Subsequent Blocks Research Articles

Multivariate SPC via sequential multiblock-PLS

The sequential multi-block partial least squares (SMB-PLS) is proposed for implementing a multivariate statistical process control scheme. This is of interest when the system is composed of several blocks following a sequential order and presenting correlated information, for instance, a raw material properties block followed by a process variables block that is manipulated according to raw material properties. The SMB-PLS uses orthogonalization to separate correlated information between blocks from orthogonal variations. This allows monitoring the system in different stages considering only the remaining orthogonal part in each block. Thus, the SMB-PLS increases the interpretability and process understanding in the model building (Phase I), since it provides a deep insight about the nature of the system variations. Besides, it prevents any special cause from propagating to subsequent blocks enabling their use in the model exploitation (Phase II). The methodology is applied to a real case study from a food manufacturing process.

Accelerating large-scale DEA computation using sequential categorization and dynamic reference set selection

Data envelopment analysis (DEA) is a well-known data-enabled analytic tool for evaluating relative efficiency of units with multiple inputs and multiple outputs. The DEA computation increases substantially in the presence of large samples. In this study, we first recall two lemmas to distinguish efficient units using arithmetic operations without solving linear programming (LP). Using the used lemmas, the total sample of units is partitioned into several sequential blocks, where units in the preceding blocks are relatively efficient to those in the subsequent blocks. A novel reference set selection procedure is then formulated. We implement the proposed approach into one of the fastest existing methods and demonstrate a significant improvement in elapsed time. We conduct simulation experiments and illustrate the outcomes across varying dimensions, cardinality, and density.

Perceptual Adaptation to Noise-Vocoded Speech by Lip-Read Information: No Difference between Dyslexic and Typical Readers.

Auditory speech can be difficult to understand but seeing the articulatory movements of a speaker can drastically improve spoken-word recognition and, on the longer-term, it helps listeners to adapt to acoustically distorted speech. Given that individuals with developmental dyslexia (DD) have sometimes been reported to rely less on lip-read speech than typical readers, we examined lip-read-driven adaptation to distorted speech in a group of adults with DD ( N = 29) and a comparison group of typical readers ( N = 29). Participants were presented with acoustically distorted Dutch words (six-channel noise-vocoded speech, NVS) in audiovisual training blocks (where the speaker could be seen) interspersed with audio-only test blocks. Results showed that words were more accurately recognized if the speaker could be seen (a lip-read advantage), and that performance steadily improved across subsequent auditory-only test blocks (adaptation). There were no group differences, suggesting that perceptual adaptation to disrupted spoken words is comparable for dyslexic and typical readers. These data open up a research avenue to investigate the degree to which lip-read-driven speech adaptation generalizes across different types of auditory degradation, and across dyslexic readers with decoding versus comprehension difficulties.

Blockchain Technology: Transforming IoT and Finance with Privacy and Security Considerations

A Block chain, is a growing list of information, called blocks, which are linked using cryptography. A Block chain is a decentralized, distributed and a public digital ledger that is used to record transactions across many devices so that any involved record cannot be changed easily, without modifying all subsequent blocks, because of this, Block chain has found its use in wide-ranging fields. This system, therefore, describes the role of Block chain in IoT and financial applications, two fields which will be benefitted the most by it. Furthermore, this paper examines the privacy and security concerns related to it and shares some insights on how these problems can be tackled. In this paper, we have reviewed multiple such papers in which similar issues were addressed. Lastly, the public perception of block chain technology is taken to addresses how block chain is perceived in different segments of society Keywords—framework; block chain; Scalable paymentVerification; Cryptographic hash function; Serial Communication; Transaction data.

Revolutionizing domestic solar power systems with IoT-enabled Blockchain technology

Solar power systems in homes have become the need of the hour due to the crisis of fossil fuels. Also, it is a useful way of rural electrification and cutting down on running electricity costs. This paper discusses a 26-kW solar power system for powering homes along with IoT-based monitoring. The proposed system is expected to be low in cost and highly efficient. The system can also be used as a battery backup without solar power. The emergence of Blockchain technology is poised to revolutionize the sharing of information by providing a means of building trust in decentralized settings without the reliance on intermediaries. This technological breakthrough has the potential to transform several industries, including the internet of things (IoT). In addition to Blockchain, IoT has also been able to address some of its limitations by utilizing innovative technologies like big data and cloud computing. For security, Blockchain as a decentralized application will be used. Each block typically contains the transaction data, and power consumption data which can’t be tampered with even if changing all subsequent blocks, which is expensive to do so.

Transcutaneous auricular vagus nerve stimulation modulates the processing of interoceptive prediction error signals and their role in allostatic regulation.

It has recently been suggested that predictive processing principles may apply to interoception, defined as the processing of hormonal, autonomic, visceral, and immunological signals. In the current study, we aimed at providing empirical evidence for the role of cardiac interoceptive prediction errors signals on allostatic adjustments, using transcutaneous auricular vagus nerve stimulation (taVNS) as a tool to modulate the processing of interoceptive afferents. In a within-subject design, participants performed a cardiac-related interoceptive task (heartbeat counting task) under taVNS and sham stimulation, spaced 1-week apart. We observed that taVNS, in contrast to sham stimulation, facilitated the maintenance of interoceptive accuracy levels over time (from the initial, stimulation-free, baseline block to subsequent stimulation blocks), suggesting that vagus nerve stimulation may have helped to maintain engagement tocardiac afferent signals. During the interoceptive task, taVNS compared to sham, produced higher heart-evoked potentials (HEP) amplitudes, a potential readout measure of cardiac-related prediction error processing. Further analyses revealed that the positive relation between interoceptive accuracy and allostatic adjustments-as measured by heart rate variability (HRV)-was mediated by HEP amplitudes. Providing initial support for predictive processing accounts of interoception, our results suggest that the stimulation of the vagus nerve may increase the precision with which interoceptive signals are processed, favoring their influence on allostatic adjustments.

Implementation of Blockchains as a Distributed Ledger

The need for security and privacy calls into question the need for a model which control a large quantity of personal information. Block chain is used to link blocks using cryptography. SHA256 algorithm is used to implement blockchains. It consists of a database that is placed across the nodes on a peer-to-peer network where a copy of the ledger and updates are made independently. Once the update is made it cannot be altered without altering the subsequent blocks. The block chains concept can be extended to form a good solution for problems related to trust issues in society. Key Words: blockchain, distributed ledger, SHA256, security

Bitemporal Remote Sensing Image Change Detection Network Based on Siamese-Attention Feedback Architecture

Recently, deep learning-based change detection methods for bitemporal remote sensing images have achieved promising results based on fully convolutional neural networks. However, due to the inherent characteristics of convolutional neural networks, if the previous block fails to correctly segment the entire target, erroneous predictions might accumulate in the subsequent blocks, leading to incomplete change detection results in terms of structure. To address this issue, we propose a bitemporal remote sensing image change detection network based on a Siamese-attention feedback architecture, referred to as SAFNet. First, we propose a global semantic module (GSM) on the encoder network, aiming to generate a low-resolution semantic change map to capture the changed objects. Second, we introduce a temporal interaction module (TIM), which is built through each encoding and decoding block, using the feature feedback between two temporal blocks to enhance the network’s perception ability of the entire changed target. Finally, we propose two auxiliary modules—the change feature extraction module (CFEM) and the feature refinement module (FRM)—which are further used to learn the fine boundaries of the changed target. The deep model we propose produced satisfying results in dual-temporal remote sensing image change detection. Extensive experiments on two remote sensing image change detection datasets demonstrate that the SAFNet algorithm exhibits state-of-the-art performance.

Fraction Execution Resolver Using a Hybrid Multi-CPU/GPU Encoding Scheme

Modern video coding standards make use of sub-pixel motion estimation to improve the video quality and reduce the bitrate. It is known that the fraction motion estimation (FME) part follows the integer motion estimation (IME) and adds an extra computational overhead due to the interpolation and the additional motion searches. In this paper, we propose a fraction execution resolver (FER) algorithm that lets the encoder skip the fraction part when specific criteria are met by introducing a preliminary fast test decision point (pFTDP) function for the IME part. If the pFTDP returns zero motion vectors (MVs) and the displacement search area center is also zero, then the fraction part is skipped. The pFTDP decision maker is executed only once, when a 2N × 2N block is first met, while all subsequent blocks follow this initial decision either by receiving the necessary MVs and RD from the pFTDP function or by using the precalculated IME values from the GPU kernel. For our experiments, we use a multithreaded CPU environment that also makes use of GPUs only for the integer part. Our evaluations provide a greater than 1600% encoding time saving at its peak in comparison with the default HEVC sequential mode and ideally a saving of greater than 2286% for still video frame sequences. The total average speedup for both Class A and Class B video sequences is ×13.45. The gain of the FER itself is more than ×3.9 compared with the same multithreaded setup environment. The PSNR and bitrate overhead observed are proportional to the tiling scheme used and are more related to the way CABAC works internally. The FER’s negative effects on coding efficiency are proven to be negligible. A balance between speed and quality achieved by using a lower tiling pattern is shown to minimize the negative effects of the encoding scheme pattern. The experimental results confirm the validity of our motivation, namely, that we can benefit from a software fraction execution resolver without any extra hardware costs. The gain is further increased when video sequences have more static blocks than others.

The effect of transcutaneous auricular vagus nerve stimulation (taVNS) on cognitive control in multitasking

Current research in brain stimulation suggests transcutaneous auricular vagus nerve stimulation (taVNS) as a promising tool to modulate cognitive functions in healthy populations, such as attention, memory, and executive functions. Empirical evidence in single-task contexts, suggests that taVNS promotes holistic task processing, which strengthens the integration of multiple stimulus features in task processing. However, it is unclear how taVNS might affect performance in multitasking, where the integration of multiple stimuli leads to an overlap in stimulus response translation processes, increasing the risk of between-task interference (crosstalk). In a single-blinded, sham-controlled, within-subject design, participants underwent taVNS while performing a dual task. To assess the effects of taVNS, behavioral (reaction times), physiological (heart rate variability, salivary alpha-amylase), and subjective psychological variables (e.g., arousal) were recorded over three cognitive test blocks. Our results revealed no overall significant effect of taVNS on physiological and subjective psychological variables. However, the results showed a significant increase in between-task interference under taVNS in the first test block, but not in the subsequent test blocks. Our findings therefore suggest that taVNS increased integrative processing of both tasks early during active stimulation.

Recursive Algorithm for Forming Structured Sets of Information Blocks that Increase the Speed of Performing Procedures for Determining Their Source

Purpose of research. The article examines the features of the synthesis of specialized devices that control the integrity and authenticity of individual information blocks based on CBC codes. Approaches aimed at reducing computational and resource costs when performing such procedures are considered. The dependencies between the performance indicators and the requirements for the volume of internal memory of specialized computers processing data in CBC mode are formulated..Methods. The implementation of data source authentication procedures using encoding in the mode of concatenation of individual data blocks is an effective method of increasing reliability in conditions of a limited size of verified authentication sequences. At the same time, its implementation in receivers requires the creation of specialized calculators that process tree-like structures that arise during data decoding, consisting of separate data blocks. To reduce resource and computational costs when processing such structures, indirect addressing is used, in which block data is stored in RAM, and their addresses are stored in the high-speed register memory of the computer itself.Results. A model of indirect addressing of data blocks in RAM has been created. Pointers to blocks are stored in a separate register memory and each pointer is placed according to the decoded sequence number of the block in the sequence. Each block address in RAM is supplemented by a set of pointers to subsequent blocks in the branches of the tree structure. The created mathematical model made it possible to estimate the size in bits of all the pointers involved and their number, which made it possible to determine the need for a computer processing a tree structure in register memory.Conclusion. The paper shows that using a combination of matrix and list organization of block address storage reduces the need for register memory of the calculator by 50-60%. At the same time, the computer can process the tree-like structure of information blocks using high-performance iterative algorithms, which would be impossible if only the list organization of address storage was used.

Comparative Analysis of Consensus Algorithms in Blockchain

A blockchain is a decentralized, shared, and public digital ledger that is used to log transactions across many devices so that the record cannot be altered, deleted, or destroyed retroactively without the alteration of all subsequent blocks and the consensus of the network. Blockchain is global and open to all users and is considered completely secured and verified. It is possible only because of the presence of the consensus protocol, which is a core part of any Blockchain network. The consensus algorithm is a strategy that a group of computers uses to agree with each other on what's true, and it is the foundation of all cryptocurrency blockchains. It is used to verify transactions and keep the underlying blockchain secure. There are various types of consensus algorithms in blockchain, each with its own set of benefits and losses. One consensus algorithm cannot meet the needs of all applications. Comparing the available consensus algorithms on a technical level is critical to highlight their strengths, weaknesses, and application scenarios. Major algorithms in use today are PoW, PoS, etc but they suffer from one or other problems directly affecting their performance, security, efficiency, and use. From statistics available for the performance and efficiency of various consensus algorithms it is determined that PBFT showcases various promising characteristics needed from a consensus algorithm. We proposed in this paper implementation to mitigate the scalability problem of PBFT. In this paper, we have identified parameters in various consensus algorithms and determined the best suitable consensus algorithm. This paper will serve as a resource for developers and researchers looking to evaluate and design a consensus algorithm

Pre-encoding based temporal dependent rate–distortion optimization for HEVC

Rate–distortion optimization (RDO) is a crucial technique in block-based hybrid video encoders, which determines the coding option for a certain coding unit to achieve optimal rate–distortion (R–D) performance. However, the current RDO method, which is applied in High Efficiency Video Coding (HEVC), is far from being globally optimal as it ignores the interaction among coding options. In fact, the current coding option not only determines the R–D point of the coded unit but also influences the achievable R–D​ performance of the subsequent coding units due to the spatial–temporal dependencies among the block/frame optimizations. In this paper, we first investigate the R–D characteristics, including operational R–D curves and R–D dependencies, in HEVC through several simulation experiments. It is shown that under the hierarchical fixed QP setting in the HEVC encoder, the temporal R–D dependence dominates the coding process, and the distortion of the reference frame has a strong impact on that of coded frames while less influence on rate. We then introduce distortion effect factors to quantitatively measure the temporal R–D​ dependency. The distortion effect factor at frame level reflects the correlation degree between the current coding frame and the previous coded frames, and the one at block level reflects the influence degree that the distortion of a block to that of subsequent blocks which refer to it in temporal domain. Finally, a pre-encoding based temporal dependent RDO is proposed, which adaptively adjusts quantization parameters and Lagrange multipliers according to the distortion effect factors. Experimental results show that under configurations of low-delay B and P frames, the proposed method can achieve 5.4% and 5.5% bit rate savings on average, respectively, with around 7% encoding complexity increment, compared with the original HEVC encoder.

A Review of Stellate Ganglion Block as an Adjunctive Treatment Modality.

Peripheral nerve blocks are becoming increasingly used as adjunctive treatment modalities for a variety of conditions refractory to medical management. Right or left stellate ganglion blocks (SGB) are a specific type of peripheral nerve block that target the sympathetic blockade of neuronal impulses using the injection of local anesthetic and steroids into nerve bundles in the cervical area. This review article is intended to summarize the common uses of stellate ganglion blocks and explain the procedural technique, which has evolved with technological advances in ultrasonography. The similarities between these disease processes are centered around sympathetic hyperactivity. This sympathetic overdrive state is created by increased levels of nerve growth factor (NGF), which causes a cascade of sympathetic sprouting resulting in increased norepinephrine (NE) systemically. Reversal of this cascade by local anesthetic injection into the stellate ganglion thereby reduces NGF and sympathetic sprouting subsequently lowering overall norepinephrine levels. This is the unifying theory by which SGB is able to provide resolution for the varied clinical conditions described in this article. This review article discusses the physiology of several conditions where stellate ganglion blocks are being investigated as an adjunct treatment modality, including anosmia, PTSD, long-COVID, chronic fatigue syndrome, menopausal hot flashes, and ventricular tachyarrhythmias. Overall, the current literature supporting the use of stellate ganglion blocks for several esoteric conditions is limited; however, case reports to date have shown promising evidence-based results supporting their use as an adjunctive treatment among patients with refractory symptoms to existing treatment algorithms. In conclusion, SGB should be considered among patients with refractory symptoms for medical management in the conditions discussed in this article. Further research is needed to delineate which patients will benefit from the use of SGB, the use of subsequent blocks and timelines in between injections, and unilateral versus bilateral blockade.

Aggregation of Masked Outputs for Improving Accuracy–Cost Trade-Off in Semantic Segmentation

Downsampling layers are essential for convolutional neural network-based semantic segmentation methods to widen their receptive fields. However, as fine-grained information is lost in the layers, the accuracy of these methods becomes limited. The need for downsampling layers can be eliminated by using a transformer encoder. Nevertheless, removing downsampling layers inevitably increases the computational cost of the network. In this paper, we present a mask transformer layer that reduces computational cost in any transformer-based networks by substituting a vanilla transformer layer. Additionally, we introduce an aggregation scheme to merge masked outputs, which enhances the accuracy of predictions. Our method aggregates intermediate outputs to generate a final output where the number of intermediate outputs depends on the importance of an area. With this strategy, we achieve different computational cost levels by modulating the threshold used to determine the importance. Our method comprises the following steps. First, we split the transformer encoder into several blocks and attach a segmentation decoder to each block to estimate the intermediate segmentation output. On the basis of the intermediate outputs and predefined thresholds, we classify unnecessary image patches and remove them in subsequent blocks. By progressively masking unnecessary patches, we obtain multiple intermediate outputs for important areas; aggregating them yields better segmentation accuracy with a lower computational burden. In addition, we determine the most effective training scheme and devise a threshold-search algorithm to optimally determine threshold hyperparameters. Extensive experiments on the ADE20K, Cityscapes, and Pascal-Context datasets verify the efficacy of our design, which surpasses the accuracy of the baseline method with lower computational cost.

BEV-radar: bidirectional radar-camera fusion for 3D object detection

Exploring millimeter wave radar data as complementary to RGB images for ameliorating 3D object detection has become an emerging trend for autonomous driving systems. However, existing radar-camera fusion methods are highly dependent on the prior camera detection results, rendering the overall performance unsatisfactory. In this paper, we propose a bidirectional fusion scheme in the bird-eye view (BEV-radar), which is independent of prior camera detection results. Leveraging features from both modalities, our method designs a bidirectional attention-based fusion strategy. Specifically, following BEV-based 3D detection methods, our method engages a bidirectional transformer to embed information from both modalities and enforces the local spatial relationship according to subsequent convolution blocks. After embedding the features, the BEV features are decoded in the 3D object prediction head. We evaluate our method on the nuScenes dataset, achieving 48.2 mAP and 57.6 NDS. The result shows considerable improvements compared to the camera-only baseline, especially in terms of velocity prediction. The code is available at <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/Etah0409/BEV-Radar">https://github.com/Etah0409/BEV-Radar</ext-link>.

Noncoherent OFDM Transmission via Off-the-Grid Joint Channel and Data Estimation

Pilot-aided channel estimation techniques are known to waste the spectral bandwidth. An off-the-grid blind estimator for time-variant orthogonal frequency division multiplexing (OFDM) systems is studied in this letter. In this regard, we propose a blind estimator based on atomic norm minimization (ANM) for OFDM systems. To do so, at the first transmission block, using a lifted ANM (LANM) and simple constraint on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> norm of data, we simultaneously estimate the channel and data. For the subsequent blocks, we use a penalized ANM (PANM) to simultaneously track the channel’s parameters and detect transmit signals. The proposed problems require an infinite dimensional search, hence are NP-hard. Therefore, we propose two semidefinite programs (SDPs) to implement them. We then derive the total computational complexity of the proposed estimator. The simulation results show the superiority of the proposed estimator to the state-of-the-arts.

Functional NIRS to detect covert consciousness in neurocritical patients

ObjectiveThis pilot study assesses the feasibility to detect covert consciousness in clinically unresponsive patients by means of functional near infrared spectroscopy (fNIRS) in a real intensive care unit setting. We aimed to verify if the hemodynamic response to familiar music measured with fNIRS varies according to the level consciousness of the patients. Methods22 neurocritical patients and 6 healthy controls were included. The experiment consisted in 3 subsequent blocks including a first resting state recording, a period of music playback and a second resting state recording. fNIRS measurement were performed on each subject with two optodes on the forehead. Main oscillatory frequencies of oxyhemoglobin signal were analyzed. Spectral changes of low frequency oscillations (LFO) between subsequent experimental blocks were used as a marker of cortical response. Cortical response was compared to the level of consciousness of the patients and their functional outcome, through validated clinical scores. ResultsCortical hemodynamic response to music on the left prefrontal brain was associated with the level of consciousness of the patients and with their clinical outcome after three months. ConclusionsVariations in LFO spectral power measured with fNIRS may be a new marker of cortical responsiveness to detect covert consciousness in neurocritical patients. Left prefrontal cortex may play an important role in the perception of familiar music. SignificanceWe showed the feasibility of a simple fNIRS approach to detect cortical response in the real setting of an intensive care unit.

BLOCKCHAIN: A Cyber Defense Force Multiplier

At its core, a blockchain is simply a database for storing digital information where each of time-stamped, published data is together with all preceding and subsequent blocks. What makes blockchain unique from traditional data storage is that data is not stored or controlled within a centralized database, but rather database management is shared by a decentralized network of trustless nodes that each maintain an identical copy of the blockchain. Before data can be published to the blockchain, it must first be validated by a majority of network nodes—typically referred to as or, depending on the consensus methodology used. This concept of “distributed consensus” makes the blockchain inherently tamper resistant and ensures that only verified transactions will be published to the blockchain.1 The two primary consensus methodologies used in blockchain technology today are , first popularized in 2008 by the founder(s) of the Bitcoin2 blockchain, and , which has been used extensively in the development of derivative blockchain projects to help solve the issues of scalabil- 1 Samantha Ravich, “Leveraging Blockchain Technology to Protect the National Security Industrial Base,” July 10, 2017, https://www.fdd.org/analysis/memos/2017/07/10/leveraging-block chain-technology-to-protect-the-national-security-industrial-base/ 2 Satoshi Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008, https://bicoin.org/ bitcoin.pdf ity and energy consumption associated with proof-of-workbased blockchains.

Cross-domain heterogeneous residual network for single image super-resolution

Single image super-resolution is an ill-posed problem, whose purpose is to acquire a high-resolution image from its degraded observation. Existing deep learning-based methods are compromised on their performance and speed due to the heavy design (i.e., huge model size) of networks. In this paper, we propose a novel high-performance cross-domain heterogeneous residual network for super-resolved image reconstruction. Our network models heterogeneous residuals between different feature layers by hierarchical residual learning. In outer residual learning, dual-domain enhancement modules extract the frequency-domain information to reinforce the space-domain features of network mapping. In middle residual learning, wide-activated residual-in-residual dense blocks are constructed by concatenating the outputs from previous blocks as the inputs into all subsequent blocks for better parameter efficacy. In inner residual learning, wide-activated residual attention blocks are introduced to capture direction- and location-aware feature maps. The proposed method was evaluated on four benchmark datasets, indicating that it can construct the high-quality super-resolved images and achieve the state-of-the-art performance. Code and pre-trained models are available at https://github.com/zhangyongqin/HRN.