systems Interoperability

Purpose To present results of an SEI IR&D project dealing with system of systems interoperability. We wanted to: • Identify critical interoperability issues • Gain insight into programs that are solving critical problems • Develop recommendations for future research 3 SOSI Motivation Interoperability between systems has become increasingly important – no modern system stands alone. • Future Combat System • Air Operations Center • Navy battle groups • Joint battle forces Improved interoperation will not happen by accident.
Scalable Byzantine Reliable Broadcast (Extended Version)

Byzantine reliable broadcast is a powerful primitive that allows a set of processes to agree on a message from a designated sender, even if some processes (including the sender) are Byzantine. Existing broadcast protocols for this setting scale poorly, as they typically build on quorum systems with strong intersection guarantees, which results in linear per-process communication and computation complexity. We generalize the Byzantine reliable broadcast abstraction to the probabilistic setting, allowing each of its properties to be violated with a fixed, arbitrarily small probability. We leverage these relaxed guarantees in a protocol where we replace quorums with stochastic samples. Compared to quorums, samples are significantly smaller in size, leading to a more scalable design. We obtain the first Byzantine reliable broadcast protocol with logarithmic per-process communication and computation complexity. We conduct a complete and thorough analysis of our protocol, deriving bounds on the probability of each of its properties being compromised. During our analysis, we introduce a novel general technique that we call adversary decorators. Adversary decorators allow us to make claims about the optimal strategy of the Byzantine adversary without imposing any additional assumptions. We also introduce Threshold Contagion, a model of message propagation through a system with Byzantine processes. To the best of our knowledge, this is the first formal analysis of a probabilistic broadcast protocol in the Byzantine fault model. We show numerically that practically negligible failure probabilities can be achieved with realistic security parameters.
Matching Reads to Many Genomes with the r-Index

The r-index is a tool for compressed indexing of genomic databases for exact pattern matching, which can be used to completely align reads that perfectly match some part of a genome in the database or to find seeds for reads that do not. This article shows how to download and install the programs ri-buildfasta and ri-align; how to call ri-buildfasta on an FASTA file to build an r-index for that file; and how to query that index with ri-align.
Sub-millimetric ultra-low-field MRI detected in situ by a dressed atomic magnetometer

Magnetic resonance imaging (MRI) is universally acknowledged as an excellent tool to extract detailed spatial information with minimally invasive measurements. Efforts toward ultra-low-field (ULF) MRI are made to simplify the scanners and to reduce artifacts and incompatibilities. Optical atomic magnetometers (OAMs) are among the sensitive magnetic detectors eligible for ULF operation; however, they are not compatible with the strong field gradients used in MRI. We show that a magnetic-dressing technique restores the OAM operability despite the gradient, and we demonstrate submillimetric resolution MRI with a compact experimental setup based on an in situ detection. The proof-of-concept experiment produces unidimensional imaging of remotely magnetized samples with a dual sensor, but the approach is suited to be adapted for 3-D imaging of samples magnetized in loco. An extension to multisensor architectures is also possible.Magnetic resonance imaging (MRI) is universally acknowledged as an excellent tool to extract detailed spatial information with minimally invasive measurements. Efforts toward ultra-low-field (ULF) MRI are made to simplify the scanners and to reduce artifacts and incompatibilities. Optical atomic magnetometers (OAMs) are among the sensitive magnetic detectors eligible for ULF operation; however, they are not compatible with the strong field gradients used in MRI. We show that a magnetic-dressing technique restores the OAM operability despite the gradient, and we demonstrate submillimetric resolution MRI with a compact experimental setup based on an in situ detection. The proof-of-concept experiment produces unidimensional imaging of remotely magnetized samples with a dual sensor, but the approach is suited to be adapted for 3-D imaging of samples magnetized in loco. An extension to multisensor architectures is also possible.
The Value of Including Unimodality Information in Distributionally Robust Optimal Power Flow

To manage renewable generation and load consumption uncertainty, chance-constrained optimal power flow (OPF) formulations and various solution methodologies have been proposed. However, conventional solution approaches often rely on accurate estimates of uncertainty distributions, which may not exist. When the distributions are not known but can be limited to a set of plausible distributions, termed an ambiguity set, distributionally robust (DR) optimization can be used to ensure that chance constraints hold for all distributions in that set. However, DR OPF yields conservative solutions if the ambiguity set is too large. In this paper, we assess the value of using both moment and unimodality information, which shrinks the ambiguity set and reduces conservatism, in DR OPF problems. Most practical uncertainty distributions in power systems are unimodal. Exact reformulations, approximations, and efficient solving techniques were developed in a previous paper. This paper develops an optimal parameter selection approach that searches for an optimal approximation, significantly improving the computational efficiency and solution quality. We evaluate the performance of the approach against existing chance-constrained OPF approaches using modified IEEE 118-bus and 300-bus systems with high penetrations of renewable generation. Results show that including unimodality information reduces solution conservatism and cost without significantly degrading reliability.
Classi-Fly: Inferring Aircraft Categories from Open Data

In recent years, air traffic communication data has become easy to access, enabling novel research in many fields. Exploiting this new data source, a wide range of applications have emerged, from weather forecasting to stock market prediction, or the collection of intelligence about military and government movements. Typically, these applications require knowledge about the metadata of the aircraft, specifically its operator and the aircraft category. armasuisse Science + Technology , the R&D agency for the Swiss Armed Forces, has been developing Classi-Fly, a novel approach to obtain metadata about aircraft based on their movement patterns. We validate Classi-Fly using several hundred thousand flights collected through open source means, in conjunction with ground truth from publicly available aircraft registries containing more than 2 million aircraft. We show that we can obtain the correct aircraft category with an accuracy of greater than 88%. In cases, where no metadata is available, this approach can be used to create the data necessary for applications working with air traffic communication. Finally, we show that it is feasible to automatically detect particular sensitive aircraft such as police and surveillance aircraft using this method.
The Impact of Developer Experience in Using Java Cryptography

Background: Previous research has shown that crypto APIs are hard for developers to understand and difficult for them to use. They consequently rely on unvalidated boilerplate code from online resources where security vulnerabilities are common.Aims and method: We analyzed 2,324 open-source Java projects that rely on Java Cryptography Architecture (JCA) to understand how crypto APIs are used in practice, and what factors account for the performance of developers in using these APIs.Results: We found that, in general, the experience of developers in using JCA does not correlate with their performance. In particular, none of the factors such as the number or frequency of committed lines of code, the number of JCA APIs developers use, or the number of projects they are involved in correlate with developer performance in this domain.Conclusions: We call for qualitative studies to shed light on the reasons underlying the success of developers who are expert in using cryptography. Also, detailed investigation at API level is necessary to further clarify a developer obstacles in this domain.