Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Integrated Control Circuits for mm-Wave Phased Arrays in 55-nm BiCMOS

This paper presents the design of different control circuits for integrated D-band phased-array transceivers, implemented using a 55-nm BiCMOS process. An SPI slave controller is first presented, which is able to control a bank of sixteen 8-bit registers. The design of an integrated ring-oscillator based clock generator is then described, which oscillates at 44 MHz. Finally, a current-steering 8-bit DAC is presented, which provides a current of 0-1.8 mA with a DNL lower than 0.9 LSB. The DAC can be used to generate bias currents for mm-wave phase shifters and amplifiers, as well as to generate self-test signals at a sampling frequency of up to 100 MSPS. The clock and DAC have been implemented on a test chip that occupies 0.5 mm ⨯ 0.4 mm.
Contract-Based Computing Resource Management via Deep Reinforcement Learning in Vehicular Fog Computing

Vehicle fog computing (VFC) is proposed as a solution that can significantly reduce the task processing overload of base station during the peak time, where the vehicle as a fog node contributes idle computing resource for task processing. However, there are still many challenges in the deployment of VFC, such as the lack of specific incentives of resource contribution, high system complexity, and offloading collisions between vehicles when the vehicles are offloading tasks simultaneously. In this paper, we first propose a novel contract-based incentive mechanism that combines resource contribution and resource utilization. Based on that, we propose to use distributed deep reinforcement learning to allocate resources and reduce system complexity. Task offloading method based on the queuing model is also proposed to avoid decision collisions in multi-vehicles task offloading. Numerical experiment results demonstrate that our proposed scheme has achieved a significant improvement in task offloading and resource allocation performance.
SET: Stereo Evaluation Toolbox for Combined Performance Assessment of Camera Systems, 3D Reconstruction and Visual SLAM

Evaluating stereo reconstruction algorithms regardless of camera system and application environment is not sufficient to rate the overall performance of a stereo system. To overcome this the Stereo Evaluation Toolbox (SET) proposes a well-founded selection and comparison approach for stereo systems. We aim at providing one performance score evaluating the generated stereo point cloud, complementary to common benchmarks which solely evaluate stereo algorithms on provided image sets. Using images captured in the desired application environment SET measures and compares performance as interaction of modular camera-algorithm combinations inside a local application scenario. Furthermore an evaluation approach for camera-based visual simultaneous localization and mapping (SLAM) systems is presented for further analysis. We apply SET on the evaluation of different camera systems in 3D reconstruction of indoor and outdoor environments as well as on visual SLAM for person indoor navigation.
A Programmable Approximate Calculation Unit Employing Time-Encoded Stochastic Computing Elements

A programmable calculation unit is proposed on the basis of piece-wise linear approximation. An arbitrary function with single variable is approximately retrieved by piece-wise linear segmentation where parameters for each segment are pre-stored in a look-up table. An improved scheme of time_encoded stochastic computing elements is developed to efficiently carry out the multiplication and summation of a specific segment. The storage of piece-wise linear parameters is realized by a set of compact multi-valued logic flip-flops. The entire calculation processor is designed with 588 MOS transistors in a 0.18um CMOS technology. For proof-of-concept, three of commonly applied non-linear functions including Tanh, exponential, and rectified linear unit are demonstrated as examples. From circuit simulation results, all the exampled functions are retrieved with the average error less than 2.1% by the proposed processor consuming the energy of 2.78pJ in maximum. The calculation speed reaches ~36x of the state-of-art works.
A Basic Study on Communication Characteristics by Bluetooth Low Energy for i-Construction

At present, construction machines such as excavators and cranes are built with communication modules in order to connect to networks. Therefore, the construction machines can be controlled through commands sent via the network to improve productivity. This is called i-construction. Two examples of i-construction are remote control and automatic control systems for the construction machine. In these systems, generally, wireless communication is used. However, the wireless communication characteristics that need to be considered in i-construction are still unclear. In order to clarify, this paper seeks to characterize the characteristics of Bluetooth Low Energy (BLE) communication when used in construction machines. In the evaluations, we use a BLE beacon module. Experimental results show that the BLE beacon module can communicate between the sender in the excavator's attachment and receiver in the cabin. The results show that there is a possibility that the wireless communication systems can be applied for the construction machines.
Inter-Brain Connectivity: Comparisons between Real and Virtual Environments using Hyperscanning

Inter-brain connectivity between pairs of people was explored during a finger tracking task in the real-world and in Virtual Reality (VR). This was facilitated by the use of a dual EEG set-up that allowed us to use hyperscanning to simultaneously record the neural activity of both participants. We found that similar levels of inter-brain synchrony can be elicited in the real-world and VR for the same task. This is the first time that hyperscanning has been used to compare brain activity for the same task performed in real and virtual environments.
Analytical study for the potential solar energy produced in Nibong Tebal

Solar energy is green, free, long lifetime, and low maintenance. It mainly depends on the light produced by the sun and other environmental parameters such as ambient temperature, air pressure, etc. Two main meteorology parameters that affect directly the produced energy of solar arrays are light and temperature. Collecting the meteorology data is critical to study and estimate the potential energy available in any area. Long-time meteorology data collection is necessary to have more accurate estimation of the potential energy. A study of the meteorology data is done at Nibong Tebal, Penang, Malaysia. In this work temperature and irradiance were collected to compute the photovoltaic (PV) sizing.
Understanding the Role of Social Media in Backpacker Tourism

The Web 2.0 revolution has affected many areas of society and tourism is not exception to that. Travel 2.0 replaced the first, booking-oriented wave with a crowdsourced, fully interactive one that aids travel planners in making decisions via user-generated content (UGC). In this work, we study how social media are shaping backpackers' experiences by employing sentiment analysis on their blogs. Into the bargain, there are two main research objectives which consist on providing insights into the contemporary backpackers' attitudes and examining the potential implications of their UGC to assist travel-related marketing strategies along with product and service improvement. Such information is found in word-based blog entries and is extracted and analyzed through a combination of methods, specifically sentiment analysis and digital ethnography.
Analysis of factors influencing the impression of speaker individuality in android robots*

Humans use not only verbal information but also non-verbal information in daily communication. Among the non-verbal information, we have proposed methods for automatically generating hand gestures in android robots, with the purpose of generating natural human-like motion. In this study, we investigate the effects of hand gesture models trained/designed for different speakers on the impression of the individuality through android robots. We consider that it is possible to express individuality in the robot, by creating hand motion that are unique to that individual. Three factors were taken into account: the appearance of the robot, the voice, and the hand motion. Subjective evaluation experiments were conducted by comparing motions generated in two android robots, two speaker voices, and two motion types, to evaluate how each modality affects the impression of the speaker individuality. Evaluation results indicated that all these three factors affect the impression of speaker individuality, while different trends were found depending on whether or not the android is copy of an existent person.
An Approximate HEVC Intra Angular Prediction Hardware

In this paper, an approximate High Efficiency Video Coding (HEVC) intra angular prediction technique is proposed for reducing area of HEVC intra prediction hardware. The proposed approximation technique uses closer neighboring pixels instead of distant neighboring pixels in intra angular prediction equations. It causes 0.0569% bit rate increase and 0.0028 dB PSNR loss on average. In this paper, an approximate HEVC intra angular prediction hardware implementing the proposed approximation technique is also proposed. FPGA and ASIC implementations of the proposed approximate hardware can process 24 and 40 quad full HD ( $3840 \times 2160$ ) video frames per second, respectively. The proposed approximate HEVC intra angular prediction hardware is the smallest and the second fastest HEVC intra prediction hardware in the literature. It is ten times smaller and 20% slower than the fastest HEVC intra prediction hardware in the literature.
A Color Moments-Based System for Recognition of Emotions Induced by Color Images

Images can evoke emotions in the viewers, but are rarely indexed based on their emotional contents because emotions are subjective. Given the ubiquitous use of images, it would therefore be desirable to have an automated classification system for recognition of human emotions induced by the visual content of images (REVC), which can index images based on the emotions that they may induce. The focus of research in REVC systems has been on identification of features that evoke different emotions and their strength. These features are then used to develop REVC systems that can accurately predict emotions, which a given image may evoke. This paper investigates the performance of a color moment-based REVC system, by identifying the suitable color moments, and measuring their effectiveness for emotion recognition. Using a combination of these features, a new REVC system is developed. The proposed method outperforms a commonly used benchmark that relies on more than 100 features to predict emotional content of the image, signifying the fact that using combination of multiple features does not necessarily improve the system performance.
Impact of Azimuthal Antenna Pattern Sampling and Variation on Throughput Measurements

Presented is a numerical study of the impact of the antenna pattern sampling resolution on the accuracy of Over-The-Air (OTA) throughput measurements. More specifically, a Line-Of-Sight (Random-LOS) testing scenario is addressed. The communication throughput is evaluated as the Probability of Detection (PoD) of a single bitstream based on the ideal digital threshold receiver model. The maximum deviation of throughput efficiency is computed as a function of the azimuthal sampling resolution for various generic antenna gain patterns. The coefficient of variation of the antenna gain pattern is introduced to evaluate the impact of the azimuthal sampling. The presented approach can be used to define the sampling resolution of the throughput performance of a device under test in Random-LOS OTA.
The Pod Assignment Model and Algorithm in Robotic Mobile Fulfillment Systems

This paper studies the pod assignment in the Robotic Mobile Fulfillment Systems which is a parts-to-picker storage system where robots carry movable shelves, called pods, to stationary pickers. On the basis of deciding which product to put in which pod depending on the product correlation, we establish a mathematical model with the objective of minimizing the total picking distance to decide which pod to put in which position. A two-stage hybrid algorithm is proposed to solve the model. In the first stage a Greedy Algorithm is designed to generate initial solution which consider the pod correlation. In the second stage, a simulated annealing algorithm is used to optimize the initial solution and get the final pod layout results. The experimental results show that the pod assignment model and algorithm optimization scheme proposed in this paper can improve the order picking efficiency.
Comparison of Machine Learning Classifiers for dimensionally reduced fMRI data using Random Projection and Principal Component Analysis

Machine learning has opened up the opportunity for understanding how the brain works. In this paper, functional magnetic resonance imaging (fMRI) data are analyzed with reduced dimension. We have carried out a performance comparison of random projection (RP) and principal component analysis (PCA) with different number of components of fMRI data. In addition to that, six different types of machine learning algorithm have been used. In particular, the Haxby dataset is chosen for our experiment. The dataset comprises 9 classes for object recognition. 10-fold cross validation step has been employed. We have discovered that RP outperforms PCA when the former is paired with logistic regression, Gaussian Naive Bayes and linear support vector machine. The best pair for this study was found to be PCA and k-nearest neighbors. Nevertheless, each algorithm was found to have its own strengths for fMRI classification approach.
An Efficient Anonymous Reputation System for Crowd Sensing

Recently, crowd sensing has been intensively researched, due to the rapid growth of sensor-integrated mobile devices. Crowd sensing is a participatory sensing service where a server gathers and analyzes sensing data submitted from mobile devices of lots of users. In crowd sensing, the user's anonymity is desired, since the server gathers sensitive data from the participants including their GPS locations and moving path. However, the anonymous data submission may compromise the trust of the sensing data, because anonymous users may submit inappropriate sensing data, but they cannot be traced. Therefore, as the system to achieve both anonymity and trust in crowd sensing, ARTSense has been proposed. In the system, the trust of the sensing data is assessed on the sensed environment, other users' sensing, and the reputation of the user, and furthermore the reputation of the user is anonymously managed on the feedback from the trust assessment for the data. However, the anonymous reputation system of ARTSense has the efficiency problem, i.e., the user needs to wait a random time after the data submission phase before requesting the reputation update, which causes the communication delay. In this paper, we propose an efficient anonymous reputation system for crowd sensing, which can be integrated to the trust assessment in ARTSense. In the proposed system, during the data submission, the reputation update is anonymously completed. This is because the server does not manage the reputation of each user, but each user manages his/her reputation in the user side, where the the validity of the reputation is ensured by a certificate and anonymously checked by zero-knowledge proofs. Therefore, the proposed system achieves the better efficiency with no delay.
A New C&C Channel Detection Framework Using Heuristic Rule and Transfer Learning

A great many of botnet detection methods focus on recognizing the significant C&C channels. Most of them require a C&C training set to build a behavior detection model. However, when lacking such training set for new or unknown botnets, these methods may become inefficient or even invalid.To overcome it, we propose a new general framework for C&C channel detection. It neither needs us to know the families of bots or prepare a training set nor requires deploying malicious activity monitors. Also, it is capable of mining useful knowledge from the historical dataset to boost its detection performance. In our framework, we put forward a clustering method and several heuristic rules to aggregate and label partial C&C traffic, a sample selection function to mine useful historical knowledge and a transfer learning based model to find other C&C channels. We evaluated our framework on two datasets and achieved the best C&C F-measure of about 0.886 and 0.960 respectively. Moreover, the comparison result further indicates its performance advantage and better behavior learning ability.
Harmonic Analysis Using Wavelet Transform for Active Power Filter Applications

Various types of non-linear loads are used to produce the harmonics to generate the output becomes sine waveforms of supply current and voltage. Due to harmonics are create some problems of equipment to damage. It may lead equipment to overheat and sometimes cause damage due to harmonics. Thus the power quality of the system is improved by shunt active power filter and to eliminate harmonics of the load current. The proposed method is used to extract signal from the load current using Wavelet transform. This method improves the quality of extracted fundamental component and transient response. The various wavelet transform filters are used for wavelet decomposition technique. To obtain result for this paper, the MATLAB / Simulink was used as a simulation tool and wavelet tool. The attained effects are within the recommended IEEE-519 standard. The comparison of various wavelet transform filters results to be quite satisfactory to mitigate harmonics.
Finding the Spreaders in a Graph Database

The use of graph databases has increased significantly in the last decades, especially for storing data in social networks (e.g. Facebook, Linkedin, etc.). An important question that has received considerable research is finding the group of spreaders or the influential nodes in a social network. These nodes are defined to be the set of nodes who collectively can reach all other nodes in the network diffusing and spreading information. These nodes can be used to drive the whole network to a certain opinion or idea, therefore finding these nodes can be of high interest to politicians and opinion makers and drivers. In this paper, we provide an algorithm that can identify the minimal set of spreaders in a database graph. The algorithm is based on first extracting the strongly connected components of the graph, and then using these to identify the set of bases of the directed graph. Each base can be used as the set of spreaders that will allow a piece of information to flow and reach all other users in the database.
Evapotranspiration estimation using vegetation index and surface reflectance SWIR Landsat-8 combination on various land cover

Accurate information of evapotranspiration (ET) will provide important information in irrigation systems. ET can be estimated using simple method using satellite data and field measurements. Field measurements was conducted in Air Buluh Village, Bangka Belitung Island on seven land cover (three different palm-oil ages, bare land, agricultural land, reclamation, shrub) by using the Penman-Monteith method. Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), spectral reflectance short-wave band (SWIR), were used from satellite data derived from Landsat 8-OLI/TIRS data. The analysis shows that the vegetation index has positive correlation with ET field measurements with a correlation only 12-15% and errors 1.3 mm/day, with SAVI has the lowest correlation, followed by EVI and NDVI. SWIR has negative correlation with 7% coefficient correlation. SWIR is a sensitive band with surface water content. SWIR increases with decrease of surface water content. Estimation of ET were built by using simple regression between ET and vegetation index and SWIR. Regression result shows that EVI has the highest correlation, 42% followed by NDVI and SAVI, 39%, and 30%. The equation formed by vegetation index and SWIR is: ET=EVI(2.68e6.14 SWIR), ET=NDVI(2.67e 4.57SWIR), ET=SAVI(4.45e4.07 SWIR) ,with P-value < 0.05 for all method with error 1-2.5 mm/day
A Combined DoA-ToA Localization Technique Based on a Broadband Switched Beam Antenna

This paper analyses the performances of a Direction of Arrival (DoA) localization algorithm based on the use of phaseless magnitude data coming from a Time of Arrival (ToA) estimation. The localization system architecture key component consists of a broadband switched beams antenna (SBA) connected to a corresponding broadband radio front-end. The receiver collects sample data being synchronized with the antenna beam, while both ToA and DoA estimations rely only on scalar data. Differently from the available literature, the proposed system doesn't locate the DoA by implementing the Time Difference of Arrival (TDoA) paradigm, but instead this is made possible by using the phaseless data of the impinging signal power, exploiting the signal magnitude gradient between SBA elements, a byproduct of the ToA estimation. Experimental results demonstrate how different algorithms lead to an angular precision with a mean error inferior to 3°.
Tiny Fusion: Tiny Deep Convolutional Neural Network for Real-time Image Fusion

Since convolutional neural networks (CNNs) were used in image fusion field, they have showed state-of-the-art quality beyond traditional methods. However, the existing CNN fusion models have a high computational cost and require high memory capacity, which is impractical for embedded applications or mobile platforms. Inspired by the efficiency of the lightweight network architecture of SqueezeNet, MobileNet, and ShuffleNet, we propose a tiny fusion method for image fusion, which significantly decreasing the number of operations and memory needed while retaining the same fusion quality. Extensive experimental results indicate that tiny deep neural network architectures can be designed for real-time image fusion that are well suited for embedded scenarios. To the best of our knowledge, our method is the first lightweight network architecture for image fusion.
LGIM: A Global Selection Algorithm Based on Local Influence for Influence Maximization in Social Networks

Influence maximization is to select $k$ nodes from social networks to maximize the expected number of nodes activated by these selected nodes. Influence maximization problem plays a vital role in commercial marketing, news propagation, rumor control and public services. However, the existing algorithms for influence maximization usually tend to select one aspect from efficiency and accuracy as its main improving objective. This method of excessively pursuing one metric often leads to performing poorly in other metrics. Hence, we think that algorithms for influence maximization should make a suitable compromise between computation efficiency and result accuracy instead of excessively pursuing for one metric. Based on the above understanding, this paper proposes a new algorithm, called Global Selection Based on Local Influence (LGIM). The basic idea of the proposed algorithm is following: if a node can influence another node with large influence, the node also has large influence. Therefore, a two-stage filtering strategy of candidate nodes is proposed, which can reduce a large number of running time. Moreover, this paper also proposes a new objective function to estimate the influence spread of a node set. In summarize, the proposed algorithm utilizes the two-stage filtering strategy of candidate nodes to avoid unnecessary computation, and adopts a new objective function to replace time-consuming Monte-Carle simulations. Experimental results on six real-world social networks demonstrate that the proposed algorithm outperforms other four comparison algorithms when comprehensively considering computation efficiency and result accuracy.