Abstract: In ultra-precision machining, vibration is the key factor which restricts the machining accuracy and surface quality of a workpiece. Using the traditional active negative-stiffness vibration control technology, the simultaneous suppression of the floor vibration interference and payload direct interference is difficult. The ability of the conventional vibration isolation system to inhibit the direct disturbance of a payload deteriorates during the suppression of the low-frequency interference, and the residual low-frequency vibration severely restricts further improvement of the machining accuracy. To solve this problem, this paper proposes a novel active vibration control method of absolute displacement feedback based on the blending of infinite and zero stiffness. The absolute displacements of the payload and floor are considered as the feedback signals, and through the series and parallel connections of positive and negative stiffness, the equivalent stiffness between an isolated payload and reference point and between the isolated payload and floor tends to infinity and zero, respectively. The infinite and zero-stiffness blending control is realized, and the direct interference of the payload and floor vibrations at low frequencies (

Abstract: The paper proposes an adaptive fuzzy position control for a 3-DOF hydraulic manipulator with large payload variation. The hydraulic manipulator uses electrohydraulic actuators as primary torque generators to enhance carrying payload of the manipulator. The proposed control combines backstepping sliding mode control, fuzzy logic system (FLS), and a nonlinear disturbance observer. The backstepping sliding mode control includes a sliding mode control for manipulator dynamics and a PI control for actuator dynamics. The fuzzy logic system is utilized to adjust the control gain and robust gain of the sliding mode control (SMC) based on the output of the nonlinear disturbance observer to compensate the payload. The Lyapunov approach and backstepping technique are used to prove the stability and robustness of the whole system. Some simulations are implemented, and the results are compared to other controllers to exhibit the effectiveness of the proposed control.

Abstract: Steganography is the science and art of secret communication between two sides that attempt to hide the content of the message. It is the science of embedding information into the cover image without causing a loss in the cover image after embedding.Steganography is the art and technology of writing hidden messages in such a manner that no person, apart from the sender and supposed recipient, suspects the lifestyles of the message. It is gaining huge attention these days as it does now not attract attention to its information's existence. In this paper, a comparison of two different techniques is given. The first technique used Least Significant Bit (LSB) with no encryption and no compression. In the second technique, the secret message is encrypted first then LSB technique is applied. Moreover, Discrete Cosine Transform (DCT) is used to transform the image into the frequency domain. The LSB algorithm is implemented in spatial domain in which the payload bits are inserted into the least significant bits of cover image to develop the stego-image while DCT algorithm is implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are inserted into the frequency components of the cover image.The performance of these two techniques is evaluated on the basis of the parameters MSE and PSNR.

Abstract: P4 introduces a standardized, universal way for data plane programming. Secure and resilient communication typically involves the processing of payload data and specialized cryptographic hash functions. We observe that current P4 targets lack the support for both. Therefore, applications and protocols, which require message authentication codes or hashing structures that are resilient against attacks such as denial-of-service, cannot be implemented. To enable authentication and resilience, we make the case for extending P4 targets with cryptographic hash functions. We propose an extension of the P4 Portable Switch Architecture for cryptographic hashes and discuss our prototype implementations for three different P4 target platforms: CPU, NPU, and FPGA. To assess the practical applicability, we conduct a performance evaluation and analyze the resource consumption. Our prototype implementations show that cryptographic hashing can be integrated efficiently. We cannot identify a single hash function delivering satisfying performance on all investigated platforms. Therefore, we recommend a set of hash functions to optimize target-specific performance.

Abstract: Recently, with the advent of various Internet of Things (IoT) applications, a massive amount of network traffic is being generated. A network operator must provide different quality of service, according to the service provided by each application. Toward this end, many studies have investigated how to classify various types of application network traffic accurately. Especially, since many applications use temporary or dynamic IP or Port numbers in the IoT environment, only payload-based network traffic classification technology is more suitable than the classification using the packet header information as well as payload. Furthermore, to automatically respond to various applications, it is necessary to classify traffic using deep learning without the network operator intervention. In this study, we propose a traffic classification scheme using a deep learning model in software defined networks. We generate flow-based payload datasets through our own network traffic pre-processing, and train two deep learning models: 1) the multi-layer long short-term memory (LSTM) model and 2) the combination of convolutional neural network and single-layer LSTM models, to perform network traffic classification. We also execute a model tuning procedure to find the optimal hyper-parameters of the two deep learning models. Lastly, we analyze the network traffic classification performance on the basis of the F1-score for the two deep learning models, and show the superiority of the multi-layer LSTM model for network packet classification.

Abstract: Consider a (multiple-access) wireless communication system where users are connected to a unique base station over a shared-spectrum radio links. Each user has a fixed number k of bits to send to the base station, and his signal gets attenuated by a random channel gain (quasi-static fading). In this paper we consider the many-user asymptotics of Chen-Chen-Guo’2017, where the number of users grows linearly with the blocklength. In addition, we adopt a per-user probability of error criterion of Polyanskiy’2017 (as opposed to classical joint-error probability criterion). Under these two settings we derive bounds on the optimal required energy-per-bit for reliable multi-access communication. We confirm the curious behaviour (previously observed for non-fading MAC) of the possibility of perfect multi-user interference cancellation for user densities below a critical threshold. Further we demonstrate the suboptimality of standard solutions such as orthogonalization (i.e., TDMA/FDMA) and treating interference as noise (i.e. pseudo-random CDMA without multi-user detection).

Abstract: This paper proposes an efficient PID control of a highly nonlinear double-pendulum overhead crane without the need for a payload motion feedback signal. Optimal parameters of the PID controllers are tuned by using an improved particle swarm optimisation (PSO) algorithm based on vertical distance oscillations and potential energy of the crane. In contrast to a commonly used PSO algorithm based on a horizontal distance, the approach resulted in an efficient performance with a less complex controller. To test the effectiveness of the approach, extensive simulations are carried out under various crane operating conditions involving different payload masses and cable lengths. Simulation results show that the proposed controller is superior with a better trolley position response, and lower hook and payload oscillations as compared to the previously developed PSO-tuned PID controller. In addition, the controller provides a satisfactory performance without the need for a payload motion feedback signal.

Abstract: In this paper, we present the results of extensive experiments on a testbed in the The Things Network (TTN), a public LoRa network. We evaluate the transmission quality of LoRa links by measuring the Packet Reception Rate (PRR) as a function of the payload length. The results show that there is only a slight impact of the payload length on PRR, which means that the bit error rate does not strongly influence the probability of packet reception. Our measurements show that the LoRa channel behaves like a slow fading Rayleigh channel, which translates into probability Ps of being (or not) in a favorable condition for each frame reception: once the frame preamble is received, there is great chance that the whole frame is correctly received. Probability Ps depends on the Spreading Factor and the Signal to Noise Ratio, and often becomes a dominant factor of successful reception depending on the signal strength at a gateway.

Abstract: The payload mass and the cable length are always different/uncertain for various transportation tasks and external disturbances that accompany industrial overhead crane systems. In addition, existi...

Abstract: In the recent years, there has been an exponential growth in the number of malware captured and analyzed by the antivirus companies. However, much of these malware are variants of already known malware. Thus, it has become necessary to determine whether a malware belongs to a known family, or exhibits a new behavior hitherto unseen, and requires further analysis. Existing traditional approaches used by antivirus companies are based on signature-based detection and can be thwarted in case of zero-day exploit-based malware. Manual examination of such executables is extremely cumbersome due to the enormous number of such cases. Also, it has become necessary to speed up the detection process and predict before the executable releases its malicious payload. In this work, we addressed the above issues using automated yet efficient malware analysis. We classified the malicious executables into different malware classes in the earliest possible time. In this work, firstly we use static approach and achieve the highest classification accuracy of 97.95% using a Random Forest classifier. Secondly, we use Dynamic approach as it provides useful insights in the case of obfuscated or packed malware where static analysis is not as effective. We achieve the highest classification accuracy of 99.13% using Random Forest classifier. Lastly, we use a combination of both the approaches to overcome the limitations of static and as well as dynamic approaches, i.e., the Hybrid approach. Our experiments achieve the highest classification accuracy of 99.74% for classifying malware into types in the initial 4 seconds of its execution using Random Forest. Our solution is robust and scalable as we have also tested our model on packed and obfuscated malware samples. The model achieves an accuracy of 96.73% and 96.31% on packed and obfuscated malware samples, respectively.

Abstract: A hybrid robust lossless data hiding algorithm is proposed in this paper by using the Singular Value Decomposition (SVD) with Fast Walsh Transform (FWT) and Slantlet Transform (SLT) for image authentication. These transforms possess good energy compaction with distinct filtering, which leads to higher embedding capacity from 1.8 bit per pixel (bpp) up to 7.5bpp. In the proposed algorithm, Artificial Neural Network (ANN) is applied for region of interest (ROI) detection and two different watermarks are created. Embedding is done after applying FWH by changing the SVD coefficients and by changing the highest coefficients of SLT subbands. In dual hybrid embedding first watermark is the ROI and another watermark consists of three parts, i.e., patients’ personal details, unique biometric ID and the key for encryption. Comparison of the proposed algorithm is done with the existing watermarking techniques for analyzing the performance. Experiments are simulated on the proposed algorithm by casting numerous attacks for testing the visibility, robustness, security, authenticity, integrity and reversibility. The resultant outcome proves that the watermarked image has an improved imperceptibility with a high level of payload, low time complexity and high Peak Signal to Noise Ratio (PSNR) against the existing approaches.

Abstract: A reserving room before encryption (RRBE) framework is proposed to provide separable reversible data hiding for encrypted images in this paper. A combination of Interpolative AMBTC and Huffman coding is used to enhance the hiding capacity. In our scheme, a receiver with only the hiding key can extract the secret data without knowing about the content. If the receiver has only the encryption key, it is not possible to extract the hidden secret data, but it is possible to decrypt an image similar to the original image. Only when the receiver has both the hiding key and the encryption key, it is possible to extract the secret data and completely recover the original content without any error. The experimental results demonstrated the efficiency of our proposed high payload reversible data hiding scheme for encrypted images (HP-RDHEI), especially with respect to the data embedding rate.

Abstract: This paper introduces the concept of a pixogram which makes possible a fresh approach to high payload video steganography. The pixogram allows for a new perspective by investigating the temporal changes that take place at the individual pixel level across frames of a video segment. Simply put, a pixogram has the property of converting highly uncorrelated spatial areas of individual frames of a video scene into highly correlated temporal segments by making use of the temporal correlation between the frames of the same scene in a given video segment, thus maximizing the redundant area suitable for hiding in the transform domain. Experimental results demonstrate the effectiveness of this new approach for increased payload capacity while maintaining visual fidelity of the stego-video as compared to competing video steganography schemes.

Abstract: Interpolation based reversible data hiding (IRDH) schemes have recently been studied for better rate-distortion performance. However, most of them do not have any consideration of an ‘effective’ capacity management for increasing size of payload. In this paper, we develop and present an IRDH scheme with adaptive embedding, which determines how many bits of an interpolated pixel can be used for the best possible embedded image quality by using a parameter to control the embedding rate. While compared with the prominent IRDH schemes, our scheme demonstrated its efficiency for better embedding rate distortion performance. Being up-sampled, the embedded image would have higher spatial resolution. It also does not require any location map, and thus the total capacity can be effectively used for data embedding. Moreover, it keeps the original pixels untouched and thus, would be useful in military and medical image applications that restrict minimum possible changes in the cover images.

Abstract: This paper presents the control of a quadrotor with a cable-suspended payload. The proposed control structure is a hierarchical scheme consisting of an energy-based control (EBC) to stabilize the vehicle translational dynamics and to attenuate the payload oscillation, together with a nonlinear state feedback controller based on an linear matrix inequality (LMI) to control the quadrotor rotational dynamics. The payload swing control is based on an energy approach and the passivity properties of the system’s translational dynamics. The main advantage of the proposed EBC strategy is that it does not require excessive computations and complex partial differential equations (PDEs) for implementing the control algorithm. We present a new methodology for using an LMI to synthesize the controller gains for Lipschitz nonlinear systems with larger Lipschitz constants than other classical techniques based on LMIs. This theoretical approach is applied to the quadrotor rotational dynamics. Stability proofs based on the Lyapunov theory for the controller design are presented. The designed control scheme allows for the stabilization of the system in all its states for the three-dimensional case. Numerical simulations demonstrating the effectiveness of the controller are provided.

Abstract: Recent advancements in computer vision led to the development of a real-time surveillance system which ensures the safety and security of the people in public places. An aerial surveillance system will be advantageous in this scenario using a platform like Unmanned Aerial Vehicle (UAV) will be very reliable and can be considered as a cost-effective option for this task. To make the system fully autonomous, we require real-time abnormal event detection. But, this is computationally complex and time-consuming due to the heavy load on the UAV, which affords limited processing and payload capacity. In this paper, we propose a cost-effective approach for aerial surveillance in which we move the large computation tasks to the cloud while keeping limited computation on-board UAV device using edge computing technique. Further, our proposed system will maintain the minimum communication between UAV and cloud. Thus it not only reduces the network traffic but also reduces the end-to-end delay. The proposed method is based on the state-of-the-art YOLO (You Only Look Once) technique for real-time object detection deployed on edge computing device using Intel neural compute stick Movidius VPU (Vision Processing Unit), and we applied abnormal event detection using motion influence map on the cloud. Experimental results demonstrate that the proposed system reduces the end-to-end delay. Further, Tiny YOLO is six times faster while processing the frames per second (fps) when compared to other state-of-the-art methods.

Abstract: This paper proposes a fuzzy edge detection based steganography approach to effectively hide data within images. Instead of applying conventional edge detection algorithms, the method uses a fuzzy edge detection approach in order to estimate more number of pixels where the data can be hidden. At the outset, the cover image is masked and the fuzzy edge detection is performed on the masked image thus retaining edge information. The number of bits to be embedded in a particular pixel is dependent on whether the pixel is an edge pixel, where more bits are embedded. In case the pixel is not an edge pixel and also not a background pixel then the amount of data that is to be embedded depends on the Euclidean distance of the respective pixel from the nearest edge pixel and is determined by the Gaussian function. Experimental results ensure that the scheme offers variable payload with acceptable quality distortion in the stego-image.

Abstract: In this paper, the deficiency of Schur decomposition (SD) for image watermarking has been comprehensively investigated. A remedial scheme is developed not only to fix the existing problems but also to reinforce the performance in robustness and imperceptibility. This scheme starts with partitioning the host image into non-overlapping blocks of 4 × 4 pixels and then applying SD to each block individually. Level shifting serves as a controlling gauge for embedding strength. The use of intentional perturbation prevents nil orthonormal vectors derived from zero eigenvalues. The dominant vector is identified by analyzing the entire Schur matrix instead of just diagonal elements. To achieve effective watermark embedding and extraction, the orthonormality of the acquired unitary matrix ought to be preserved while the resulting distortion can be compensated via the systematic modification of the Schur matrix. Finally, a recursive regulation guarantees the retrieval of watermark bits. Experiment results indicate that the proposed scheme is free of errors in the absence of attacks and can withstand a variety of image processing attacks as well. Moreover, the inclusion of distortion compensation contributes an improvement of 2.47 dB in terms of peak signal-tonoise ratio. In comparison with previous SD-based schemes, the proposed one exhibits superior robustness and imperceptibility while operating at the same payload capacity.

Abstract: The awareness to secure medical data has significantly increased. Steganographic has binged an important topic especially in this area since it has the capability to avoid medical data breach. This paper proposes a new steganography scheme based on Bit Invert System (BIS) using three control random parameters. The random selection process is performed based on Henon Map Function (HMF). In order to increase the security level, affine cipher and Huffman method is used for encryption as well as to minimize the encrypt data prior to the embedding for high payload ability. This integration is effective due to two main reasons: first, checking, and mapping to determine 0- and 1-bits during embedding, and second, segmenting the secret data to track and map every bit in stego image. The results showed that the presented scheme can assure confidentiality and security of the medical data while maintaining the image quality.

Abstract: In this Letter, the authors proposed a novel scheme, with neither extra transmission energy nor extra bandwidth, to transmit additional bits along with low-density parity-check (LDPC) coded payload data. At the transmitter, additional bits are first transformed into a random-like sequence, which is then superimposed onto the LDPC coded data, resulting in the transmitted sequence. At the receiver, a statistical learning-based algorithm is employed to detect the additional bits. Then the interference of the additional bits is removed and the payload data is recovered by the conventional LDPC decoder. Simulation results show that, for a (3,6)-regular LDPC code of length 8064, ten additional bits can be transmitted reliably with negligible effect on the reliability of the payload data.

Abstract: Focusing on the received power prediction of millimeter-wave (mmWave) radio-frequency (RF) signals, we propose a multimodal split learning (SL) framework that integrates RF received signal powers and depth-images observed by physically separated entities. To improve its communication efficiency while preserving data privacy, we propose an SL neural network architecture that compresses the communication payload, i.e., images. Compared to a baseline solely utilizing RF signals, numerical results show that SL integrating only one pixel image with RF signals achieves higher prediction accuracy while maximizing both communication efficiency and privacy guarantees.

Abstract: High-performance remote sensing payload communication is a vital problem in air-borne and space-borne surveillance systems. Among different remote sensing imaging systems, video synthetic aperture radar (ViSAR) is a new technology with lots of principal and managerial data which should be compressed, aggregated, and communicated from a radar platform (or a network of radars) to a ground station through wireless links. In this paper, a new data aggregation technique is proposed towards efficient payload transmission in a network of aerial ViSAR vehicles. Our proposed method is a combination of a recent interpolation-based data hiding (IBDH) technique and visual data transformation process using discrete cosine transform (DCT) which is able to outperform the reference method in terms of data aggregation ability.

Abstract: Social networking and cloud computing are being extensively used, and in this era, the frequency of sending information or images to each other is increasing. The prevention of private information leakage during communication over the Internet has become a concern in the past decades. Several data protection methods, such as cryptographic, watermarking, and steganography techniques, have been proposed to protect private data. In this paper, an embedding method is proposed based on an absolute moment block truncation coding (AMBTC)-compressed image. High and low mean tables are extracted from a compressed image and are divided into non-overlapping blocks. An adaptive variable N-bit bit plane truncation image embedding method is proposed to embed the secret data in each block. In this method, at the receiver end, the secret data are extracted, and the original AMBTC image could be recovered by recalling the stored peak and zero points. In addition, a chaotic encryption scheme is integrated into the proposed system to improve robustness against security vulnerability. The results show that the proposed method has superior performance and higher payload compared with the reference methods.

Abstract: Localization in long-range Internet of Things networks is a challenging task, mainly due to the long distances and low bandwidth used. Moreover, the cost, power, and size limitations restrict the integration of a GPS receiver in each device. In this article, we introduce a novel received signal strength indicator (RSSI)-based localization solution for ultra narrow band (UNB) long-range IoT networks such as Sigfox. The essence of our approach is to leverage the existence of a few GPS-enabled sensors nodes ( $GSN\text{s}$ ) in the network to split the wide coverage into classes, enabling RSSI-based fingerprinting of other sensors nodes ( $SN\text{s}$ ). By using machine learning algorithms at the network backed-end, the proposed approach does not impose extra power, payload, or hardware requirements. To comprehensively validate the performance of the proposed method, a measurement-based dataset that has been collected in the city of Antwerp is used. We show that a location classification accuracy of 80% is achieved by virtually splitting a city with a radius of 2.5 km into seven classes. Moreover, separating classes, by increasing the spacing between them, brings the classification accuracy up-to 92% based on our measurements. Furthermore, when the density of $GSN$ nodes is high enough to enable device-to-device communication, using multilateration, we improve the probability of localizing $SN\text{s}$ with an error lower than 20 m by 40% in our measurement scenario.

Abstract: This paper investigates the spectrum sharing problem in vehicular networks, where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We model the resource sharing as a multi-agent reinforcement learning (RL) problem, which is then solved using a fingerprint-based deep Q-network method. The V2V links, each acting as an agent, collectively interact with the vehicular environment, receive distinctive observations yet a common reward, and then improve policy design through updating their Q-networks with gained experiences. Simulation results demonstrate desirable performance of the proposed resource allocation scheme based on multi-agent RL in terms of both V2I capacity and V2V payload delivery probability.