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The classification of color image that contain skin pixel is a challenging process. It is challenging due to certain factor such as brightness, background similar to human skin represent the obstacles thereof. The basic approach of skin detection is color based classification. The human skin image is the composition of RGB color. The data values of skin is calculated by randomly sampling RGB color values of different face images which have different age, race, genders sets. However, its performance has not really been high because of the high overlapped degree between “skin” and “non-skin” pixels. This paper describes the linear norm fuzzy based twin support vector machine (LN-FTSVM)) approach for discriminate skin and non-skin data values of skin dataset and to enhance the skin recognition performance. The concept of fuzzy is resolved the unclassified and overlapped data region problems. If no decision function is positive for a data set, this data set is classified into a class with the large membership value. By computational experiments shows that Experiments result shows that the accuracy is improved by linear norm fuzzy based TSVM (LN-FTSVM)) over the conventional methods.

Gain-cell embedded DRAM (GC-eDRAM) is an attractive alternative to traditional SRAM, due to its high-density, low-leakage, and inherent 2-ported operation, yet, its dynamic nature leads to limited retention time that requires periodic, power-hungry refresh cycles. This drawback is further aggravated in scaled technologies, where increased leakage currents and decreased in-cell storage capacitances lead to accelerated data integrity deterioration. However, the emerging approximate computing paradigm utilizes the inherent error resilience of some applications to tolerate data errors. Such error tolerance can be exploited by reducing the refresh rate in GC-eDRAM to achieve a substantial decrease in power consumption, at the cost of an increase in cell failure probability. In this paper, we present the first fabricated and fully functional GC-eDRAM in a 28 nm bulk CMOS technology. The array, which is based on a novel mixed-V t 4T bitcell, can be used in both traditional and for approximate computing applications, featuring a small silicon footprint and supporting high-performance operation. Silicon measurements demonstrate successful operation at 800 Mhz under a 900 mV supply, while retaining almost 30% lower area than a single-ported 6T SRAM in the same technology.

In this paper, a new configuration for a multi-terminal offshore wind farms based VSC-HVDC and large scale photovoltaic system using modified unified adaptive linear neuron (Adaline) algorithm is presented. In the offshore station, the permanent magnet synchronous generator (PMSG)-based wind turbines are tied to the bus via back-to-back converters and a step-up transformer. The large scale photovoltaic power is connected to the dc bus between the offshore voltage source converters (VSC) and onshore VSC-HVDC stations via a modular multilevel converter (MMC). The Power management and the synchronization between two buses are achieved by controlling the two VSC converters. The performance of the topology proposed is tested using Matlab/Simulink during permanent operation mode and in the presence of DC fault.

In nature, many species became extinct as they could not adapt quickly enough to their environment. They were simply not fit enough to adapt to more and more challenging circumstances. Similar things happen when algorithms are too static to cope with particular challenges of their "environment", be it the workload, the machine, or the user requirements. In this regard, in this paper we explore the well-researched and fascinating family of adaptive indexing algorithms. Classical adaptive indexes solely adapt the indexedness of the data to the workload. However, we will learn that so far we have overlooked a second higher level of adaptivity, namely the one of the indexing algorithm itself. We will coin this second level of adaptivity meta-adaptivity. Based on a careful experimental analysis, we will develop an adaptive index, which realizes meta-adaptivity by (1) generalizing the way reorganization is performed, (2) reacting to the evolving indexedness and varying reorganization effort, and (3) defusing skewed distributions in the input data. As we will demonstrate, this allows us to emulate the characteristics of a large set of specialized adaptive indexing algorithms. In an extensive experimental study we will show that our meta-adaptive index is extremely fit in a variety of environments and outperforms a large amount of specialized adaptive indexes under various query access patterns and key distributions.

Machine vision based automatic apparatus is a necessary component of an automated production line. In this paper, taking can-end surface defect detection as an example, we presented a visual inspector and proposed a new surface defect detection framework. Firstly, the Region of Interest (ROI) of the input image is got by circle detection and prior radius knowledge of can-end. Then, the saliency map and texture filtering image are computed to overcome the problems of irregular texture and non-uniform illumination. Finally, all suspected defects are obtained by saliency map segmentation, connected component analysis. Following, the size, the average saliency value and the mean texture filtering value of each suspected defective region are calculated, and the 3-dimensional features are used to distinguish true defects in the central panel region of can-end. We employ the designed equipment to grab can-end images and do tests divided into four groups on the grabbed images. The accuracy is 98.16% and the execution time is about 65.7 milliseconds. Experimental results demonstrate that the proposed method can inspect faint and low contrast defect accurately. Meanwhile, the proposed method is simple and computationally efficient and meets the high speed of industrial automation.

Elasticity makes cloud computing an attractive platform for executing complex large-scale expensive dataflows, as it enables different trade-offs between execution time and monetary cost, by varying the number of resources to be provisioned. With cloud providers offering heterogeneous types of resources with different performance and price characteristics, the problem of identifying the various trade-offs available is a great challenge, as the number of possible alternative configurations increases significantly compared to a homogeneous environment, which is itself already computationally difficult. This paper proposes a novel algorithm for dataflow scheduling on heterogeneous clouds that identifies solutions (schedules) close to the optimal pareto front, by exploring the search space in an efficient way. The results of an experimental comparison with the state of the art show that, in several cases, the proposed algorithm provides a richer, more diverse set of solutions, several of which are characterized by significantly better time-money trade-offs.

The paper presents a novel short-range wireless sensor node architecture, based on Integral Pulse Frequency Modulator (IPFM) and Ultra-Wideband (UWB) pulse generator. Due to the lack of internal clock signal source and multi user implementation without application of a microprocessor, the architecture is simple and energy efficient. Multi-user coding is performed using delay elements, where each user has unique delay time value. The output of the IPFM is fed to the delay element. Delayed and original signal from the IPFM are feeding UWB pulse generator. The paper presents the transmitter architecture and measurements of the transmitter signals in time and frequency domain.

Conventional traffic light detection methods often suffers from false positives in urban environment because of the complex backgrounds. To overcome such limitation, this paper proposes a method that combines a conventional approach, which is fast but weak to false positives, and a DNN, which is not suitable for detecting small objects but a very powerful classifier. Experiments on real data showed promising results.

In this paper we propose a Sliding Mode Controller for a class of scalar bilinear systems with delay in both the input and the state. Such a class is considered since it has shown to be suitable for modelling and control of a class of turbulent flow systems. The stability and robustness analysis for the reaching phase in the controlled system are Lyapunov-based. However, since the sliding dynamics is infinite dimensional and described by an integral equation, we show that the stability and robustness analysis is simplified by using Volterra operator theory.

The high energy consumption has become one bottleneck in the development of the data centers (DCs), where the main energy consumers are the cooling system and the servers. Therefore, the joint optimization for the energy efficiency of the cooling system and the servers is a crucial problem, while most of previous works on energy saving only studies one of these two components in an isolated manner. In this paper, we propose a real-time strategy, rTCS (real-time Task Classification and Scheduling strategy), to jointly optimize the energy efficiency of these two components in the scenario where the tasks arrive dynamically. Strategy rTCS first labels the tasks to classify them according to their run time and end time with a time complexity of O(1) and a bounded space complexity. Then, rTCS schedules the tasks in real time based on their labels and the energy consumption model of the DC. Simulation results show that rTCS can effectively improve the energy efficiency of DCs.

Due to the existing cryptographic schemes based on the satisfiability problem (SAT) lack the specific security model and the security analysis of the provable security theory, the security is questioned. For this problem, combining with the advantages of SAT and Elgamal scheme, we construct a secure, verifiable encryption scheme in this paper. Firstly, combining and improving the SRR(N, k, s) model based on the difficulty problem of the random regular formula near the phase transition point, we construct an encryption algorithm. Secondly, on the basis of the existing Damgârd Elgamal scheme, a more secure scheme based on SAT and variants of discrete logarithm problem is proposed. Lastly, the analysis results show that compared with the existing SAT-based hybrid cryptographic schemes, the private key of the hybrid encryption scheme proposed in this paper is shorter and the ciphertext satisfies the verifiability, and the scheme can secure against adaptive chosen ciphertext attacks (CCA2).

Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research., Accepted for publication at 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA 2020

Identifying butterfly species by image patterns is a challenging task in computer vision and pattern recognition community due to many butterfly species having similar shape patterns with complex interior structures and considerable pose variation. In additional, geometrical transformation and illumination variation also make this task more difficult. In this paper, a novel image descriptor, named Gaussian convolution angle (GCA) is proposed for butterfly species classification. The proposed GCA projects the butterfly vein image function and intensity image function along a group of vectors that start from a common contour points and ends at the remaining contour points which results a group of vectors that capture the complex vein patterns and texture patterns of butterfly images. The Gaussian convolutions of different scales are conducted to the resulting vector functions to generate a multiscale GCA descriptors. The proposed GCA is not only invariant to geometrical transformation including rotation, scaling and translation, but also invariant to lighting change. The proposed method has been tested on a publicly available butterfly image dataset that has 832 samples of 10 species. It achieves a classification accuracy of 92.03% which is higher than the benchmark methods.

Cable accessories made of ethylene-propylene-diene terpolymer (EPDM) are considered to be the weakest part of HVDC cable system due to the existence of the interface between cable insulation and itself. The charges are likely to accumulate at the interface between insulation materials with different conductivity and permittivity, which may induce the occurrence of partial discharge and breakdown. Nanoparticles can be applied to adjust the interface charge behaviors. In this paper, carbon black (CB) nanoparticles were dispersed into EPDM with 0, 0.5, 1, 3 and 5 wt% respectively. The effects of nanoparticle doping on the interface charge distribution of LDPE and EPDM composite insulation were measured under −15 kV/mm. Furthermore, dielectric properties and DC conduction were introduced to discuss the suppression mechanism of carbon black nanoparticle doping. Obtained results the interface charge density can be suppressed to 0.5 C/m3 with 1 wt% carbon black nanoparticle doping, much less than the undoped and other doped groups. The mechanism of interface charge suppression is attributed to the decrease of conduction and permittivity mismatch by carbon black nanoparticle doping, which was proved by the dielectric spectroscopy and conduction current tests. The interface charge buildup depends on the time constant controlled by the doping proportion.

The objectives set for smart grids are as diverse as they are exciting and ambitious. Instead of overloads, bottlenecks and blackouts, smart grids will ensure the reliability, sustainability and efficiency of the Electric Vehicle Supply Equipment (EVSEs). Therefore, EVSEs and Electric Vehicles (EVs) must integrate wireless communication between them to discover the availability and make pre-reservations of charging time slots. In this paper, we propose a novel multi-objective EV Charging Slots Assignment (CSA) optimization model that, besides balancing energy usage between EVSEs, minimizes the latency time of EVs. Moreover, we integrate our optimization model to a communication protocol between EVs and EVSEs that allows a reliable reservation process, called Reliable Broadcast for EV Charging Assignment (REBECA) [1]. Then, we propose a centralized heuristic to efficiently solve our model as an offline CSA process.

In order to sell items of a genre that each user is not interested in, their latent interests of the genre have to be induced. Therefore, we focus on "Influencer," who is the person that has a large impact on a user's behaviors, and research a recommender system which advertises some items by utilizing influencer's remarks via social media. This paper investigates what kinds of remarks and remarkers would induce users'interests. As the result, we have revealed many findings. One is that "One of the factors that would induce the users' positive interests on the movie, is the positive reputation for the movie that is included in tweets." The other is that "If the user does not like the remarker, the user's negative interests for the items in the remarks of the person would be induced, even though the person has famousness.", INSPEC Accession Number: 18674306

In this paper, a new procedure to produce broadside circular polarization (CP) in modified rectangular dielectric resonator antenna (DRA) has been presented. The dielectric resonator is deformed periodically in stairshaped form along anti-clockwise direction. The concept of perfect magnetic boundary conditions at the interface of air and dielectric resonator has been successfully applied in this work. Final proposed antenna shows input impedance bandwidth and axial ratio bandwidth of 35% (3.34 – 4.76 GHz) and 8.5% (3.70- 4.03 GHz) respectively.

Training of Convolutional Neural Networks (CNNs) on long video sequences is computationally expensive due to the substantial memory requirements and the massive number of parameters that deep architectures demand. Early fusion of video frames is thus a standard technique, in which several consecutive frames are first agglomerated into a compact representation, and then fed into the CNN as an input sample. For this purpose, a summarization approach that represents a set of consecutive RGB frames by a single dynamic image to capture pixel dynamics is proposed recently. In this paper, we introduce a novel ordered representation of consecutive optical flow frames as an alternative and argue that this representation captures the action dynamics more effectively than RGB frames. We provide intuitions on why such a representation is better for action recognition. We validate our claims on standard benchmark datasets and demonstrate that using summaries of flow images lead to significant improvements over RGB frames while achieving accuracy comparable to the state-of-the-art on UCF101 and HMDB datasets., Comment: Accepted in WACV 2017

This paper reports a concise complexity analysis for the multilevel fast multipole algorithm. The algorithm can solve a plethora of problems governed by different kinds of physics, and therefore the analysis is done for surface and volume problems in two and three dimensions for the sake of generality.

This paper presents an idea to further simplify and relax the linear matrix inequality (LMI) stability conditions of Takagi-Sugeno (T-S) fuzzy systems. By considering the distribution of membership functions in a unified space, we can easily find the overall relation of the original nonlinear system and its approximated local subsystems. Based on the theory of convex combination, the upper-bounds and lower-bounds of each membership functions can be directly used to construct less conservative LMI conditions. The cases of both Type-1 and Type-2 systems are considered, and examples are provided to illustrate the achieved improvement.

Existing Multi-Instance learning (MIL) methods for medical image classification typically segment an image (bag) into small patches (instances) and learn a classifier to predict the label of an unknown bag. Most of such methods assume that instances within a bag are independently and identically distributed. However, instances in the same bag often interact with each other. In this paper, we propose an Induced SelfAttention based deep MIL method that uses the self-attention mechanism for learning the global structure information within a bag. To alleviate the computational complexity of the naive implementation of self-attention, we introduce an inducing point based scheme into the self-attention block. We show empirically that the proposed method is superior to other deep MIL methods in terms of performance and interpretability on three medical image data sets. We also employ a synthetic MIL data set to provide an intensive analysis of the effectiveness of our method. The experimental results reveal that the induced self-attention mechanism can learn very discriminative and different features for target and non-target instances within a bag, and thus fits more generalized MIL problems.

User stories are descriptions of functionality that a software user needs. They play an important role in determining which software requirements and bug fixes should be handled and in what order. Developers elicit user stories through meetings with customers. But user story elicitation is complex, and involves many passes to accommodate shifting and unclear customer needs. The result is that developers must take detailed notes during meetings or risk missing important information. Ideally, developers would be freed of the need to take notes themselves, and instead speak naturally with their customers. This paper is a step towards that ideal. We present a technique for automatically extracting information relevant to user stories from recorded conversations between customers and developers. We perform a qualitative study to demonstrate that user story information exists in these conversations in a sufficient quantity to extract automatically. From this, we found that roughly 10.2% of these conversations contained user story information. Then, we test our technique in a quantitative study to determine the degree to which our technique can extract user story information. In our experiment, our process obtained about 70.8% precision and 18.3% recall on the information.

This paper presents a control design for the irrigation station by sprinkling. The proposed method is applied in order to solve the problem of managing water sources and distributions systems. The main contribution is focused primarily on conceiving T-S fuzzy model for the irrigation station. In formalism of the fuzzy-coalescence algorithm, we derive sufficient conditions to determine the model parameters. Meanwhile, additional effort is developed to achieve PI control for the irrigation station model.

Password security of user accounts is a major concern in today's fast moving and advanced computing world with password authentication the most commonly used identification mechanism. Its security can be enhanced using typing biometrics as a transparent layer of user authentication. This paper describes a password hardening mechanism that is able to recognize different users based on their time latencies. Our research focuses on only using the time period between keystrokes as the measure of the individual's typing pattern. Seven individuals used the same 7-digit password which allowed the system to record the time interval between each digit. Identification improvements up to 3% were achieved with Subtractive Clustering Fuzzy Inference when rule conflicts were removed.

Wireless caching is an efficient mechanism for reducing downloading delay and reducing the traffic pressure over backhual channels by caching some popular content, e.g., video clips, in small base stations (SBSs). In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs) and mobile users (MUs). The NSP leases its SBSs to VRs in order to earn profits, while the VRs store popular videos into the lent SBSs, thereby gaining profits from providing better services to the MUs. We conceive the system within the framework of contract theory by designing the optimal quality-price contract. We establish the profit function of NSP and VRs and solve the profit maximization problem through contract theory. Numerical results validate the effectiveness of our incentive mechanism for the system.

Three-dimensional head pose estimation has been an important and challenging task in computer vision partly because of its diverse applications. In this paper, we propose a new method to estimate head pose for the faces in the wild using deep neural network based on classification, rather than conventional regression. The network consists of three CNNs, corresponding to three head pose components, i.e. yaw, pitch and roll, respectively, and each CNN has 3 channels for color input. We have trained the network using the representative face datasets in the wild, such as AFLW, AFW and YouTube, and found that it outperforms the state-of-art algorithms with a substantial margin. We have also explored transfer learning among them: training the network with a dataset and testing it against others. It is found that there is directional difference between them. And yet, result obtained by transferring from AFLW to others is still better than those from recent studies.

In the era of Internet of Things, crowd counting, which estimates the number of people within a region, becomes the underpinning for many emerging applications, such as occupancy estimation in smart building and queuing management and product placement in shopping center. Existing vision based crowd counting schemes require favorable lighting conditions and also raise privacy concerns. RF based approaches rely on specialized sensors and require users to carry RF devices. Thus, an accurate, reliable and non-intrusive crowd counting scheme is still desired. In this paper, we propose FreeCount, a device-free crowd counting scheme that is able to precisely estimate the number of people within a region using only commodity WiFi routers. To this end, the channel state information (CSI) data in PHY layer is obtained directly by upgrading the router's software. We propose an information theory based feature selection scheme to select the most representative features that are sensitive to human motion. To build a classifier that is robust to temporal and environmental disparities, we adopt transfer kernel learning, which minimizes the difference between the source and target distributions in the reproducing kernel Hilbert space, is adopted to process the real-time CSI feature data. Experiments were conducted in moderate sized rooms and the results demonstrated that FreeCount is able to accurately estimate the number of people with 96% crowd counting accuracy consistently over temporal and environmental variation.

SLAM (Simultaneous Localization and Mapping) of robot is the key to achieve autonomous control of robot, and also a significant topic in the field of mobile robotics. Aiming at 3D modeling of indoor complex environment, this paper presents a fast three-dimensional simultaneous location and mapping (SLAM) method for mobile robots. On the basis of RGB-D SLAM algorithm, the open-source software combining the RGB-D sensor like Kinect with the wheeled mobile robot is used to obtain the odometry data, and then the information of their location is matched through the image feature extraction and in the end the map is constructed. Finally, the feasibility and effectiveness of the proposed method are verified by experiments in indoor environment.

In this paper we propose to utilize a new concept of discrete directional Gabor frames for automatic image registration. The directional Gabor representations have been shown to provide more accurate feature extraction than directional wavelet transforms for images where texture is the dominant feature. Initial experimental results are presented here which indicate that discrete directional Gabor frames exhibit strong correlations, which indicates that they are likely to improve the existing image registration toolbox.

Object tracking and avoidance is a vital functionality in autonomous vehicles. Object tracking techniques must be both fast and accurate for this safety-critical application. Neither of the two factors mentioned can be compromised in this safety critical application. Much of the work done in this field uses the point clouds themselves for object matching. This ensures greater accuracy at the expense of run time. This paper posits that such a fine level of registration is not required for comparable levels of accuracy. The object tracking technique proposed uses a coarse technique for object matching via voxels which reduces run time without compromising on accuracy. It also allows for evaluation of quality of matches.

Mobile vehicles, equipped with low-cost sensors, can provide unprecedented opportunities for urban monitoring with a wide coverage. The quality of collected data is very important for many applications. The low-cost mobile sensors can, however, suffer from limited accuracy, high instability, and sensor drift. Therefore, they need to be frequently calibrated to preserve a good data quality. Frequent sensor calibration can be achieved by deploying static high-precision sensors (called reference sensors) and exploiting meeting points (i.e., rendezvous) between the reference sensors and mobile sensors. A calibrated mobile sensor can also be used to calibrate an uncalibrated mobile sensor when they meet, referred to as multihop calibration. In this paper, we introduce a novel concept, k-hop calibratability, in the context of multihop calibration: a sensor is k-hop calibratable if the calibration path is no larger than k. We consider the problem of how to deploy the reference sensors to ensure that all sensors in the network are k-hop calibratable. To address this problem, we formally define the rendezvous connection graph to precisely describe the relationship between mobile sensors and (virtual) reference sensors. Based on this definition, we formulate the reference sensor deployment problem as a set cover problem. We also extend our problem to consider practical deployment requirements. We propose efficient algorithms and conduct an extensive evaluation in the context of mobile air quality monitoring. We present a detailed prototype implementation of the mobile sensors and reference sensors. Evaluations using real-world datasets show the effectiveness of the algorithms.

In this paper, a long reading range Yagi-Uda UHF RFID tag antenna with temperature sensing is proposed and demonstrated. The proposed RFID sensor tag is equipped with an RFID chip integrates temperature sensors with temperature data acquisition and processing. The alteration of the local environment may cause an impedance mismatch between the antenna and the chip and a change in the tag's gain with the consequence of bandwidth narrowing and the read range degradation. In this case, it's important to design high gain antennas to achieve long-distance sensing in high-temperature environments. The proposed antenna works at around 915MHz and the max gain reaches 5.09dBi, which maximum reading distance exceeds 10m.

P2P botnets represent another escalation level in the race of arms between criminals and the research community. By utilizing a distributed P2P architecture they are resilient against random failures and attacks and overcome the limitations of a central command and control server. For this reason, it is important to monitor them to gather information for potential takedown attempts. In this paper, we introduce our high-frequency crawling tool Strobo-Crawler that can carry out a fine-grained node enumeration. Furthermore, we propose mechanisms to derive accurate snapshots of the botnet graph on the basis of restricted monitoring data. We applied Strobo-Crawler in a two week crawling campaign in the P2P botnets Sality and ZeroAccess and describe the results along with a careful evaluation of our graph reconstruction. Furthermore, we provide a thorough analysis of the resulting botnet graphs and also provide these graphs to the public. Our results indicate that they are highly resilient against node churn, but also against targeted attacks. Bots are highly interconnected and the graphs are characterized by a high clustering coefficient, high density, and low diameter.

In this paper we obtain a Maximum Likelihood Estimator (MLE) for average received power in a multiuser non coherent multiple frequency-shift keying (MFSK) system over a fast Rayleigh fading channel. It is shown that this estimator is unbiased and that its variance can be obtained by a simple linear function. The Cramer Rao lower bound (CRLB) is derived and compared to the MLE's variance. It is shown that in most of the practical cases the MLE is inefficient by a multiplier that depends on system parameters but not on the number of sample points used. Numerical results are obtained and average performance is analyzed.

Six sigma process (SSP) is a process of providing the perfect products without any defects for their customers by considering both mean and variance of manufacturing imperfections. A few researchers have presented some papers on motor and/or drive considering performance variance as well as mean using the SSP. However, all of the presented papers cannot focus on the overall SSP, but mainly optimization step taking into account the probability of failure (POF). Therefore, there was lack of explanation for parts such as analysis of the effects of design factors (variables) on responses (objectives), how to minimize the number of optimized factors and how to optimize design factors considering POF by tolerance design (TD), and so on. This paper presents the full details of SSP from defining objectives to verifying design results with an example study of spoke-type PM motor design, which is applicable to motor design process including the above mentioned terms. SSP is composed of the five sequential steps i.e. Define-Measure-Analyze-Improve-Control (DMAIC) steps, while some analysis tools and statistical theories should be considered in each step. First of all, two important indexes i.e. Z-value and p-value along with the statistical analysis are explained in detail. Next, some useful tools for each step are explained e.g. Y-y mapping method in define step, Gage R&R in measure step, analysis of variance (ANOVA) in analyze step, response surface methodology (RSM) and tolerance design (TD) in improve step, and monte-carlo simulation (MSC) and process capability analysis (PCA) in control step, respectively. Finally, it is shown that the SSP is useful for motor design process to satisfy both the required performances and POF of spoke PM motor.

Nowadays, data is seen as one of the most valuable assets of organizations. Representing and exposing data in a suitable manner is mandatory for allowing consumers – either human beings or software systems – to properly retrieve and interpret such data. Web Services along with semantic Web techniques may be adopted to address this issue. This paper presents the semantic data-driven microservice, a cloud service capable of providing linked data based on non-semantic data sources. Its main goal is to work as a solution for publishing linked data and for maximizing data reuse.

This paper considers a low-complexity hybrid beamforming (HBF) for uplink multiuser mmWave MIMO systems. The proposed scheme employs a two-stage beamforming design. In the first stage, the base station (BS) analog beamforming is designed to maximize the $k$th user's signal-to-interference-plus-noise-ratio (SINR) in single-carrier systems and to maximize the sum rate's upper bound of uplink virtual cooperative users in orthogonal frequency division multiplexing (OFDM) based systems (also called multi-carrier systems), respectively. Furthermore, we perform phases extraction and quantize phase control on the obtained BS analog beamforming. In the second stage, the BS digital beamforming is designed by performing minimum mean square error (MMSE) with the low-dimensional effective channel. The proposed schemes are simulated in both ideal Rayleigh fading channels and sparsely scattered mmWave channels, approaching the sum rate performance of the full digital solution in single-carrier case and of the virtual uplink user cooperation scheme in multi-carrier case, respectively. The simulation results demonstrate that the proposed schemes outperform the existing HBF designs for uplink multiuser mmWave MIMO systems.

Wireless device-free passive human detection is an essential primitive for a broad range of applications including asset security, emergency responses, privacy-preserving children and elderly monitoring, etc. Previous works have studied the Channel State Information (CSI) to detect moving humans by comparing static profiles and abnormal profiles, however, few of these profiles have been considered to adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Moreover, the multi-antennas in MIMO systems has not further exploited to improve the detection accuracy. In this paper, we propose a robust and adaptive passive human detection system (RAPD) using a semi-supervised approach to construct signal profiles, and the profiles can be adaptively update to accommodate the movement of the mobile devices and day-to-day signal calibration. Experimental evaluation in two different scenarios demonstrates that our approach can achieve great performance improvement in spite of environment changes.

Machine learning (ML) algorithms provide a good solution for many security sensitive applications, they themselves, however, face the threats of adversary attacks. As a key problem in machine learning, how to design robust feature selection algorithms against these attacks becomes a hot issue. The current researches on defending evasion attacks mainly focus on wrapped adversarial feature selection algorithm, i.e., WAFS, which is dependent on the classification algorithms, and time cost is very high for large-scale data. Since mRMR (minimum Redundancy and Maximum Relevance) algorithm is one of the most popular filter algorithms for feature selection without considering any classifier during feature selection process. In this paper, we propose a novel adversary-aware feature selection algorithm under filter model based on mRMR, named FAFS. The algorithm, on the one hand, takes the correlation between a single feature and a label, and the redundancy between features into account; on the other hand, when selecting features, it not only considers the generalization ability in the absence of attack, but also the robustness under attack. The performance of four algorithms, i.e., mRMR, TWFS (Traditional Wrapped Feature Selection algorithm), WAFS, and FAFS is evaluated on spam filtering and PDF malicious detection in the Perfect Knowledge attack scenarios. The experiment results show that FAFS has a better performance under evasion attacks with less time complexity, and comparable classification accuracy.

Based on the analysis of the status of archives social media information at home and abroad, it is not only a strategic step in the construction of archives informatization, but also an inevitable choice for its effective construction and utilization. This paper draws lessons from the international standards of document management system and the reference model of open archives information system, and designs the framework of archives social media integration system at the top level. It also takes the heterogeneous data of various archives social media information as the analysis source, and excavates the contents in depth. The LDA model is improved and extended by utilizing certain attribute information in microblog messages. At the same time, the relevant features of data sources are fused into the traditional document sorting function, which significantly improves the retrieval performance of multi-source social media information.

In this paper, we explore building classifiers to detect Salsa dance step primitives in choreographies available in the Huawei 3DLife data set. These can collectively be an important component of dance tuition systems that support e-learning. A dance step is reasoned as the shortest possible extract of bodily motion that can uniquely identify a particularly repeatable movement through time. The representation of dance steps adopted is a concatenation of vectorized matrices involving the 3D coordinates of tracked body joints. Under this modeling context, a Salsa dance performance is seen as an ordered sequence of Salsa dance steps, requiring a multiple of the variables allocated in the representation of a single step. Following a previous work by Masurelle & Essid that discusses the classification of six Salsa dance steps from 3DLife, we show that it is possible to obtain better classifiers under a similar experimental protocol in terms of both test accuracy and F-measure. By carefully re-annotating the data in 3DLife, we refocus on the six-step classification problem and then extend the protocol to the case of 20 dance steps. In comparison to common classifiers of the trade operating on full-dimensions, we show that it is possible to produce more accurate models by computing a subspace of the data. At the same time it is possible to reduce problematic bias in resulting models due to the uneven distribution of samples across step data classes. We provide and discuss experimental findings to support both hypotheses for the two experimental settings.

In this paper, we propose an efficient near-field source DOA estimation method based on the Fractional Fourier transform. In this method, the received data of the sensor array for the near-field source is modeled as a linear-frequencymodulated (LFM) signal in spatial domain after the Fresnel approximation. Then the Fractional Fourier transform is employed to estimate the modulated frequency rate, which stands for the influence of quadratic phase of near-field source. After compensating this quadratic phase term, the received signal can subsequently be converted into a far-field expression. Finally, classical DOA estimation methods for far-field can be applied to find the direction of the source. In addition, for multiple sources, a CLEAN technique is proposed to extract the sources iteratively. Simulations are finally carried out to demonstrate its validity.

In this paper, we propose a rate adaptation algorithm for Hypertext Transfer Protocol (HTTP) streaming. The proposed algorithm selects the video bit rates based on the estimated throughput and playback buffer occupancy. The performance of the algorithm is compared with that of throughput- and buffer-based adaptation algorithms. The proposed method selects high-quality video segments, while minimizing video quality changes and the risk of playback interruption. We evaluate the algorithm for single- and multi-user environments and demonstrate that it performs remarkably well under varying network conditions. Furthermore, we determine that it efficiently utilizes network throughput to achieve a high video rate; competing HTTP clients achieve equitable video rates. We also confirm that the proposed algorithm performs well regardless of the buffer size and segment duration.

The transformer partial winding deformation, as the precursor of the inter-turn insulation breakdown and total winding mechanic instability, often induces the change of magnetic flux leakage distribution. And monitoring online the magnetic flux leakage characters and diagnosing the transformer partial winding deformation is the novel and good method to prevent the transformer faults in advance, which can make up for the shortcomings of the existing methods. Thus, it is key to explore the magnetic flux leakage distribution law under different winding deformation and working conditions. In the paper, the 35kV power transformer was selected as the research object and its simulation model with different partial radial and axial winding deformation was built. The equivalent method of abnormal operating conditions including DC (Direct current) bias and external short-circuit was determined. Consequently, the change law of magnetic flux leakage characteristics in the transformer was analyzed under the different conditions. The results show the magnetic flux leakage under different winding deformation has a different distribution characteristic, and the information can be used to identify the severity degree and location of the defects. And the character of winding deformation can be distinguished from the disturbances by the feature identification.

The positioning systems’ high accuracy and reliability are crucial enablers for various future applications, including autonomous shipping worldwide. It is especially challenging for the Arctic region due to the lower number of visible satellites, severe ionospheric disturbances, scintillation effects, and higher delays than in the non-Arctic and non-Antarctic regions. In regions up North, conventional satellite positioning systems are generally proposed to be utilized, together with other situational awareness systems, to achieve the necessary level of accuracy. This paper provides a detailed review of the current state-of-the-art, satellite-based positioning systems’ availability and performance and reports high-level positioning requirements for the oncoming applications. In particular, the comparative study between three Global Navigation Satellite System (GNSS) constellations is executed to determine whether they are suitable for autonomous vessel navigation in the Arctics’ complex environment as the two most significant drivers for a reevaluation of the related satellite constellations. This work analyzes the ongoing research executed in different (inter-) national projects focused on Galileo, Global Positioning System (GPS), and GLObal NAvigation Satellite System (GLONASS). Based on the literature review and the simulation campaign, we conclude that all the convectional constellations achieve an accuracy of fewer than three meters in the analyzed Arctic scenarios. It is postulated that other complementary positioning methods should be utilized to improve accuracy beyond this limit. Finally, the study emphasizes existing challenges in the Arctic region regarding the localization and telecommunication capabilities and provides future research directions. publishedVersion

Clustering plays an important role in data mining and machine learning. Then, intuitionistic fuzzy sets (IFSs) are flexible and practical in dealing with vagueness and uncertainty problems. To cluster the information expressed by intuitionistic fuzzy data, this paper proposes the joint training auto-encoder based intuitionistic fuzzy clustering algorithm. Firstly, we propose the auto-encoder based intuitionistic fuzzy clustering by utilizing similarity measure of IFSs, auto-encoder and k-means algorithm. Then, we propose the joint training auto-encoder based intuitionistic fuzzy clustering algorithm by utilizing the proposed auto-encoder based intuitionistic fuzzy clustering and two kinds of similarity measures for the clustering analysis of intuitionistic fuzzy data. Lastly, several experiments are provided to verify the effectiveness of the proposed intuitionistic fuzzy clustering algorithms.

Emerging as a widely accepted technique for malware analysis, YARA rules due to its flexible and customisable nature, allows malware analysts to develop rules according to the requirements of a specific security domain. YARA rules can be automatically generated using tools, however, they may require post-processing for their optimisation, and may not be effective for the specific security domain. This compels the requirement to enhance automatically generated YARA rules and increase their effectiveness for malware analysis without increasing computa- tional overheads. Reflecting on the above requirement, this paper initially evaluates automatically generated YARA rules using three YARA tools: yarGen, yaraGenerator and yabin. These tools are Python-based open-source tools used to generate YARA rules automatically utilising different underlying techniques. Subsequently, it proposes a method to enhance automatically generated YARA rules using a fuzzy hashing method. This proposed enhancement method can improve the effectiveness of YARA rules irrespective of the chosen YARA tool used to generate YARA rules, which is demonstrated through several experiments on samples of collected malware and goodware.

Rapid and easy transaction through credit card system has increased fallacious cases everywhere. Machine Learning algorithms has been applied for identifying fraudulent transactions. Fraud detection problem have two major issues, firstly, the legitimate and fraudsters consistently change behaviour and secondly, datasets are severely skewed. Implementation of the system littered with machine learning algorithms, excerpting variables and sampling accession on datasets. This paper investigates the performance of Logistic Regression, Random Forest, Decision Tree and SVM (Support Vector Machine). The proposed system shows results according to accuracy, sensitivity, specificity, precision of above techniques. Transactions in the dataset are heavily right skewed. Undersampling and oversampling is conducted on the data. The work is implemented in Python.

When a person learns, they observe and interact with their surroundings, and monitor the outcome of these interactions. During this process, the brain only examines single snapshots of information. It does not need to continuously revisit past instances of time to retain learned information. Supervised neural networks, as much as they resemble the human brain, do not learn well incrementally. The standard multilayer perceptron, radial basis function network and others, have a common problem of losing learned information when additional data points are presented. In this paper, we develop a supervised learning neural network that converges on new information without overriding previous knowledge. The proposed Neural Field can operate in real time environments where a data point is seen once and never revisited, or chase a function that changes over time without continuously relearning on a full dataset. A dataset can be presented in portions and the network will attain knowledge incrementally. With a static ordered grid of radial basis function neurons, the Neural Field both learns incrementally and generalizes effectively.

This paper demonstrates some results from an on-going experiment on energy management in a residential testbed in Singapore by controlling the air-conditioning (AC) system. The testbed is set up at the faculty housing of Singapore University of Technology and Design (SUTD) in which the ACs installed within each unit can be controlled from a remote server. The control can be conducted both in terms of switching on/off and changing the temperature set-point of the ACs. The main objectives are to reduce the consumption of energy at a residential setting by controlling the ACs' temperature set-points and investigating the feasibility of having residential ACs as interruptible loads for demand response and thus enabling residential users to participate in the energy market. The control methodology is briefly discussed and the users' inputs on the experience of such experiments are also explained. Some preliminary results from the experiments are discussed and some insights are provided into how much load can be controlled through the set-point temperature of ACs such that the users do not feel any discomfort.