SoC security has become essential with devices now pervasive in critical infrastructure in homes and businesses. Today's embedded SoCs are becoming increasingly high-performance and complex, comprising multiple cores, accelerators, and IP blocks interconnected with a Network-on-Chip (NoC). As these IPs can originate from diverse sources, they cannot be trusted to form the root of trust in SoCs. However, the NoC itself, being the communication backbone linking all IPs, is naturally positioned to be the basis for a secure SoC. Therefore, there is a need for an efficient solution that both meets the stringent requirements of modern embedded SoC designs, while maintaining a high level of security.

Error injection has become critically important for testing the reliability of newly designed hardware systems. Evaluating how a design under test (DUT) reacts to different error-injection methodologies is essential for verification engineers to design dependable universal verification methodology (UVM) scoreboards for error-detection purposes. The first main contribution of this paper is to decide on the feasibility and compatibility of some error-injection techniques when used with networks-on-chip (NoC) platforms for simulation and hardware emulation environments. We target a UVM-based error-injection and detection environment with reusable components. Proposed techniques, introducing both positive and negative test scenarios, are applied to two examples of NoC components: 1) a base router and 2) Daniel router. Base router is a simple case study to prove proposed schemes, whereas Daniel router is a complex reconfigurable open-source case study. Daniel router provides the ability to change router architecture with some parameters and applied algorithms. The second main contribution of this paper is to integrate a full UVM environment with various verification approaches. Target approaches include error injection and detection using reusable and generic UVM environment and components for NoC. Network response is inspected according to error type and methodology. Finally, the proposed UVM environment is used to test and verify an N × N 2-D network composed of base routers or Daniel routers.

Monitoring illegal explosions is mandatory for the safety of human life, environment, and protect the important buildings such as High-dam in Egypt. This kind of monitoring can be accomplished by detecting and identifying the explosions. If an illegal explosion happens such as quarry blast, an alarm should be reported to the government to take immediate action. However, the main problem is that many measured signals from received explosions are similar to earthquakes in their shape and both cannot differentiate from each other. Also, incorrect classification possibly will distort the real seismicity nature of the region. This problem motivates us to search for unique discriminating features to distinguish between earthquakes and explosions with precise accuracy. Therefore, in this paper, we propose to extract the discriminative features based on Autoencoder from the first few seconds after the P-wave arrival time of the event. The discriminative features are found to be in the first 60 samples after the arrival time of P-wave. Thus the first stage of the proposed algorithm is extracting the discriminative features via the Autoencoder. Then, softmax classifies the event based on these extracted features. The proposed algorithm achieves a classification accuracy of 98.55% when applied to 900 earthquakes and quarry blasts waveforms recorded by Egyptian National Seismic Network (ENSN).

With the increase in the number of cores embedded on a chip; The main challenge for Multiprocessor System-on-Chip (MPSoC) platforms is the interconnection between that massive number of cores. Networks-on-Chip (NoC) was introduced to solve that challenge, by providing a scalable and modular solution for communication between the cores. In this paper, we introduce a configurable MPSoC framework called RVNoC that generates synthesizable RTL that can be used in both ASIC and FPGA implementations. The proposed framework is based on the open source RISC-V Instruction Set Architecture (ISA) and an open source configurable flit-based router for interconnection between cores, with a core network interface of our design to connect each core with its designated router. A benchmarking environment is developed to evaluate variant parameters of the generated MPSoC. Synthesis of a single building block containing a single core without any peripherals, a router, and a core network interface, using 45nm technology, shows an area of 102.34 kilo Gate Equivalents (kGE), a maximum frequency of 250 MHz, and a 9.9 μW/MHz power consumption.

The accurate detection of P-wave arrival time is imperative for determining the hypocenter location of an earthquake. However, precise detection of onset time becomes more difficult when the signal-to-noise ratio (SNR) of the seismic data is low, such as during microearthquakes. In this letter, a stacked denoising autoencoder (SDAE) is proposed to smooth the background noise. The SDAE acts as a denoising filter for the seismic data. In the proposed algorithm, the SDAE is utilized to reduce background noise such that the onset time becomes more clear and sharp. Afterward, a hard decision with one threshold is used to detect the onset time of the event. The proposed algorithm is evaluated on both synthetic and field seismic data. As a result, the proposed algorithm outperforms the short-time average/long-time average and the Akaike information criterion algorithms. The proposed algorithm accurately picks the onset time of 94.1% for 407 field seismic waveforms with a standard deviation error of 0.10 s. In addition, the results indicate that the proposed algorithm can pick arrival times accurately for weak SNR seismic data with SNR higher than -14 dB.

Flexibility either in buffering or routing is a very promising solution for NoC congestion problem. In this paper, a new 3D router with flexible architecture is introduced and validated for 3D NoCs. In fact, the Flexible router architecture introduced in literature would be deadlock prone if it is incorporated in a 3D NoC domain. We successfully design the new buffering constraints and extensively evaluate its performance under various traffic scenarios using real benchmark applications. We show the great enhancement of the new candidate router architecture in comparison to the conventional 3D NoC router architecture. Finally, we show that this enhancement in performance comes at a very low impact in power and area; especially for large NoC sizes.

High quality videos became an essential requirement in recent applications. High efficiency video coding (HEVC) standard provides an efficient solution for high quality videos at lower bit rates. On the other hand, HEVC comes with much higher computational cost. In particular, motion estimation (ME) in HEVC, consumes the largest amount of computations. Therefore, fast ME algorithms and hardware accelerators are proposed in order to speed‐up integer ME in HEVC. This study presents a fast centre search algorithm (FCSA) and an adaptive search window algorithm (ASWA) for integer pixel ME in HEVC. In addition, centre adaptive search algorithm, a combination of the two proposed algorithms FCSA and ASWA, is proposed in order to achieve the best performance. Experimental results show notable speed‐up in terms of encoding time and bit rate saving with tolerable peak signal‐to‐noise ratio (PSNR) quality degradation. The proposed fast search algorithms reduce the computational complexity of the HEVC encoder by 57%. This improvement is accompanied with a modest average PSNR loss of 0.014 dB and an increase by 0.6385% in terms of bit rate when compared with related works.

HEVC has adopted Sample Adaptive Offset (SAO) as a new in-loop filtering block. SAO can significantly improve coding efficiency, however, it requires exhaustive operations in order to calculate best SAO parameters for each Coding Tree Unit (CTU). SAO parameters estimation process takes around 93 % of the overall SAO execution time which depends on number of candidate SAO modes. Real time and low power video encoders are in demand for more efficient SAO encoding algorithms. In this paper, we propose an algorithm that reduces the SAO parameter estimation complexity by adaptively reusing the dominate mode of corresponding set of CTUs. The proposed algorithm can save up to 75% of SAO parameter estimation time with only 0.8% BD-rate penalty.

Osteoporosis is a bone disease characterised by a reduction in bone mass, resulting in an increased risk of fractures. Doctors need the bone mineral density (BMD) measurements of vertebral bodies in order to diagnose and treat osteoporosis. The authors' objective is to segment the VBs as accurately as possible and hence to increase the accuracy of the BMD measurements and fracture analysis. Three pieces of information (intensity, spatial interaction and shape) are modelled to optimise a probabilistic energy functional. A universal shape prior, which is modelled using the cervical, thoracic and lumbar spinal regions, is proposed. Volumetric computed tomography data sets with various challenges are used in this study. The authors classify data sets based on some features related to the anatomy, imaging modality and level of the bone health. The proposed framework is one of only a few reported in the literature tested on the data obtained from different imaging devices. The experimental results reveal that the proposed method is robust under various noise levels, less variant to the initialisation and faster than existing vertebrae segmentation reports in the literature.

Faces in the wild have recently captured the focus of researchers for all facial analysis problems. Partial occlusion is a major problem for analyzing faces captured in unconstrained non-cooperative conditions. Even detecting the faces in such conditions is a challenging problem that needs to be solved before any further analysis of such faces can be done. In this paper, we propose modelling the face as a collection of parts that can be selected from the visible regular facial features and some other objects that can possibly occlude faces such as sunglasses, caps and hands. The proposed algorithm is more toward scene understanding in the sense that it is not only detecting faces but it also suggests the visible parts of these faces and even some of the occluding objects which can help in any further analysis. Experimental results show a state of the art performance on the challenging FDDB database with a thorough analysis of the performance with different types of partial occlusion.

Network bandwidth is a performance concern especially for collective communication because the bisection bandwidth of recent supercomputers is far less than their full bisection bandwidth. In this context we propose to exploit the use of a network coding technique to reduce the number of unicasts and the size of transferred data generated by latency-sensitive collective communication in supercomputers. Our proposed network coding scheme has a hierarchical multicasting structure with intra-group and inter-group unicasts. Quantitative analysis show that the aggregate path hop counts by our hierarchical network coding decrease as much as 94% when compared to conventional unicast-based multicasts. We validate these results by cycle-accurate network simulations. In 1,024-switch networks, the network reduces the execution time of collective communication as much as 64%. We also show that our hierarchical network coding is beneficial for any packet size.

In this study, a novel probabilistic, geometric and dynamic shape‐based level sets method is proposed. The shape prior is coupled with the intensity information to enhance the segmentation results. The two‐dimensional principal component analysis method is applied on the training shapes to represent the shape variation with enough number of shape projections in the training step. The shape model is constructed using the implicit representation of the projected shapes. A new energy functional is proposed (i) to embed the shape model into the image domain and (ii) to estimate the shape coefficients. The proposed method is validated on synthetic and clinical images with various challenges such as the noise, occlusion and missing information. The authors compare their method with some of related works. Experiments show that the proposed segmentation method is more accurate and robust than other alternatives under different challenges. ∗ Note: Colour figures are available in the online version of this paper.

Accurate 3D modeling of the human teeth/jaw helps patients avoid the discomfort of the mold process, and improves the data accuracy for oral orthodontists and dental care personnel. Since the surface of the human tooth is almost textureless, Shape from Shading (SFS) has been successfully adopted in solving this problem. In this paper, we attempt to improve the limitations of previous 3D tooth reconstruction algorithms by developing a new approach for shape reconstruction from single image shading with Two-Dimensional Principle Component Analysis (2D-PCA) shape priors. The surface reflectance is modeled using Oren-Nayar-Wolff model which accounts for the retro-reflection case and we experimentally prove that the teeth surface follows the microfacet theory. Our formulation exploits the shape priors as extracted from a set of training CT scans of real human jaws. Our experiments provide promising quantitative metric results for the proposed approach. This work is fundamental for establishing an optical-based approach for reconstructing the human jaw that is inexpensive and does not use ionizing radiation.

We propose a new shape-based segmentation approach using the statistical shape prior and level sets method. The segmentation depends on the image information and shape prior. Training shapes are grouped to form a probabilistic model. The shape model is embedded into the image domain taking in consideration the evolution of a contour represented by a level set function. The evolution of the front gathers information from the image intensities and shape prior. The segmentation approach is applied in segmenting the vertebral bodies in CT images. Our results shows that the technique is accurate and robust compared with the other alternative in the literature.