Miracle-dz/UniFAR-FTUs huggingface dataset:
Foreground Target Extraction in Bounding Box Based on Sub-block Region Growing and Grab Cut

The region of interest for the network detection based on deep learning algorithms exists a problem of background interference, which goes against the feature extraction of the region of interest. In order to solve the problem of background interference faced by the region of interest, this paper proposes a foreground extraction algorithm for the target detection based on deep learning algorithms in combination of the sub-block region growing algorithm and the Grab Cut algorithm. Firstly, this algorithm pre-processes the color image through the Retinex algorithm to improve the image contrast; secondly, it extracts the features of the image sub-blocks (including dominant hue, secondary hue, color and luminance, etc.) and constructs the similarity measurement function between different sub-blocks; thirdly, it defines the seed region, sub-block region growing and termination criterion of the region growing and achieves the coarse segmentation of the foreground; finally, it re-extracts the coarse segmentation results that have been processed through the open operation by means of Grab Cut algorithm to obtain the foreground segmentation results of the input image. The experimental results show that this method can offer a better foreground extraction result with a low miss rate and a low false positive rate to solve the problem of background interference faced by the target detection based on deep learning network.
Sonar-based SLAM Using Occupancy Grid Mapping and Dead Reckoning

This paper presents a solution for Simultaneous localization and mapping (SLAM) with an autonomous differential drive robot in an indoor environment. We extract data from sonars and analyze it to construct a map of the explored area using occupancy grid mapping. In addition, relative positioning approaches provide location using an inertial measurement unit (IMU) and wheel encoders. Localization and mapping are essential tasks for an autonomous robot's navigation or exploration without a prior map and the system is based on discrete step-wise or event modeling which guides its navigation. The system’s result is compared for accuracy against an actual map. The results show that the approach is largely accurate although the resolution of the map produced could be improved.
Differential Thresholding of SIFT-based Blood Vessel Segmentation in Retinal Fundus Image

Automatic vessel segmentation often makes the worse performance on extraction factor since it is desirable to have continuous structure. Henceforth, imperative to fill and examine the gaps among vessels. This paper has spotted the limitations of two different segmentation models and practiced a new segmentation model that incorporates SIFT method followed by differential thresholding. SIFT method discovers the unique key points that cannot be varied by any scaling or rotation process. This proposed segmentation process skips overseeing the vessel portions as major and minor. Instead, the proposed work evaluates the vessel portions based on their scale invariant characteristics and pixel intensity. Finally, the proposed model compares its performance over other conventional segmentation models including Iterative Segmentation model and Gaussian Mixture Model (GMM) in terms of Accuracy, Specificity, Sensitivity and Area Under the Curve (AUC) and proven the efficiency of proposed work.
Target Tracking Using Pre-tracking Compressive Detector

Track before detect(TBD) is an effective target tracking strategy in Nyquist sensing and processing(NSP) field. However, it cannot be directly applied in compressive sensing and processing (CSP), which refers acquiring and processing signal at sub-Nyquist rate without signal recovery, due to the sub-sampled nonlinear measurement. In this work, a TBD method is proposed for tracking a single target in CSP. A pre-tracking compressive detector is first developed to improve the detection performance by processing the compressive measurement directly with prior information on the signal structure, then a Modified Variational Bayesian Adaptive Kalman Filter (VBAKF) algorithm is adopted for target tracking. Simulation results verify the validity of the proposed method both in target detecting and tracking.
A new nanohybrid for electrochemical removal of humic acids and Cr(VI)

A new nanohybrid, based on flower‐like AuPtPd‐Fe3O4, has been designed and prepared through an easy and scalable synthetic approach for the effective and simultaneous removal of humic acids (HAs) and Cr(VI). The nanocatalyst, prepared in a one‐step procedure, consists of nanoparticles with a grain size of about 40 nm, constituted of a magnetite core ca. 20 nm and petals of PtPd and Au and, after a ligand exchange process, results dispersible in water. Calibration equations were plotted for the removal evaluation in water samples. Electrochemical tests were carried out at pH 3.0 and 30 mA. HAs were highly mineralized with TOC removal efficiency of 95% after only 3‐h electrolysis, while 99% of total chromium (Cr) was removed in 3 h. A mechanism was proposed to describe the catalyst activity. The nanocatalyst shows high removal performances for HAs and Cr(VI) even after 10 times treatment, indicating good stability.
Clutter Properties and Suppression Methods of Hyper Sonic Airborne Radar

Due to the high speed and critical height, clutter spectrum of hyper sonic airborne radar turn out to be seriously non-stationary. The multiple clutter aliasing which resulted from range ambiguity and Doppler ambiguity are analyzed in this paper. For solving the problem that the homogenous samples are starving, a knowledge-aided space time adaptive processing method is proposed in which samples are replenished through the high fidelity clutter simulation based on prior information such as digital elevation map and the usage of history data. The simulation results illustrate that the proposed method greatly improves the performance of clutter suppression for the hyper sonic airborne radar.
A Deep Learning Method for Suit Detection in Images

The classification and detection of human clothing is a major challenge for machine vision. Accurate and rapid detection of the categories of clothing in images or videos helps to develop intelligent social interaction systems that accord with human thinking. It can also automatically classify mass images in the network and improve the trend analysis of fashion trends. The development of deep convolution neural network greatly improves the accuracy of the artificial intelligence target detection and detection technology. Based on the experiments of three main frameworks (Faster R-CNN, R-FCN and SSD), a garment detection and location method is proposed. Unlike standard sample sets, the training samples are from the unprocessed material of e-commerce platforms. Both the size of the picture and the pose of the model are very different, which can be more conducive to the generalization of the application scene. The experiment validates the performance of deep learning in clothing detection and combines image visual segmentation to solve the problem of small target detection. Negative samples are enhanced to improve the adaptability of the system scene. The experimental results show that the method is effective in identifying all kinds of suits and can be extended to other styles of clothing detection and location.
A Novel Multi-Color Feature Selection Method for Person Re-identification

In this paper, a novel multi-color feature selection method is proposed for person re-identification. Firstly, multi-color features, which consisting of HSV, LAB, RGB and nRnG color features, were extracted and concatenated into a whole feature vector. Then the D-optimal Partial Least Squares feature selection method was adopted to select an optimal feature subset that could minimize the variance of the regression model. Finally, an asymmetric distance model for similarity matching was utilized to observe distinctive features from a different perspective. Experimental results show that rank 1 performance of the proposed method were 48.67%, 63.12% and 65.04% respectively on the VIPeR, Prid_450s and CUHK01 databases, which have achieved state-of-art performances.
Speaker Identification Using Tensor Decomposition of Acoustic Models

This paper explores speaker identification based on the speaker adaptation via multilinear decomposition of a speaker model.Tucker decomposition of the third order mean Tensor of training speaker yields three subspaces corresponding to each mode. The mean of the mixtures for speakers is expressed as a product of the mixture space and a weight matrix comprising of the other two spaces.We update only the mean of the mixtures for the adaptation stage . During testing, log likelihood is used to identify the scores for the test speakers.Experiments conducted on the TIMIT and NIST 2003 databases shows comparable performance with the baseline even in channel mismatch conditions of the development and enrollment dataset. Furthermore, using higher order tensors, we can easily adapt this problem to include noise and environment factors as well.
An Improved LSTM For Language Identification

In this paper, we propose a novel framework by combining the phonetic temporal neural model (PTN) with an improved LSTM (IM-LSTM). This is achieved by using an up-down connection from the time t to t+1 in the LSTM structure, which aims to capture the latent information from the previous time step. This updated structure can perform better to discriminate the frame-level phonetic information produced by PTN. On the AP16-OLR language identification dataset, our final model achieves relative growth rate 5.04%, 2.19%, 2.73% on EER and 6.55%, 5.81%, 2.23% on Cavg in 1s, 3s and full-length utterance condition than the standard PTN, respectively. The proposed framework receives a better performance than the standard PTN and other proposed models, particularly in 1s condition. This shows the efficacy and flexibility of the proposed method.
Community Detection and Mining Using Complex Networks Tools in Social Internet of Things

in recent time rapid and extraordinary technological advancement is dominated by the social internet of things (SIoT). SIoT connects people together socially and opens doors to people, to share ideas by using this information. Typically, SIoT deals with the massive amount of data and information. This data is used by various online social networks (ONS), i.e. Twitter, LinkedIn and Facebook etc. analyzing and mining of useful extracted information from these social networks is not an easy task. SIoT has a special interest in numerous research fields, i.e. computer sciences and social sciences. The detection and mining of a community reveal how the structure affects the people and their relationships. In order to facilitate the community discovery, a wide range of tools has been developed over years. Each of them differs from other, in respect of features and benefits. Choosing the right tool is somehow a difficult task. In order to overcome this difficulty, our work offers an analysis by dividing them into various categories such as network platform, algorithm complexity, community detection and their execution time. Finally, we discussed various visualization layouts of social networks which are helpful in order to precise the network data.
Context Aware Computing Systems: A survey

The term context has been studies in different areas of Computer Science. Context-aware systems have high demand in areas like Intelligent Environments, Pervasive & Ubiquitous Computing and Ambient Intelligence. Such systems gather data and adapting system behavior accordingly using context information like physical context, computational context, and user context/tasks. Developing such context-aware applications is inherently complex and hence should be supported by adequate context information modeling and reasoning techniques. In this paper the concept of context awareness is discussed along with requirements for context identification and formulation, procedure for converting context modeling to intelligent actions and context recognition techniques & algorithms are discussed. The applications of context aware are reviewed.
Performance Evaluation of Frame-level Parallelization in HEVC Intra Coding Using Heterogeneous Multicore Platforms

High Efficiency Video Coding (HEVC) is the latest video coding standard released as a successor of H.264/AVC, it expected to reduce the bitrate by 50% for the same perceptual quality. One of the major contributors to the higher compression performance of HEVC is the introduction of larger Coding Units (CU) with recursive partitioning mechanisms. This achievement in performance is accompanied by a high computational complexity, making this new standard very difficult to be embedded in current multimedia services and broadcast platforms. In this paper, a performance evaluation of All-Intra (AI) parallel realization of HEVC encoder is proposed, using a heterogeneous Octa-core CubieBoard4 platform that includes two quad-core ARM A7 and ARM A15. We used the OpenMP paradigm for parallel realization where each thread is assigned to a core processor to encode a separate frame. AI configuration is used to break coding dependencies between successive frames, which allow the parallel processing of a set of images. Experimental results shows that the proposed parallel realization of HEVC encoder, using eight threads, reduces the computational complexity to about 4.35×, without any loss in coding performance. These results do not match to the expected acceleration due to the heterogeneity of the platform.
Pole-Shared Realization of Complex Wavelet Base Using AFSA

In this paper, the method for pole-shared approximation of complex wavelet bases has been proposed, which mainly involves the rational fraction approximation of Gaussian envelope and the Laplace transform of the product of approximation function and sine or cosine function. To enhance the approximation accuracy of Gaussian envelope, the mathematical approximation model has been constructed, and the Artificial Fish Swarm Algorithm is used for the optimization. Finally, the complex Morlet wavelet is used as an example in this paper to illustrate the construction and optimization procedure of the approximation model for Gaussian envelope. Experimental results show that the approximation functions of the real and the imaginary part of complex wavelet base have the same poles, and also the approximation accuracy for modulus and phase is relatively high.
4×128Gb/s Faster-than-Nyquist 16-QAM WDM Transmission with Heterodyne Detection

We demonstrate 4×128Gb/s Faster-than-Nyquist 16-QAM WDM signal transmission with heterodyne detection using signal-signal beat interference cancellation. The overall BER achieves 2.7×10-3 (< 7% HD-FEC) after 80km SSMF transmission. The net spectral efficiency is as high as 4.04bit/s/Hz for one polarization.
Dynamic Bending Reliability Analysis of Flexible Hybrid Integrated Chip-Foil Packages

With the emergence of the Internet-of-Things (IoT) and wearable devices in the recent years, Flexible Hybrid Electronics (FHEs) has attracted significant attention. Chip-foil packages (also known as flexible interposers) fabricated by integrating ultra-thin silicon ICs onto or embedded into polymer foils comprising of metal interconnects (wiring lines, through hole via interconnects etc.) for powering up the system and transmission of data signals / IO commands are ideal candidates for FHE integration. The principal advantages of chip-foil packages over standard Surface Mount Device (SMD) components are their bend ability and conformability. However, the behavior of chip-foil packages under repeated bending must be analyzed in detail through extensive investigations to enable the transfer of the technology from research labs to industrial manufacturing platforms. Hence, we conducted two different types of bending tests to examine the repeated or dynamic bending reliability of the wiring lines and the via interconnects of the chip-foil packages. The experimental results highlight the need for design optimized dimensioning of the wiring lines and the via interconnects for manufacturing FHEs with high performance and good dynamic bending reliability. Such results complement the state-of-the-art information available regarding the electrical as well as mechanical reliability of chip-foil packages and are required as essential information for establishing guidelines for handling chip-foil packages during integration as well as for producing FHEs that are more reliable.
A Survey of Fine-Grained Image Categorization

It is known that the ability of humans to perform basic-level visual classification (e.g. birds versus dogs) develops well before their ability to perform fine-grained visual categorization (e.g. distinguish different sub-categories from Hooded Oriole to Scott Oriole). Similarly, computer vision research follows such trajectory. Basic-level image classification has made great progress with the help of deep learning models in recent years, while fine-grained image categorization is still facing a number of challenges and attracting more and more attention. In this paper, we review the recent progress in fine-grained image categorization. Starting from its definition, we give a brief introduction to some recent developments in fine-grained image categorization. After that, we elaborate different algorithms from strongly supervised learning and weakly supervised learning, and compare their performances on four publicly available benchmarks. Finally, we provide a brief summary of these methods as well as the potential future research directions, i.e. suggest to explore deeper neural networks and generative adversarial networks.
Function Risk Assessment Under Memory Leakage

Code reuse attack (CRA), specifically reusing and then reconstructing the codes (gadgets) already existed in programs and libraries, is widely exploited in software attacks. Admittedly, determination of the location of the gadgets consisted of target instructions along with control flow transfer instructions, is of critical importance. Address Space Randomization (ASR), which serves as an effective technique to mitigate CRA, increases the entropy by randomizing the location of the code or data, and baffles adversaries from figuring out the memory layout. Currently, variable randomization methods of high granularity are proposed by scholars to prevent adversaries from deducting memory layout. However, their credibility on alleviating CRA is yet to be confirmed, especially when the suitable pointer is exposed to adversaries. In this paper, we focus on studying what kinds of function leakage can lead to a CRA more likely. A function risk assessment model focusing on function coupling is proposed to quantify the risk caused by the suitable function pointer leakage and it is extended to assess the risk of the whole program and library under the memory leakage. Our experimental results show that popular open-source software is vulnerable when certain code pointer is leaked to adversaries and even severer when the system library is accessible. In addition, suggestions to eliminate function coupling and evaluate the availability of randomization methods are further discussed.
Channel Access Control for Collisions Caused by Hidden Nodes and Phase Synchronization among Periodic Data Flows

With the rapid increase in IoT (Internet of Things) applications, more sensor devices, generating periodic data flows whose packets are transmitted at regular intervals, are being incorporated into WSNs (Wireless Sensor Networks). However, packet collision caused by the hidden node problem becomes serious particularly in large-scale multi-hop WSNs. Moreover, focusing on periodic data flows, continuous packet collisions among periodic data flows are caused once periodic packet transmission phases are synchronized. In this paper, we tackle the compounded negative effect of the hidden node problem and the continuous collision problem among periodic data flows. To realize this objective, we propose a new MAC layer mechanism. The proposed method predicts risky durations during which collision can be caused by hidden nodes by taking into account periodic characteristics of data packet generation. In the risky duration, each sensor node stops the transmission of packets in order to avoid collisions. To the best of our knowledge, this is the first paper that considers the compounded effect of hidden nodes and continuous collisions among periodic data flows. Other advantages of the proposed method include that any new control packets are not required and it can be implemented in widelydiffused IEEE 802.11 and IEEE 802.15. 4 devices.
Analysis of Small-World Features in Vehicular Social Networks

In this work, we demonstrate that Vehicular Social Networks (VSNs) require specific data dissemination techniques. To this aim, we consider the Barabási model and we study the impact of its use for data dissemination in VSNs. Moreover, the use of a probabilistic data dissemination protocol based on social features [1] proves that the time-evolving dynamics of the vehicular social network graph presents a small-world structure, where nodes tend to connect through clusters whose average distance is low on average. This result highlights how VSNs are a specific class of vehicular ad hoc networks with peculiar features that distinguish them from classical online social networks.
Analysis of Inversely Proportional Carrier Sense Threshold and Transmission Power Setting Based on Received Power for IEEE 802.11ax

In this study, we conducted an analysis of the system performance of a wireless local area network in which access points (APs) dynamically adjust the carrier sense threshold (CST) based on the individual average received power to determine the optimal CST. Adjustment of the CST is a promising approach to improve spatial reuse and proposed for IEEE 802.11ax standard. Here, assuming to adopt the inversely proportional setting of the CST and transmission power, we can make the carrier sensing relationship symmetric, restraining throughput starvation. This paper analytically derives the density of successful transmissions (DST) on the basis of stochastic geometry. The DST is a system performance metric which expresses the number of APs whose transmission is successful based on signal-to-interference-plus-noise power ratio. We show that both results of the analytically derived DST and Monte Carlo simulation have the same trend. From the perspective of the derived DST, the optimal parameter setting is also discussed.
An Overview: Security Issue in IoT Network

The IoT expedients are formed on cost-effective devices and wireless communique methods to connect with each other and propel mobile facts to the Unified system. This paper focuses on major safety problems of IoT particularly, stressing the cybercrimes and counter attacks. Number of IoT expedients become a target of cyber-attacks because of deficiency in safety mechanism in IoT expedients. This paper discusses the safety necessities like secrecy, unity, and substantiation etc. This study contains twelve different kinds of attacks , for example, low-graded attacks, average level attacks, great attacks ad particularly high intensity attacks along with their nature or behavior as well as resolutions to cater these attacks are discussed.