A novel Modified Chaotic Simplified Advanced Encryption System (MCS-AES): mixed reality for a secure surgical tele-presence

Mixed Reality (MR) surgery has not been effectively implemented in telemedicine due to strict requirements of security and delay minimization during real-time video transmission. Hence, this paper aims to propose a novel solution for Surgical Telepresence with highly secured and faster real-time video transmission. The proposed system consists of three components: Authentication (Pre-surgery), Data transmission (During-Surgery), and Storage (Post-Surgery). For Authentication, Pass-Matrix technique is used at both ends to provide graphical passwords. During the surgery, a hybrid system is used to provide highly secured and faster real-time video transmission. This system includes a Feistel Encryption System (FES), Modified Scaled Zhongtang Chaotic System (M-SCZS), and Modified Advanced Encryption System (M-AES) algorithm. After Surgery, the transmitted data are stored using the Information Accountability Framework (IAF) for future purposes. The results are obtained from the during-surgery stage for jaw, breast, and bowel surgery. Both solutions are simulated in MATLAB on a personal computer with average processing capability. The proposed solution improves the entropy from 7.733~7.782 to 7.798–7.996 and reduces the processing time from 8.642~9.911 s/frames to 5.071~6.563 s/frames. The proposed focus on reducing the total processing time for the encryption and decryption process with improving security during the surgery process. Finally, this solution provides a fast security system for surgical telepresence that helps both local and remote surgeons for secure real-time communication. The complexity for this work need to know the used chaotic method, the values of the chaotic parameters and for which this method was used, in addition to the complexity of state of the art.

     

A novel augmented reality visualization in jaw surgery: enhanced ICP based modified rotation invariant and modified correntropy

Image registration, accuracy, processing time and occlusions are the main limitations of augmented reality (AR) based jaw surgery. Therefore, the main aim of this paper is to reduce the registration error, which will help in improving the accuracy and reducing the processing time. Also, it aims to remove outliers and remove the registration outcomes trapped in local minima to improve the alignment problems and remove the occlusion caused by surgery instrument. The enhanced Iterative Closest Point (ICP) algorithm with rotation invariant and correntropy was used for the proposed system. Markerless image registration technique was used for AR-based jaw surgery. The problem of occlusion caused by surgical tools and blood is solved by using stereo based tracing with occlusion handling techniques. This research reduced alignment error 0.59 mm?~?0.62 mm against 0.69?~?0.72 mm of state-of-the-art solution. The processing time of video frames was enhanced to 11.9?~?12.8 fps against 8?~?9.15 fps in state-of-the-art solution. This paper is focused on providing fast and accurate AR-based system for jaw surgery. The proposed system helps in improving the AR visualization during jaw surgery. The combination of methods and technology helped in improving AR visualization for jaw surgery and to overcome the failure caused by a large rotation angle and provides an initial parameter for better image registration. It also enhances performance by removing outliers and noises. The pose refinement stage provides a better result in terms of processing time and accuracy.

     

A novel solution of using deep learning for prostate cancer segmentation: enhanced batch normalization

Multimedia Tools and Applications - Deep learning has not been successfully implemented in the past with accurate segmentation of prostate on Magnetic Resonance (MR) image in nerve sparing prostate...     

A secure multi-hop relay node selection scheme based data transmission in wireless ad-hoc network via block chain

In today’s scenario, data transmission is established through the single or multi-hop relay nodes in Wireless Ad-Hoc Networks (WANET). Traditional relay node selection techniques undergo collusion attacks, increased energy consumption, delay, and reduced network lifetime. To cope with these problems, we propose a Quantum Atom Search Optimization coupled with Blockchain aided Data Transmission (QASO-BDT) scheme for a relay node selection with security aided data transmission. This approach comprises three phases such as registration, clustering, and transmission. Initially, in the node registration phase every sensor node gets registered in the blockchain network through Capillary Gateway (CG). Next, in the clustering phase, a CH is selected and an enhanced multi-view clustering model is used to cluster the nodes into several clusters. Finally, the multi-hop transmission phase assists in best relay node selection for multi-hop transmission using QASO, and the blockchain-based transaction is carried out to ensure security in the system. The proposed scheme is simulated in the MATLAB platform and achieves a result of 91.5% throughput, the reduced energy value of 40%, end to end delay of 20.6%, and the exhaustion of node is 1% which results in an increased lifetime of the nodes. Also, security is evaluated in comparison with other traditional methods.

     

A novel enhanced region proposal network and modified loss function: threat object detection in secure screening using deep learning

The Journal of Supercomputing - Detection of threat objects concealed in passenger clothing and baggage poses a challenge to aviation security. At present, the detection technology is capable of...     

Augmented reality for visualization the narrow areas in jaw surgery: modified Correntropy based enhanced ICP algorithm

Multimedia Tools and Applications - Over time, Augmented Reality (AR) based technology becomes not being properly to implement with oral and maxillofacial surgery to visualise the narrow area spot...     

Improved authentication and computation of medical data transmission in the secure IoT using hyperelliptic curve cryptography

The Journal of Supercomputing - Data transmission is a great challenge in any network environment. However, medical data collected from IoT devices need to be transmitted at high speed to ensure...     

A hybrid encryption/hiding method for secure transmission of biometric data in multimodal authentication system

Biometric security is a fast growing area that gains an increasing interest in the last decades. Digital encryption and hiding techniques provide an efficient solution to protect biometric data from accidental or intentional attacks. In this paper, a highly secure encryption/hiding scheme is proposed to ensure secure transmission of biometric data in multimodal biometric identification/authentication system. The secret fingerprint and iris vectors are sparsely approximated using accelerated iterative hard thresholding technique and then embedded in the host Slantlet-SVD domain of face image. Experiments demonstrate the efficiency of our technique for both encryption and hiding purpose, where the secret biometric information is well encrypted and still extractable with high fidelity even though the carrier image is seriously corrupted. Our experimental results show the efficiency of the proposed technique in term of robustness to attacks, Invisibility, and security.

     

Efficient and secure data hiding in encrypted images: A new approach using chaos

ABSTRACT

The recent trends in technology have made it possible to reproduce and share digital media more easily and more rapidly. This leads to the problem of exploiting the media illegitimately. To alleviate this problem, many cryptographic techniques are used to secure the data by encrypting them. However, the cloaked form of encrypted data attracts the intruder and shows the way to malicious attacks. Due to this, in recent times steganography has drawn more attention to secure the data. This article presents a new technique that embeds data in intermediate significant bit (ISB) and least significant bit (LSB) planes of the cover image. The method employs chaotic maps to generate random locations to hide the data bits as well as permutation order to encrypt the cover image. The cover image is first encrypted by applying permutation order, then embedding is carried out on the random locations generated. After embedding, the decrypted cover image is transmitted. This method provides two-level security in extracting the hidden data. Experimental outcomes (PSNR, MSE, NAE, and NCC) confirm that the method is proficient. The randomness of the values generated by chaotic maps is assessed by the NIST standard test suite.     

A novel augmented reality for hidden organs visualisation in surgery: enhanced super-pixel with sub sampling and variance adaptive algorithm

In recent years, Augmented Reality (AR) has gained more attention as an effective tool in medical surgeries. The potentials of using AR in the medical field can change conventional medical procedures. However, the technology still facing fundamental challenges, especially hidden organs, for example, the organs behind the bowel and liver. The surgeries in these areas lack accuracy in the visualization of the soft tissues behind the bowel and liver like the uterus and gall bladder. This research aims to improve the accuracy of visualisation and the processing time of the augmented video. The proposed system consists of an enhanced super-pixel algorithm with variance weight adaptation and subsampling method. The simulation studies show significant improvements in visualization accuracy and a reduction in processing time. The results show reduced visualisation error by 0.23 mm. It provides better accuracy of the video in terms of visualization error from 1.58?~?1.83 mm to 1.35?~?1.60 mm, and the processing time decreases from 50?~?58 ms/frames to 40?~?48 ms/frames. The proposed system \ focused on the pixel refinement for the 3d reconstruction of the soft tissue, which helps solve the issue of visualising the bowel and liver in an augmented video.

     

Earth science learning in SMALLab: A design experiment for mixed reality

Conversational technologies such as email, chat rooms, and blogs have made the transition from novel communication technologies to powerful tools for learning. Currently virtual worlds are undergoing the same transition. We argue that the next wave of innovation is at the level of the computer interface, and that mixed-reality environments offer important advantages over prior technologies. Thus, mixed reality is positioned to have a broad impact on the future of K-12 collaborative learning. We propose three design imperatives that arise from our ongoing work in this area grounded in research from the learning sciences and human-computer interaction. By way of example, we present one such platform, the Situated Multimedia Arts Learning Lab [SMALLab]. SMALLab is a mixed-reality environment that affords face-to-face interaction by colocated participants within a mediated space. We present a recent design experiment that involved the development of a new SMALLab learning scenario and a collaborative student participation framework for a 3-day intervention for 72 high school earth science students. We analyzed student and teacher exchanges from classroom sessions both during the intervention and during regular classroom instruction and found significant increases in the number of student-driven exchanges within SMALLab. We also found that students made significant achievement gains. We conclude that mixed reality can have a positive impact on collaborative learning and that it is poised for broad dissemination into mainstream K-12 contexts.     

A novel solution of enhanced loss function using deep learning in sleep stage classification: predict and diagnose patients with sleep disorders

Multimedia Tools and Applications - Sleep stage classification is important to accurately predict and diagnose patients with sleep disorders. Though various deep learning approaches have been...     

A novel solution of using deep learning for early prediction cardiac arrest in Sepsis patient: enhanced bidirectional long short-term memory (LSTM)

Multimedia Tools and Applications - Cardiac arrest is a common issue in Intensive Care Units (ICU) with low survival rate. Deep learning algorithms have been used to predict cardiac arrest...     

A mixed reality 3D system for the integration of X3DoM graphics with real-time IoT data

Multimedia Tools and Applications - A mixed reality system is a set of interlinked real-time sensors’ and actuators’ data presented within a virtual world with graphics and texts. This...     

A virtual reality system using the concentric mosaic: construction, rendering, and data compression

This paper proposes a new image-based rendering (IBR) technique called "concentric mosaic" for virtual reality applications. IBR using the plenoptic function is an efficient technique for rendering new views of a scene from a collection of sample images previously captured. It provides much better image quality and lower computational requirement for rendering than conventional three-dimensional (3-D) model-building approaches. The concentric mosaic is a 3-D plenoptic function with viewpoints constrained on a plane. Compared with other more sophisticated four-dimensional plenoptic functions such as the light field and the lumigraph, the file size of a concentric mosaic is much smaller. In contrast to a panorama, the concentric mosaic allows users to move freely in a circular region and observe significant parallax and lighting changes without recovering the geometric and photometric scene models. The rendering of concentric mosaics is very efficient, and involves the reordering and interpolating of previously captured slit images in the concentric mosaic. It typically consists of hundreds of high-resolution images which consume a significant amount of storage and bandwidth for transmission. An MPEG-like compression algorithm is therefore proposed in this paper taking into account the access patterns and redundancy of the mosaic images. The compression algorithms of two equivalent representations of the concentric mosaic, namely the multiperspective panoramas and the normal setup sequence, are investigated. A multiresolution representation of concentric mosaics using a nonlinear filter bank is also proposed.     

A novel solution of deep learning for endoscopic ultrasound image segmentation: enhanced computer aided diagnosis of gastrointestinal stromal tumor

Multimedia Tools and Applications - Gastrointestinal stromal tumor is one of the critical tumors that doctors do not suggest to get frequent endoscopy, so there is a need for a diagnosis system...     

A Gibbs sampler for mixed logit analysis of differentiated product markets using aggregate data

In this paper, we offer the Gibbs sampler as an alternative to the GMM estimator developed by Berry, Levinsohn, and Pakes (Econometrica 63(4), 841–890, 1995) in their equilibrium differentiated product market analysis of the automobile industry. We use the GMM objective as the basis for forming a posterior distribution, thereby making use of certain attributes of the GMM approach that reduce the computational cost of conducting posterior inference. The advantages provided by the our Bayesian GMM approach are that it enables us to conduct inference under the exact posterior distribution for the parameters, to estimate moments of functions of interest that are not readily available using GMM, and to capture non-normalities in the parameter distributions. The cost of posterior inference takes the form of additional distributional assumptions and longer computational time. In an illustration within, we find the random coefficients to be only weakly identified by the data. This results in highly non-normal distributions. The GMM estimates hint at this problem, but it can only be fully characterized by the Gibbs sampler.      

HoloFlows: modelling of processes for the Internet of Things in mixed reality

Software and Systems Modeling - Our everyday lives are increasingly pervaded by digital assistants and smart devices forming the Internet of Things (IoT). While user interfaces to directly monitor...     

A secret sharing-based scheme for secure and energy efficient data transfer in sensor-based IoT

The sensed data from Internet of Things (IoT) devices are important for accurate decision making. Thus, the data integrity, non-repudiation, data confidentiality, data freshness, etc., are necessary requirements in sensor-based IoT networks. Further, the IoT devices are resource constrained in terms of computation and communication capabilities. Hence, striking a balance between network lifetime and data security is of utmost importance. The present work explores the sensor-based IoT-specific security threats like, data modification, selective forwarding and replay attacks. Further, a scheme is proposed based on secret sharing and cryptographic hash functions which detects these attacks by a malicious entity and protects the data from passive listeners too. Extensive simulations were performed to evaluate the efficacy of the scheme, and results show that the proposed scheme outperforms previously explored schemes like SIGN-share, SHAM-share, and PIP algorithm, in terms of sensor processing time, energy consumption during in-node processing and aggregation time. Network lifetime has been further analyzed to show the efficacy of the scheme.