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ABSTRACT An FPGA implementation of the 128-bit SEED block cipher is presented in this paper. The proposed architecture achieves high-speed with little hardware resources using feedback logic and inner pipeline with negative edge-triggered... more
ABSTRACT An FPGA implementation of the 128-bit SEED block cipher is presented in this paper. The proposed architecture achieves high-speed with little hardware resources using feedback logic and inner pipeline with negative edge-triggered registers. In this way, the delay of the critical path is reduced, without increasing the latency of cipher execution. The proposed implementation reaches a data throughput of 369.6 Mbps at 46.2 MHz clock frequency. The design was coded using VHDL language and for the hardware implementation, the Xilinx Spartan-3A FPGA device was used.
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In the present contribution two schemes that combine REG and Segmented Image Coding (SIC) for the progressive transmission of digital images, are described. Compared to JPEG-based progressive transmission schemes, the new ones produce... more
In the present contribution two schemes that combine REG and Segmented Image Coding (SIC) for the progressive transmission of digital images, are described. Compared to JPEG-based progressive transmission schemes, the new ones produce reconstructed images of better quality across all stages of transmission, while transmitting approximately the same number of bits. The computational complexity of the new schemes is lower than that of the SIC-only approaches.
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Finger vein recognition is a biometric method of authentication that offers high security, efficiency and stability. In this paper we propose a new finger vein recognition system that utilizes the Enhanced Maximum Curvature Points (EMC)... more
Finger vein recognition is a biometric method of authentication that offers high security, efficiency and stability. In this paper we propose a new finger vein recognition system that utilizes the Enhanced Maximum Curvature Points (EMC) technique for finger vein pattern extraction and introduces a new pre-processing stage. In addition, it combines two matching methods, leading to better recognition performance in terms of EER, FAR, FRR and recognition rate than other methods. We present the experimental results obtained by applying our system on the databases SDUMLA-HMT, Tsingua, FV-USM and HKPU and compare them with similar approaches applied on these databases.
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In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often... more
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using Temporal Convolutional Networks. The proposed network yields an improvement in gesture recognition of almost 5% to the state of the art reported in the literature, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.
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The latest advancements in the field of deep learning and biomedical engineering have allowed for the development of myoelectric interfaces based on deep neural networks. A longstanding problem of these interfaces is that the models... more
The latest advancements in the field of deep learning and biomedical engineering have allowed for the development of myoelectric interfaces based on deep neural networks. A longstanding problem of these interfaces is that the models cannot easily be applied to new users due to the high variability and stochastic nature of the electromyography signals. Further training a new model for every new subject requires the collection of large volumes of data. Therefore, this work proposes a transfer learning (TL) scheme which allows reusing the knowledge of a pre-existing model for a new user. Firstly, a convolutional neural network (CNN) is trained on an initial dataset using the data of multiple subjects. Then, the weights of this model are fine-tuned for a new target subject. The approach is evaluated on the Ninapro datasets DB2 and DB7. The experimentation included three different CNN models and eight preprocessing alternatives. The results showed that the success of the TL method depends on how the data are preprocessed. Specifically, the biggest accuracy improvement (+5.14%) is achieved when only the first 20% of the signal duration is used.
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In the past few years, a great interest for the classification of hand gestures with Deep Learning methods based on surface electromyography (sEMG) signals has been developed in the scientific community. In line with latest works in the... more
In the past few years, a great interest for the classification of hand gestures with Deep Learning methods based on surface electromyography (sEMG) signals has been developed in the scientific community. In line with latest works in the field, the objective of our work is the construction of a novel Convolutional Neural Network architecture, for the classification of hand-gestures. Our model, while avoiding overfitting, did not perform significantly better compared to a much shallower network. The results suggest that the lack of diversity in the sEMG recordings between certain hand-gestures limits the performance of the model. In addition, the classification accuracy on a database we developed using a commercial device (Myo Armband) was substantially higher (approximately 24%) than a similar benchmark dataset recorded with the same device.
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Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of... more
Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed towards surface electromyography (sEMG) based gesture recognition, often addressed as an image classification problem using Convolutional Neural Networks (CNN). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals that are then classified by CNN. The proposed method is evaluated on different network architectures and yields a classification improvement of more than 3%.
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Due to the significance of the visual information exchanged in Internet of Video Things (IoVT) networks, attackers are constantly launching new attacks and attempt to exploit new vulnerabilities. One of the most common and difficult-to-... more
Due to the significance of the visual information exchanged in Internet of Video Things (IoVT) networks, attackers are constantly launching new attacks and attempt to exploit new vulnerabilities. One of the most common and difficult-to- prevent attacks on the Visual Layer is the Frame Duplication Attack (FDA). Recently, two techniques were proposed for FDA detection at the edge by using the embedded Electrical Network Frequency (ENF) signals in an effort to surpass limitations of conventional passive methods. In this paper, a Real-Time ENF signal hiding technique at the edge is proposed. Our motivation is to examine the possibility of authenticating the surveillance feed by hiding the ENF signal. Experiments are conducted, including an extensive performance comparison between the proposed and reference encoder, a feasibility study for the proposed encoder’s integration to a Raspberry Pi for video streaming purposes and finally the implementation of a proof-of-concept prototype. According to the findings, the proposed approach provides real- time FDA detection at reduced computational complexity and hardware requirements, thus rendering this method appropriate for applications at the edge.
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography... more
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architec...
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Over the past years, Deep Learning methods have shown promising results to a wide range of research fields including image classification and natural language processing. Their increased success rates have drawn the attention of many... more
Over the past years, Deep Learning methods have shown promising results to a wide range of research fields including image classification and natural language processing. Their increased success rates have drawn the attention of many researchers from various domains. This chapter investigates the application of Deep Learning methods to the problem of electromyography-based gesture recognition. A signal processing pipeline based on Deep Learning is presented through examples taken from the literature, whereas the details of state-of-the-art neural network architectures are discussed. In addition, this chapter illustrates a few ways adopted from image classification tasks that visualize what the neural network learns. Finally, new approaches are proposed and evaluated with publicly available datasets. Keywords— sEMG, gesture recognition, Deep Learning, signal processing
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The estimation of location based on the time varying Electric Network Frequency (ENF) is a new emerging technology in Information Forensics. This requires the extraction of the ENF signal from multimedia recordings and a comparison with... more
The estimation of location based on the time varying Electric Network Frequency (ENF) is a new emerging technology in Information Forensics. This requires the extraction of the ENF signal from multimedia recordings and a comparison with already known power grid signatures. In this paper, we focus on ENF signal extraction and statistical modelling of ENF signals. We introduce a novel technique based on instantaneous frequency estimation using the Hilbert transform, which shows promising results.
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Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks. Especially in the area of computer vision and speech processing, DL has recently... more
Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks. Especially in the area of computer vision and speech processing, DL has recently demonstrated better performance and generalisation properties, compared to classical machine learning approaches, which are based on the extraction of hand-crafted model-based features followed by classification. Hand gestures and speech constitute two of the most important modalities in human-to-human communication and man-machine interaction. In biomedical engineering, a lot of new work is directed towards electromyography-based gesture recognition. In this paper, we present a brief overview of DL methods for electromyography-based hand gesture recognition and then we select from literature a simple model based on Convolutional Neural Networks that we consider as the baseline model. The proposed modifications to the baseline model yield a 3% class...
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Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly... more
Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variet...
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With the development of computer graphics rendering software, it has become extremely difficult to distinguish whether an image is computer generated or a natural one. Therefore, it is really important to device robust methods for... more
With the development of computer graphics rendering software, it has become extremely difficult to distinguish whether an image is computer generated or a natural one. Therefore, it is really important to device robust methods for correctly classifying these two categories of images. In this work, a new approach to face the above problem is developed that is based upon the discrete cosine transform (DCT) of an image, in the YCbCr color space. The statistical features extracted, have been tested in suitable databases and the remarkable results indicate that the proposed model has a great potential to be used in digital images forensics.
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Falls can be severe enough to cause disabilities especially to frail populations. Thus, prompt health care provision is essential to prevent and restore any harm. The purpose of this study is to develop a smartphone-based fall detection... more
Falls can be severe enough to cause disabilities especially to frail populations. Thus, prompt health care provision is essential to prevent and restore any harm. The purpose of this study is to develop a smartphone-based fall detection system that can distinguish between falls and activities of daily living (ADL). The typical fall detection system consists of a sensing component and a notification module. Android devices, equipped with sensors and communication services, are the best candidates for the development of such systems. This work incorporates a threshold based algorithm, whose accuracy is enhanced by a k Nearest Neighbor (kNN) classifier. In addition, this paper proposes the implementation of a personalization and power regulation system. It achieves high fall detection accuracy, (97.53% sensitivity and 94.89% specificity), which is comparable to related works.
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DCT and fractal compression incorporate certain advantages and disadvantages. In the present communication, a hybrid fractal/DCT image compression technique is presented. DCT is employed whenever the quality of the decoding block is of... more
DCT and fractal compression incorporate certain advantages and disadvantages. In the present communication, a hybrid fractal/DCT image compression technique is presented. DCT is employed whenever the quality of the decoding block is of sufficient quality or else fractal compression technique is used for the encoding that will also lead to great compression. Pruned DCT is used to lower even more the computational time. The result is faster coding than fractals, better compression ratios than the DCT and better ...
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Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new... more
Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed toward surface electromyography (sEMG)-based gesture recognition, often addressed as an image classification problem using convolutional neural networks (CNNs). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals, which allows the application of typical image processing pipelines such as CNNs on sequence data. The proposed method is evaluated on different state-of-the-art network architectures and yields a significant classification improvement over the approach without the Hilbert curve. Additionally, we develop a new network architecture (MSHilbNet) that takes advantage of multiple scales of an initial Hilbert curve representation and achieves equal performance with fe...
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The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at... more
The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal mag...
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The issue addressed in this work is the design and implementation of a robust watermarking method enhanced with an image registration scheme. The method is integrated into the framework of a Digital Image Library allowing advanced... more
The issue addressed in this work is the design and implementation of a robust watermarking method enhanced with an image registration scheme. The method is integrated into the framework of a Digital Image Library allowing advanced security features like copyright protection, copy control and transaction tracking. For the evaluation of the watermarking method widely accepted benchmarking tools are utilized. 1.
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Agarose gel electrophoresis is the easiest and commonest way of separating and analyzing DNA and RNA molecules (although proteins can also be separated on agarose gels). The separation is achieved by utilizing the mobilities with which... more
Agarose gel electrophoresis is the easiest and commonest way of separating and analyzing DNA and RNA molecules (although proteins can also be separated on agarose gels). The separation is achieved by utilizing the mobilities with which different-sized DNA or RNA can ...
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The estimation of location based on the time varying Electric Network Frequency (ENF) is a new emerging technology in Information Forensics. This requires the extraction of the ENF signal from multimedia recordings and a comparison with... more
The estimation of location based on the time varying Electric Network Frequency (ENF) is a new emerging technology in Information Forensics. This requires the extraction of the ENF signal from multimedia recordings and a comparison with already known power grid signatures. In this paper, we focus on ENF signal extraction and statistical modelling of ENF signals. We introduce a novel technique based on instantaneous frequency estimation using the Hilbert transform, which shows promising results.
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Electrical Network Frequency (ENF), which refers to the instantaneous frequency of a power distribution network, is adopted for a wide range of applications. Taking into consideration both the lack of reference databases for different... more
Electrical Network Frequency (ENF), which refers to the instantaneous frequency of a power distribution network, is adopted for a wide range of applications. Taking into consideration both the lack of reference databases for different power grids all around the world and also the lack of Websites for hosting ENF-related data, we decided to create a website featuring some of the latest web development technologies in order to visually represent ENF-related data obtained from our reference database which is being updated by a Raspberry Pi ENF monitoring system.
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Electrical Network Frequency (ENF), the instantaneous fluctuation of the power grid's frequency around its nominal frequency due to the imbalance between energy production and consumption, was initially used in forensics applications and... more
Electrical Network Frequency (ENF), the instantaneous fluctuation of the power grid's frequency around its nominal frequency due to the imbalance between energy production and consumption, was initially used in forensics applications and since then it has been adopted into a wide range of applications. In this report, the development of a real-time ENF monitoring system is described. The whole system is based on a Raspberry Pi Model B computer, which performs the filtering of the digital signal, the calculation of the ENF values and finally the storage of the frequency values in real-time. Signal capturing is achieved by means of a sensing circuit that we have also designed and constructed. The performance of the system has been compared to existing solutions, thus validating its measuring accuracy.