Feature Compression Research Articles

Double and triple compression-based forgery detection in JPEG images using deep convolutional neural network

Recompression-based image forensics has been a largely investigated area in the forensic community over recent years. This mainly involves detecting the number of different compressions in an image by exploiting certain artifacts left behind by the compression stages. The artifacts left in images by Joint Photography Experts Group (JPEG) compression coding operations play an important role in passive forensic analysis of images, which help in assessing the authenticity of the image. In this regard, most of the solutions proposed to date are restricted to double compression detection in JPEG images. However, in practical cases, this restriction may not always work. This is because an image having undergone multiple stages of compression, including triple or higher degrees of compression, will be “missed” by such forgery detection algorithms. To address this issue, we propose a deep convolutional neural network (CNN) model that detects both double and triple compression in JPEG images. The model is trained with the compression features specific to single, double, and triple JPEG compressions. The trained model is capable of determining the authenticity of an image by distinguishing between tampered and untampered image regions. Further, the proposed model localizes the forged regions within an image, distinguishing between double and triple compressed regions. Our experimental results establish the efficiency of the proposed model.

Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning

Object tracking is a critical research in computer vision and has attracted significant attention over the past few years. However, the traditional object tracking algorithms often suffer from the object drifting problem due to various challenging factors in complex environments such as object occlusion and background clutter. This paper proposes a robust and effective object tracking algorithm, called MCM, which combines compressive sensing and online multiple instance learning in a multi-classifier fusion framework. In this framework, we integrate the different discriminative classifiers by learning the varied and compressed feature vectors based on different random projection matrices. And then an improved online multiple instance learning mechanism SMILE is adopted, which introduces the relative similarity to select and weight the instances in the positive bag. The experiments show that the proposed algorithm can improve the performance of object tracking on the challenging video sequences.

Classification of electromyographic hand gesture signals using machine learning techniques

The electromyogram (EMG) signals from an individual’s muscles can reflect the biomechanics of human movement. The accurate classification of individual and combined finger movements using surface EMG signals is able to support many applications such as dexterous prosthetic hand control. The existing research of EMG-based hand gesture classification faces the challenges of inaccurate classification, insufficient generalization ability and weak robustness. To address these problems, this paper proposes a deep learning model that combines convolutional auto-encoder and convolutional neural network (CAE+CNN) to classify an EMG dataset consisting of 10 classes of hand gestures. The proposed method shrinks the inputs into a smaller latent space representation using CAE and the resultant compressed features are served as inputs of CNN, which reduces the redundancy of EMG signals and improves the classification accuracy and training efficiency. Besides, to enhance the robustness and generalization ability for classification, a data processing approach is proposed which combines the windowing method and majority voting of the obtained results from the classifier. In addition, comprehensive comparative study is carried out with 8 widely applied and state-of-the-art classifiers in terms of classification accuracy, robustness subject to noise and statistical analysis (sensitivity, specificity, precision, F1 Score and Matthews correlation coefficient). The results demonstrates that the integration of windowing method, CAE+CNN and majority voting achieves the best performance (99.38% test accuracy for the data without adding noise, which is 3.78% higher than the best classifier used for comparison), strongest robustness (achieved 98.13% test accuracy when Gaussian noise of level 1e-5 is added to the raw dataset, which is 4.07% higher than the best classifier used for comparison) and statistical properties compared to other classifiers, which shows the potential for healthcare applications such as movement intention detection and dexterous prostheses control.

Feature selection with missing labels based on label compression and local feature correlation

Feature selection can efficiently alleviate the issue of curse of dimensionality, especially for multi-label data with multiple features to embody diverse semantics. Although many supervised feature selection methods have been proposed, they generally assume the labels of training data are complete, whilst we only have data with incomplete labels in many real applications. Some methods try to select features with missing labels of training data, they still can not handle feature selection with a large and sparse label space. In addition, these approaches focus on global feature correlations, but some feature correlations are local and shared by a subset of data. In this paper, we introduce an approach called Feature Selection with missing labels based on Label Compression and Local feature Correlation (FSLCLC for short). FSLCLC adopts the low-rank matrix factorization on the sparse sample-label association matrix to compress labels and recover the missing labels in the compressed label space. In addition, it utilizes sparsity regularization and local feature correlation induced manifold regularizations to select the discriminative features. To solve the joint optimization objective for label compression, recovering missing labels and feature selection, we develop an iterative algorithm with guaranteed convergence. Experimental results on benchmark datasets show that the proposed FSLCLC outperforms the state-of-the-art multi-label feature selection algorithms.

On compressive and morphological features of 3D printed almond skin powder reinforced PLA matrix

This paper reports the compressive, morphological and surface hardness properties of almond skin powder reinforced polylactic acid (PLA) matrix based 3D printed functional prototypes prepared by fused filament fabrication (FFF) as biomedical scaffolds. In this study the 3D printed functional parts were subjected to compression, morphological and Shore D hardness investigations. The results of this study highlight that maximum compressive strength at peak (37.712 MPa), maximum compressive strength at break (33.93 MPa) and Young’s modulus (387.80 MPa) was observed in case of sample printed at infill density 100%, infill angle 90° and infill speed 70 mm s−1 (as per ASTM D695). The maximum modulus of toughness as 3.47 MPa was observed in the case of printed sample at infill density 60%, infill angle 60° and infill speed 70 mm s−1. The results are supported by optical photomicrographs and Shore D hardness. Finally mathematical equations were developed (as empirical model) to predict different mechanical properties (based upon historical data approach) supported by Taguchi analysis.

Recognition of Negative Emotion using Long Short-Term Memory with Bio-Signal Feature Compression.

Negative emotion is one reason why stress causes negative feedback. Therefore, many studies are being done to recognize negative emotions. However, emotion is difficult to classify because it is subjective and difficult to quantify. Moreover, emotion changes over time and is affected by mood. Therefore, we measured electrocardiogram (ECG), skin temperature (ST), and galvanic skin response (GSR) to detect objective indicators. We also compressed the features associated with emotion using a stacked auto-encoder (SAE). Finally, the compressed features and time information were used in training through long short-term memory (LSTM). As a result, the proposed LSTM used with the feature compression model showed the highest accuracy (99.4%) for recognizing negative emotions. The results of the suggested model were 11.3% higher than with a neural network (NN) and 5.6% higher than with SAE.

CoNNa–Hardware accelerator for compressed convolutional neural networks

In this paper, we propose a novel Convolutional Neural Network hardware accelerator called CoNNA, capable of accelerating pruned, quantized CNNs. In contrast to most existing solutions, CoNNA offers a complete solution to the compressed CNN acceleration, being able to accelerate all layer types commonly found in contemporary CNNs. CoNNA is designed as a coarse-grained reconfigurable architecture, which uses rapid, dynamic reconfiguration during CNN layer processing. The CoNNA architecture enables the on-the-fly selection of the CNN network that should be accelerated and also supports the acceleration of CNN networks with dynamic topology. Furthermore, by being able to directly process compressed feature and kernel maps, and skip all ineffectual computations during CNN layer processing, the CoNNA CNN accelerator is able to achieve higher CNN processing rates than some of the previously proposed solutions. The CoNNA architecture has been implemented using Xilinx ZynqUtrascale+ FPGA family and compared with seven previously proposed CNN hardware accelerators. Results of the experiments seem to indicate that the CoNNA architecture is up to 14.10, 6.05, 4.91, 2.67, 11.30, 3.08 and 3.58 times faster than previously proposed MIT's Eyeriss, NullHop, NVIDIA's Deep Learning Accelerator (NVDLA), NEURAghe, CNN_A1, fpgaConvNet, and Deephi's Aristotle CNN accelerators respectively, while using identical number of computing units and operating at the same clock frequency.

A Cytokine Protein Identification Model Based on the Compressed PseKRAAC Features

Cytokine proteins, which form a complex cytokine regulatory network, participate in a variety of important physiological functions of the human body. Identification of cytokine proteins is very important and has attracted the attention of many researchers. In this paper, we propose a MRMD-cosine model based on the PseKRAAC features to identify the cytokine proteins. First, the PseKRAAC feature extraction method is used to extract four kinds of feature sets from the cytokine proteins, named type1 g-gap, type1 lambda, type2 g-gap and type2 lambda feature sets. Then the MRMD algorithm is used to remove the redundant features from the feature sets. Three kinds of metrics are used by the MRMD algorithm to measure the redundancy of a feature set, which are the Euclidean distance, Cosine similarity and Tanimoto coefficient. Bagging and random forest algorithms are used to construct the classification models based on the compressed feature set. The experimental results show that the MRMD-cosine model based on the type1 lambda feature set constructed by the random forest algorithm can achieve the best performance among all models. Finally, we compare the performance of the MRMD-cosine model with another state-of-art model, named greedy based feature compression model based on the CNT features. It shows that the MRMD-cosine model uses only 15% features of the greedy based model to achieve a better accuracy.

Дослідження розподілення електричного струму при відновленні автомобільних деталей контактним наварюванням

The goal of completed research is determination of common factors of dynamic electricity distribution in the area of simple welding spot forming due to contact welding-on compact and powdered materials. Due to contact welding-on of compact (rod, belt) and powdered materials there is the dissimilarity of an electric field, which determines the unequal distribution of temperature field in the area of the moulded core creating and influence on the welded on covering quality. By the contact welding-on of compact materials, there is an occurrence of central core zone unheating, which is coming as a result of geometrical, temperature and magnetic-electric factors. During contact welding of metal powders, considering high speed of heating and a small-time of dynamic electricity passage through powder, it is possible to make a conclusion, that the stability of heat generation in a powder surface will be determined by starting electricity resistance value. High-quality plating can be reached if the powder charge will have low and stable electricity resistance in a cross-section and good compactness under the condition of low compress pressure, which is used for welding. (40 ... 70 MPa). Electrical field dissimilarity with contact welding-on powdered materials leads to unequal heating of powdered materials and covering defects, which is taking place due to the features of powder compression. As a result of current lines concoction in the central area is observed at a local temperature increase. Besides, interstices are dielectrical phase and increase electrical resistance in the peripheral areas. To increase the quality of coverings, got by contact welding-on is able due to providing high-quality powder material compression on the whole covering cut.

Feature-Compression-Based Detection of Sea-Surface Small Targets

This paper aims to develop a feature-based detector using seven existing salient features of radar returns to improve the detection ability of high-resolution maritime ubiquitous radars to sea-surface small targets. Maritime ubiquitous radars form simultaneously dwelling beams at multiple azimuths by digital array receiver and allow long observation time for detection. Due to absence or incompletion of training samples of radar returns with various types of sea-surface small targets, the detection boils down to designing a one-class classifier in the seven-dimensional (7D) feature space mainly by using training samples of sea clutter. A feature compression method, though maximizing interclass Bhattacharyya distance, is proposed to compress the 7D feature vector into one 3D feature vector with the help of simulated radar returns of typical targets. In the compressed 3D feature space, a modified convexhull learning algorithm is given to determine one convex polyhedron decision region of sea clutter at a given false alarm rate. In this way, a feature-compression-based detector is constructed, which can exploit more features of radar returns to improve detection performance. It is verified by the recognized and open IPIX and CSIR radar databases for sea-surface small target detection. The results show that it attains obvious performance improvement.

Efficient BiSRU Combined With Feature Dimensionality Reduction for Abnormal Traffic Detection

Abnormal traffic detection is an important network security technology to protect computer systems from malicious attacks. Existing detection methods are usually based on traditional machine learning, such as Support Vector Machine (SVM), Naive Bayes, etc. They rely heavily on manual design of traffic features and usually shallow feature learning, which get a low accuracy for high-dimensional traffic. Although the method based on Long Short-Term Memory (LSTM) has an excellent ability to detect abnormal traffic. The sequence-dependent structure of LSTM cannot realize parallel computation, which leads to slow model training and limits its applicability. To address the above problem, we propose an efficient Bidirectional Simple Recurrent Unit (BiSRU) combined with feature dimensionality reduction for abnormal traffic detection. Specifically, in order to perform feature dimensionality reduction on the original high-dimensional network traffic, we design a stack Sparse Autoencoder (sSAE) to extract the compressed high-level features. For the purpose of realizing efficient parallel computation and accurate feature extraction, a BiSRU is utilized to extract the bidirectional structural features of the traffic. Finally, the experimental results show that our proposed method significantly outperforms existing methods in terms of accuracy and training time. The method we propose can timely and accurately detect various abnormal traffic and achieve effective network security protection.

The Feature Compression Algorithms for Identifying Cytokines Based on CNT Features

As the signaling proteins, cytokines regulate a wide range of biological functions. It is important to distinguish the cytokines from other kinds of proteins. The 188-Dimensional CNT features are presented to identify the cytokines, which contain many redundant features. In this paper, we propose three kinds of feature compression algorithms to exclude the redundant features from the 188D features and keep the accuracy of the algorithm at the same time. The three algorithms are called the genetic based algorithm, the greedy based algorithm and the brute-force based algorithm. Experimental results demonstrate that the brute-force based algorithm gets the highest classification accuracy among the three algorithms. The genetic based algorithm achieves the least number of compressed features among the three algorithms. But they consume much more time than that consumed by the greedy based algorithm. The greedy based algorithm makes a good trade-off among the three factors, which are the classification accuracy, the number of compressed features and the time consumption.

A Content-Based Method for Sybil Detection in Online Social Networks via Deep Learning

Online social networks (OSNs) are generally susceptible to Sybil attack, which causes a series of cybersecurity problems and privacy violations. Malicious attackers can create massive Sybils and further utilize those fake identities to launch various Sybil attacks. Therefore, Sybil detection in OSNs has become an urgent security research problem for both academia and industries. The existing content-based methods to detect Sybils base on the combination of manual-design features and machine learning algorithms, which requires lots of professional experiences and human effort. These methods divide the Sybil detection problem into two piece-wise sub-problems, which prevents us from getting the optimal solution. In this work, we propose a novel content-based method to detect Sybils. The proposed method is an end-to-end classification model that extracts features directly from the input data, and then output the classification results in a unified framework. The proposed method includes three main parts: first, the self-normalizing convolutional neural network (CNN) is adopted to extract lower features from the multi-dimensional input data; second, the bidirectional self-normalizing LSTM network (bi-SN-LSTM) is developed to extract higher features from the compressed feature map sequence; third, the dense layer and softmax classifier are stacked to output the classification results. Unlike the traditional bidirectional long short-term memory network (bi-LSTM), the proposed bi-SN-LSTM network utilizes SELU as the activation function of its recurrent step, which provides unbounded changes to the state value. Through the case study of the real-world dataset, the comparison experiments demonstrate that our method significantly outperforms other state-of-the-art methods.

Compression Features of Ice Composite Materials with Natural Reinforcing Fillers

Compression of ice and composition materials based on ice have shown that reinforcement of ice matrix with plant fillers (hay and sawdust) increases the strength and plastic properties of the materials. The fracture pattern of the specimens has been described.

Pullout strength behaviour of self-compacted concrete in aggressive solutions

The aim of this study was to investigate the pullout strength behaviour of self-compacted concrete under corrosion conditions. Block samples with two configurations were formulated. A total number of twelve block samples in addition to 24 cubes were tested. The hardened concrete samples were either cured in tap water or exposed to salt solution. A suitable epoxy was used for coating part of the formulated samples. The preliminary mechanical properties and the rate of corrosion were firstly measured, the pullout was then carried out. A comparison was also made with the relevant code of practice. The results obtained showed that both compressive strength and density features exhibited a notable increase at an early age when the samples are exposed to salt solution. The adverse effect of such curing condition appeared at the later ages when the corrosion rate became more intensive. The highest decrease in the value of pullout strength was observed for the reinforced concrete block samples containing epoxy coating reaching to 44.5% less than those cured in tap water.

Yutma güçlüğüne neden olan retrofarengeal kitle: servikal kordoma

Kordomalar, notokordun embriyolojik artıklarından kaynaklanan nadir primer kemik tümörleridir. Servikal bölge yerleşimli kordomalar oldukça nadir görülmektedir. Tüm kordomaların %5 kadarını oluşturan servikal kordomalar, komşu oldukları parafarengeal bölge, orofarenks gibi anatomik boşluklara uzanım gösterip; boyunda şişlik, orofarenkste kitle, yutma güçlüğü, nefes darlığı benzeri klinik bulgular verebilirler. Bu olgu sunumunda yutma güçlüğü şikayeti ile başvuran ve orofarenkste kitle olarak bulgu veren servikal kordoma olgusunun klinik özellikleri sunulmuş, tanıda yaşanan güçlükler ve cerrahi bulgular tartışılmıştır.

Direct sequence spread spectrum communication narrowband interference cancelation in compressed domain

AbstractDue to the broad bandwidth nature of the direct sequence spread spectrum (DSSS) signal, the application of narrowband interference (NBI) cancelation algorithms based on the Nyquist‐Shannon sampling theory are limited by the high sampling rate. To solve the sampling and processing difficulty in the traditional DSSS communication interference cancelation method, we used the compressive sensing (CS) to reduce the sampling rate of the DSSS communication system, and further proposed a compressed domain NBI elimination method in this study. We proved that NBI with a certain bandwidth and DSSS signal are both sparse in specific dictionaries and proposed the corresponding sparse dictionary construction method. Detailed analysis of the compressed domain features of NBI and the DSSS signal demonstrated that it is possible to extract the strong NBI components from the compressed measurements. Two compressed domain NBI cancelation algorithms were proposed based on this idea. The extracted NBI components were filtered out from the compressed measurements directly in the first algorithm, and then we achieved the DSSS signal demodulation in compressed domain using the separated DSSS signal components with the orthogonal matching pursuit (OMP) algorithm. A proposed extraction filter block sparse Bayesian learning (EFBSBL) framework algorithm was utilized to estimate the NBI from the compressed measurements in the second algorithm. The estimated NBI was eliminated from the received signal, and then we recompressed the DSSS signal after NBI cancelation and realized the DSSS signal demodulation in compressed domain with the OMP algorithm. Simulation results support our theoretical analysis results and reveal that the two proposed algorithms are both effective for NBI cancelation and significant interference cancelation performance are achieved.

The effect of renin-angiotensin system blockers on spinal cord dysfunction and imaging features of spinal cord compression in patients with symptomatic cervical spondylosis

BACKGROUND CONTEXTCervical spondylosis may lead to spinal cord compression, poor vascular perfusion, and ultimately, cervical myelopathy. Studies suggest a neuroprotective effect of renin-angiotensin system (RAS) inhibitors in the brain, but limited data exist regarding their impact on the spinal cord. PURPOSETo investigate whether RAS blockers and other antihypertensive drugs are correlated with preoperative functional status and imaging markers of cord compression in patients with symptomatic cervical spondylosis. STUDY DESIGNRetrospective observational study. PATIENT SAMPLEIndividuals with symptomatic degenerative cervical stenosis who underwent surgery. OUTCOME MEASURESImaging features of spinal cord compression and functional status (modified Japanese Orthopedic Association [mJOA] and Nurick grading scales). METHODSTwo hundred sixty-six operative patients with symptomatic degenerative cervical stenosis were included. Demographic data, comorbidities, antihypertensive medications, and functional status (including mJOA and Nurick grading scales) were collected. We evaluated canal compromise, cord compromise, surface area of T2 signal cord change, and pixel intensity of signal cord change compared with normal cord on T2-weighted magnetic resonance imaging sequences. RESULTSOf 266 patients, 41.7% were women, 58.3% were men; median age was 57.2 years; 20.6% smoked tobacco; 24.7% had diabetes mellitus. One hundred forty-nine patients (55.8%) had hypertension, 142 (95.3%) of these were taking antihypertensive medications (37 angiotensin-II receptor blockers [ARBs], 44 angiotensin-converting enzyme inhibitors, and 61 other medications). Patients treated with ARBs displayed a higher signal intensity ratio (ie, less signal intensity change in the compressed cord area) compared with untreated patients without hypertension (p=.004). Patients with hypertension had worse preoperative mJOA and Nurick scores than those without (p<.001). In the multivariate analysis, ARBs remained an independent beneficial factor for lower signal intensity change (p=.04), whereas hypertension remained a risk factor for worse preoperative neurological status (p<.01). CONCLUSIONSIn our study, patients with hypertension who were treated with RAS inhibitors had decreased T2-weighted signal intensity change than untreated patients without hypertension. Patients with hypertension also had worse preoperative functional status. Prospective case-control studies may deepen understanding of RAS modulators in the imaging and functional status of chronic spinal cord compression.

Multiresolution Compressive Feature Fusion for Spectral Image Classification

Compressive spectral imaging (CSI) has emerged as an alternative acquisition framework that simultaneously senses and compresses spectral images. In this context, the spectral image classification from CSI compressive measurements has become a challenging task since the feature extraction stage usually requires reconstructing the spectral image. Moreover, most approaches do not consider multi-sensor compressive measurements. In this paper, an approach for fusing features obtained from multi-sensor compressive measurements is proposed for spectral image classification. To this end, linear models describing low-resolution features as degraded versions of the high-resolution features are developed. Furthermore, an inverse problem is formulated aiming at estimating high-resolution features including both a sparsity-inducing term and a total variation (TV) regularization term to exploit the correlation between neighboring pixels of the spectral image, and therefore, to improve the performance of pixel-based classifiers. An algorithm based on the alternating direction method of multipliers (ADMM) is described for solving the fusion problem. The proposed feature fusion approach is tested for two CSI architectures: three-dimensional coded aperture snapshot spectral imaging (3D-CASSI) and colored CASSI (C-CASSI). Extensive simulations on various spectral image data sets show that the proposed approach outperforms other classification approaches under different performance criteria.

Intraspinal Extension of Neuroblastoma: a Report of Two Cases

Neonatal NB is the most common extracranial solid tumor, accounting for 28–39% of all solid tumors and 2% of all pediatric malignancies with an incidence of 0.61/100000 live birth [1, 2]. Intraspinal extension of NB is exceptionally rare, and thus, only a few cases have been reported in the literature till now. Five to ten percent of cases of NB may present with neurological symptoms like limb weakness and bladder or bowel dysfunction because of epidural compression [3]. Thus, any paraspinal mass with neurological symptoms requires urgent investigation and intervention. We report two cases of NB with features of cord compression that were treated in our institute.