Miracle-dz/UniFAR-FTUs huggingface dataset:
Building a Quality System for an Electronic Component Base for High-Tech Industries

The main aspects of quality assurance for the electronic component base (ECB) installed in the flight samples of space vehicles are considered. It is shown that the homogeneity analysis of ECB batches is now becoming a vital task, especially for ECBs with a high level of integration. The economic justification for creating special batches as a way of centralizing the equipment of space vehicles on the principle of equal reliability is given.
An End-to-End Neural Network for Multi-line License Plate Recognition

Currently, license plate recognition plays an important role in numerous applications and a number of technologies have been proposed. However, most of them can only work with single-line license plates. In the practical application scenarios, there are also existing many multi-line license plates. The traditional approaches need to segment the original input images for double-line license plates. This is a very difficult problem in the complex scenes. In order to solve this problem, we propose an end-to-end neural network for both single-line and double-line license plate recognition. It is segmentation-free for the original input license plate images. We view each of these whole images as a unit on feature maps after deep convolution neural network directly. A large number of experiments show that our method is effective. It is better than the state-of-the-art algorithms in SYSU-ITS license plate library data.
Joint Detection and Tracking via Path Planning in the Mobile Underwater Sensing Network

Due to the complexity of the underwater acoustic environment, it is a great challenge to jointly detect and track a target by passive methods. A path planning algorithm is presented to improve the performance of the passive detection and tracking for the mobile underwater wireless sensor network(MUWSNs). The target state is modeled as a random finite set, and the Bernoulli filter is utilized to detect and track the target. Moreover, the filter is associated with the path planning algorithm based on two reward functions: the Rényi divergence and the Fisher information gain. The simulations are performed with some practical underwater acoustic environmental parameters, and the results show that the proposed path planning algorithm can improve the detection and tracking performance of the MUWSNs. For the comparison of two reward functions, the results indicate that the algorithm with Rényi divergence has lower position errors.
Two-Step Hybrid Multiuser Equalizer for Sub-Connected mmWave Massive MIMO SC-FDMA Systems

Most of the works consider hybrid fully connected architectures to overcome the constraints of millimeter wave massive MIMO systems. However, these schemes require a one-to-one connection between the RF chains and antennas. In this paper we propose a two-step broadband multiuser equalizer for hybrid sub-connected architectures, which is a more realistic approach for practical systems. The low-complexity user-terminals employ only analog precoders computed from the knowledge of the average angle of departure of each cluster, which is constant over the bandwidth. At the receiver side, we design a hybrid multi-user equalizer by minimizing the average bit-error-rate. A two-step approach is considered, where the analog part is constant over the iterations due to hardware constraints and the digital part is iterative. The analog part is also constant over all subcarriers while the digital part is computed on a per subcarrier basis. The proposed sub-connected based equalizer is compared with the fully connected counterpart. The results show that the performance of the proposed scheme is close to the fully connected one after just a few iterations performed at the digital domain.
Medical Knowledge Constrained Semantic Breast Ultrasound Image Segmentation

Computer-aided diagnosis (CAD) can help doctors in diagnosing breast cancer. Breast ultrasound (BUS) imaging is harmless, effective, portable, and is the most popular modality for breast cancer detection/diagnosis. Many researchers work on improving the performance of CAD systems. However, there are two main shortcomings: (1) Most of the existing methods are based on prerequisites that there is one and only one tumor in the image. (2) The results depend on the datasets, i.e., an algorithm using different datasets may obtain different performances. It implies that the performance of traditional methods is dataset-dependent. In this paper, we propose an effective approach: (1) using information extended images to train a fully convolutional network (FCN) to semantically segment BUS image into 3 categories: mammary layer, tumor, and background; and (2) applying layer structure information - the breast cancers are located inside the mammary layer - to the conditional random field (CRF) for conducting breast cancer segmentation and making the segmentation result more accurate. The proposed method is evaluated utilizing BUS images of 325 cases, and the result is the best comparing with that of the existing methods by achieving true positive rate 92.80%, false positive rate 9%, and Intersection over Union 82.11%. The proposed approach has solved the above mentioned two shortcomings of the existing methods.
Influence of the Concentration of Cs on the Properties of Thin Films of CsXMA(1-X)PbI3 to be used as Active Layer in Hybrid Solar Cells

In this paper we studied the effect that the substitution of the methylammoniumion by the cesium ion cause on the properties of the Meta lorganic compound CsxMA(1-x) PbI3, synthesized using the spin coating technique. Through a study of synthesis parameters and their correlation with results of characterization performed using techniques such as spectrophotometry,X-ray diffraction (XRD) and Scanning electron microscopy (SEM), conditions were found to grow this compound with perovskita tetragonal type structure and suitable properties (in terms of bandgap, thickness and coefficient of absorption) to be used as absorber layer in hybrid solar cells. Special emphasis was placed on evaluating the effect of the change in Cs/MA ratio over the optical, structural and morphological properties of CsxMA(1-x)PbI3 films.
Distributed Resource Allocation for Mobile Users in Cache-Enabled Software Defined Cellular Networks

In this paper, we study the problem of resource allocation in a cache-enabled software defined cellular network (SDCN) with mobile users, where the SDCN controller has global information of the network and the popular contents that the users request are stored at the content server and cache-enabled small base stations (SBSs). We propose a Markov chain based model to predict the users’ mobility patterns and then use the predicted mobility patterns to determine optimal resource allocation. The mobility prediction and resource allocation problem are jointly formulated as an optimization problem whose goal is to maximize the network throughput. Based on the predicted users’ mobility patterns, a distributed alternating direction method of multipliers (ADMM) is proposed to solve the resource allocation problem. The proposed ADMM algorithm enables the multiple SBSs implement their resource allocation simultaneously and, hence decreases the control overhead of the SDCN controller. Simulation results show that the proposed algorithm achieves up to 9.35% and 33.17% gains in terms of the average throughput compared to the random algorithm and the nearest association with equal allocated resource algorithm.
Power-Efficient p-Cycle Protection with Power Conscious Routing in Elastic Optical Networks

With rapid increase in power consumption in information and communication technology (ICT], there is need of power-efficient solutions in telecom networks. Elastic optical networks (EONs), as spectrum efficient and flexibility in bandwidth, are also major contributor in the overall power consumption. There is immense amount of data transmission in the network and any loss of data due to network failure can incur huge losses. In order to meet this high reliability requirement, network architectures are deployed with a large number of redundant resources to provide the backup protection. These redundant resources are also major contributor to overall power consumption. Shortest path routing (SPR) is employed in some power saving strategies. This paper considers power efficient protection with power conscious routing (PCR) in EONs against single link failure by using preconfigured cycles (p-cycles). An integer linear programme (ILP) has been developed to optimize total required power consumption. Optimization of power consumption is based on PCR and selection of p-cycle by maximizing number of idle network resources be put into sleep mode. Numerical results show that the power consumption during low traffic with PCR is lesser than SPR. In addition, it is seen that as the traffic increases, the power consumption saturates.
Inverse modeling of diffusion-reaction processes with image-type measurement data

The inverse source problem for 1D diffusion-reaction model is considered. The measurement data is given as the images of the concentration fields dynamics for the subset of the interacting species. These inverse problems arise in the study of the growing tissues (morphogenes theory), in the development of the tissue engineering technologies and in the other fields of modern mathematical biology. The sensitivity operator, composed of the ensemble of the independent adjoint problem solutions allow to transform the inverse problem to the family of nonlinear ill-posed integral equations. Each member of the family correspond to the image to structure operator that extracts certain features of the image. An equation from the family is solved with the Newton-Kantorovich-type algorithm combining truncated SVD and iterative regularization. Due to the design with the adjoint problems ensemble, the algorithm can be efficiently parallelized. The algorithm’s convergence and stability are illustrated numerically in Brusselator model case.
A Cognitive Architecture for Social Robots

The paper illustrates a software architecture allowing a robot to socially interact with human beings, sharing with them some basilar cognitive mechanisms. Robust sensing of the environment and people is strongly linked with an artificial somatosensory system that drives the robot behavior at a low level and influences its motivation. Both long-term memory and short-term memory store relevant data to detect and recognize the social context (and social practice), and the human social behavior. Using both internal and external evaluations, the robot learns and improves its social skills, which take into account its physiological and emotional demands (affiliation, competence, certainty). Social interaction is encoded in the cognitive architecture by considering at the same level the human understanding and the robot communicative actions. This is done by using the same interaction channels (both verbal and nonverbal). Some examples derived from previous works show the effectiveness and the potential of the cognitive architecture.
Anti-Forensics of JPEG Compression Using Generative Adversarial Networks

JPEG compression is one of the most popular image compression methods. The manipulation history of JPEG compression provides evidence of operational information about the device or software utilized to generate an image. Besides, JPEG compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring, which lower the image visual quality. Therefore, erasing the traces left by JPEG compression is of great importance. To solve this problem, we present a JPEG compression anti-forensic method adopting the framework of generative adversarial networks (GANs). This architecture consists of a generator and a discriminator, where the generator can automatically learn how to hide the traces left by JPEG compression during the optimization process against the discriminator. Through extensive experiments, it is demonstrated that the anti-forensically modified images generated by our method can deceive the existing JPEG compression detectors and have very good visual quality.
A Survey on Privacy Preserving Techniques in Cloud Environments

Cloud computing methodology is a conceptual based technology which is used widely nowaday. Cloud Computing is an emerging technology which offers an innovative business model for the organizations with massive data without upfront investment, but most of the organizations still hesitate to explore their business over cloud due to security issues. Data privacy protection and data retrieval control is one of the most challenging research works in cloud computing due to confidentiality of user data. Security is one of the major issues which hamper the growth of cloud. This Paper focuses on specific analysis of privacy preservation techniques, comparative analysis and challenges.
On the Errors Arising from the Use of Discrete Surface Models for Land Clutter Simulation

The paper analyzes influence of a discrete model of reflective features of the earth surface on the error of echo signal simulation. It is shown that the discreteness leads to the errors in calculating a power of the signal simulated. An evaluation of the error level and its dependence to the discretization step is obtained. The requirements to the sampling period determining the adjusted error level are presented.
Revised Contrastive Loss for Robust Age Estimation from Face

Age estimation has broad applications in many fields, such as video surveillance, social networking, and human-computer interaction. Many of the existing approaches treat age estimation as a classification problem; however, the individual age values are not independent classes; they have an ordinal relationship. Classification loss such as softmax is not able to model such kind of relationship. In this paper, we propose a new loss, called revised contrastive loss, to model the ordinal relationship of individual ages. Specifically, the revised contrastive loss is proposed to penalize the distance between two face images in the feature space according to their age difference, which makes the learned features more discriminative for the age estimation task. We embed the proposed revised contrastive loss and softmax loss into a Convolutional Neural Network (CNN), and optimize the networks via Stochastic Gradient Descent (SGD) in an end-to-end fashion. Experimental results on a number of challenging face aging databases (FG-NET, MORPH Album II, and CLAP2016) show that the proposed approach outperforms the state-of-the-art methods by a large margin using a single model.
Multi-Stream Region Proposal Network for Pedestrian Detection

Pedestrian detection aims to locate person instances with bounding boxes in images or videos. Although researches have achieved significant progress in pedestrian detection recently, there are still open questions regarding occlusion, variation and confusion. In this paper, we propose a multi-stream region proposal network to enhance the ability of pedestrian detection. First, multiple visible region guided networks are proposed to obtain the disparate features based on diverse visible region patterns of human body. Then, fusion network is introduced to integrate the feature maps from multiple visible region patterns. Finally, a specially tailored region proposal network is applied to generate the proposal regions using the fused region features. In addition, boosted forest is used to classify the proposal regions. The proposedmethod is evaluated on Caltech database and achieves comparable performance with the state-of-the-art methods.
One-factor Cancellable Biometrics based on Indexing-First-Order Hashing for Fingerprint Authentication

Despite biometrics is deemed a more secure and user-friendly solution than password-based or token-based approach for identity management, biometric templates are vulnerable to adversary attacks that may lead to privacy invasion and irreversible identity theft. Cancelable biometrics is a template protection method that generates a noninvertible identifier from the original biometric template by means of a parameterized transformation function and user/application-specific parameters. However, the necessity to input parameter, either in possession (token) or in memory (password) form along with biometrics, hence two factors, jeopardizes usability of the biometrics. In this paper, we propose a one-factor cancellable biometric authentication scheme that empowered by Indexing First Order hashing, a tailor-made locality sensitive hashing function for template protection. We evaluate the proposed scheme with respect to four template protection design criteria, namely noninvertible, renewability, unlinkability and accuracy performance. We also analyze the threat model of the proposed scheme that enclosed five major security attacks. Despite the scheme can be applied to any binary biometric features, we adopt binary fingerprint vector as a case study for this paper. The evaluations have been carried out under six datasets taken from FVC 2002 and FVC 2004 benchmark databases.
Fine-grained Video Retrieval using Query Phrases — Waseda_Meisei TRECVID 2017 AVS System —

In this paper, a joint team from Waseda University and Meisei University (team name: Waseda_Meisei) report their efforts on the ad-hoc video search (AVS) task for the TRECVID benchmark, which is conducted annually by the National Institute of Standards and Technology (NIST). For the AVS task, a system is required to perform a fine-grained search of target videos from a large-scale video database using a query phrase including multiple keywords, such as objects, persons, scenes, and actions. The system we submitted has the following two characteristics. First, to improve the coverage rate of classes corresponding to keywords in query phrases, we prepared a large number of classifiers that can detect objects, persons, scenes, and actions, which were trained using various image and video datasets. Second, when choosing a concept classifier corresponding to a keyword, we introduced a mechanism that allows us to select additional concept classifiers by incorporating natural language processing techniques. We submitted multiple systems with these characteristics to the TRECVID 2017 AVS task and one of our systems ranked the highest among all the submitted systems from 22 teams.
Impact of Electric Propulsion on Aircraft Noise - All-Electric Light Aircrafts Case Study

Aircraft electrification is on the verge of radically changing air transportation. Energy-efficient distributed propulsion, vertical take-off and landing capabilities and reduced emissions are some examples of the great possibilities that electric propulsion offers and how it could disrupt air travel and urban air mobility in the future. Next to the many technological challenges associated with such new concepts, important regulatory barriers still need to be overcome to make it come true. One aspect of particular attention is the impact on environmental noise. In this paper, the acoustic performance of two all-electric light aircrafts is assessed based on ground and in-flight measurements. The investigated aircrafts are the Magnus eFusion, first all-electric aerobatic training airplane in the world and the Extra 330LE, world's first electric aircraft to tow a glider into the sky. For both airplanes, the actual impact of electric propulsion on exterior radiated noise during fly-over is quantified - for the first time - using two variants of the same aircraft, equipped with two different propulsion systems: in one case with a conventional piston engine, in the other case with an electric motor. Cabin noise is also assessed for the two aircraft variants. Sound quality metrics as well as sound source localization techniques are used to perform a detailed analysis of the interior and exterior aircraft noise and to get insight into the noise generation mechanisms. The manuscript details the complete set of measurement techniques which are available for acoustic engineers to develop quieter electric aircrafts in the future. It reports about the setup and execution of the test campaign; it describes the processing of the acquired data and discusses the major findings. Results highlight the different footprints of the two types of propulsion in terms of fly-over and cabin interior noise levels.
Neuro-Adaptive Traffic Congestion Control for Urban Road Networks

The rapid increase of private vehicles combined with the limited capabilities of the urban road infrastructure has made congestion one of the main problems of major cities worldwide, having a severe impact on both the economy and the environment. In this work, we shall attempt to solve the traffic management problem by examining in a unified manner the traffic network, the route guidance of the vehicles and the regulation of the traffic lights, as the basic elements of a single controlled system. In particular, we propose a decentralized adaptive control system, comprised of three main modules: i) the network congestion estimator, ii) the reference travel time estimator, and iii) the rate controller, that is capable of efficiently regulating the travel time along the traffic network while avoiding congestion at the junctions. The design of decentralized control algorithms and their implementation as traffic management applications for portable computing devices (e.g., 3rd and 4 th generation mobile phones, tablets, computers embedded in “smart” vehicles) is expected to improve drastically the traffic condition of urban road networks. Meanwhile, in future traffic networks, where the navigation of the vehicles will be conducted by autopilots in the absence of human-drivers, the use of such a distributed autonomous management system will be essential.
Iterative Reorganization With Weak Spatial Constraints: Solving Arbitrary Jigsaw Puzzles for Unsupervised Representation Learning

Learning visual features from unlabeled image data is an important yet challenging task, which is often achieved by training a model on some annotation-free information. We consider spatial contexts, for which we solve so-called jigsaw puzzles, i.e., each image is cut into grids and then disordered, and the goal is to recover the correct configuration. Existing approaches formulated it as a classification task by defining a fixed mapping from a small subset of configurations to a class set, but these approaches ignore the underlying relationship between different configurations and also limit their applications to more complex scenarios. This paper presents a novel approach which applies to jigsaw puzzles with an arbitrary grid size and dimensionality. We provide a fundamental and generalized principle, that weaker cues are easier to be learned in an unsupervised manner and also transfer better. In the context of puzzle recognition, we use an iterative manner which, instead of solving the puzzle all at once, adjusts the order of the patches in each step until convergence. In each step, we combine both unary and binary features of each patch into a cost function judging the correctness of the current configuration. Our approach, by taking similarity between puzzles into consideration, enjoys a more efficient way of learning visual knowledge. We verify the effectiveness of our approach from two aspects. First, it solves arbitrarily complex puzzles, including high-dimensional puzzles, that prior methods are difficult to handle. Second, it serves as a reliable way of network initialization, which leads to better transfer performance in visual recognition tasks including classification, detection and segmentation.