The automatic monitoring/tracking of environmental boundaries by multi-agent systems is a fundamental problem which has many practical applications. In this paper, we address this problem with formation control techniques based on parametric curves that represent the boundary’s feedback shape. For that, we approximate the curve with truncated Fourier series, whose finite coefficients are utilized to characterize the curve’s shape and to automatically distribute the agents along it. These feedback Fourier coefficients are exploited to design a new type of formation controller that drives the agents to form desired curves. A detailed stability analysis is provided for the proposed control methodology, considering both fixed and switching multi-agent topologies. The reported numerical simulation study demonstrates the performance and feasibility of our new method to track closed boundaries of different shapes.

Background: Relaxation methods determine residual stresses by measuring the deformations produced by incremental removal of a subdomain of the specimen. Measured strains at any given increment, determined by the cumulative effect of the relieved stresses, appear as an integral equation, which must be inverted to obtain residual stresses. In practice, stress distributions are discretized by a finite-dimensional basis, to transform the integral equations into a linear system of equations, which is often ill-conditioned. Objective: This article demonstrates that the problem is actually ill-posed and comes with an inherent bias-variance tradeoff. Methods: The hole drilling method is used as an example application, and the practical effects of ill-posedness are illustrated. Results: Traditional regularization of the solution by limiting the resolution of the discretization reduces solution variance (noise) at the expense of increased bias and often results in the ultimately harmful practice of taking fewer data points. A careful analysis including the alternate Tikhonov regularization approach shows that the highest number of measurements should always be taken to reduce the variance for a given regularization scheme. Unfortunately, the variability of a regularized solution cannot be used to build a valid confidence interval, since an unknown bias term is always present in the true overall error. Conclusions: The mathematical theory of ill-posed problems provides tools to manage the bias-variance tradeoff on a reasonable statistical basis, especially when the statistical properties of measurement errors are known. In the long run, physical arguments that provide constraints on the true solution would be of utmost importance, as they could regularize the problem without introducing an otherwise unknown bias. Constraining the minimum length scale to some physically meaningful value is one promising possibility.

Precise stock market prediction is crucial for investors, but the volatility of the stock market is influenced by multiple factors such as public sentiments, business news, and related product volatility. While several algorithms have been proposed to predict the stock exchange index based on historical data, they are not ideal as external factors play a critical role in market volatility. To address this issue, we proposed a machine learning model that incorporates historical data with external factors such as social media sentiments, oil and gold trends, and financial news data to enhance prediction accuracy. Our study used HPQ, IBM, ORCL, and MSFT stock market datasets to validate the effectiveness of the proposed model, including an analysis of the impact of Covid19 on companies. Our experimental results showed the highest accuracy of 87.2% using oil and sentiment datasets. Additionally, we identified that social media significantly affects IBM stocks, and the GBM (Gradient Boosting Classifier) classifier produced consistent results.

Mixed-precision Neural Networks achieve high energy efficiency and throughput for hardware deployment. The most common mixed-precision methods adopt layer-wise granularity. However, the layer-wise method does not quantize the model to its limit because the optimal bit precision that preserves accuracy for different kernels can be different. To address this issue, this paper presents GroupQ, a group-wise quantization method with multi-objective optimization for CNN accelerators. Group-wise divides the convolutional kernels in a layer into several groups by clustering, and each group shares the same bit precision. The multi-objective optimization algorithm is used to optimize the quantization policy automatically based on the selected quantization objectives, such as model accuracy, model size, or computation cost. The experiments show that GroupQ significantly outperforms the existing layer-wise retraining-free methods, even better than some training-based methods. Specifically, GroupQ achieves a 0.49% higher accuracy with up to 28.1% smaller Bit Operations (BOPs) on ResNet-18 compared to HAWQ-V3 and can quantize MobileNetV2 to 1.65MB model size with 71.75% top-1 accuracy. This paper shows that GroupQ is friendly for hardware deployment by a lookup table (LUT)-based mixed-precision processing element (LMPE) proposed for CNN accelerators. LMPE provides power reduction of up to 3.6%, up to 3.9% lower area, compared to conventional implementation.

Sparse Regression Code (SPARC) connects the sparse signal recovery framework with the error control coding techniques. In this paper, we focus on improving the block error performance of SPARC in short block length regime over the AWGN channel. Towards that, we introduce suitable candidates for dictionary matrices in real and complex fields using Gold sequences and mutually unbiased bases (MUB). We propose two generalizations of SPARC (GSPARC), develop a greedy decoder called Match and Decode (MAD) algorithm, and provide its analytical noiseless recovery guarantees. We propose a parallel greedy search technique called parallel MAD (PMAD) to improve performance. We describe the applicability of GSPARC with PMAD decoder for multi-user channels, providing a non-orthogonal multiple access scheme. We present numerical results comparing the block error rate (BLER) performance of the proposed algorithms for GSPARC in AWGN channels in the short block length regime. The PMAD decoder gives better BLER than the approximate message-passing decoder for SPARC. GSPARC with PMAD gives comparable and competitive BLER performance compared to other existing codes. In multi-user channels, GSPARC with PMAD decoder outperforms the sphere packing lower bounds of an orthogonal multiple access scheme with the same spectral efficiency.

The challenge of query execution in MySQL involves the efficient and accurate processing of SQL queries to retrieve, manipulate, and store data in a database. It requires optimizing the query execution plan, identifying and resolving performance issues, and ensuring data integrity and security. The challenge lies in balancing the trade-offs between query complexity, database size, system resources, and user requirements. Choosing the right approach depends on the specific characteristics of the data and workload. Therefore, in this paper, we have applied various techniques to get optimized results.

This paper deals with multi-object fusion in the presence of misbehaving sensor nodes, for example due to faults or adversarial attacks. In this setting, the main challenge is to identify and then remove messages coming from corrupted nodes. To this end, a three-step method is proposed, where the first step consists of choosing a reference density among the received ones on the basis of a minimum upper median divergence criterion. Then, thresholding on the divergence from the reference density is performed to derive a subset of densities to be fused. Finally, the remaining densities are fused following either the generalized covariance intersection (GCI) or minimum information loss (MIL) criterion. The implementation of the proposed method for resilient fusion of label multi-Bernoulli densities is also discussed. Finally, the performance of the proposed approach is assessed via simulation experiments on a multi-target tracking case study

Taking into consideration the rise of Industry 5.0 (I5.0), more and more solutions are being developed, aiming towards increased environmental consciousness through advanced technologies, and human centricity. Digital Product Passports (DPPs) constitute a novel technological solution that can enable the transition toward a digital Circular Economy (CE) and sustainable I5.0, as digital identities that are assigned to physical products, capable of tracing their lifecycles through data such as their technical specifications, usage instructions, and repair and maintenance information. Although the respective research community has started providing a thorough analysis of DPPs potential to constitute a CE enabler, their technical requirements are still unclear. As part of our contribution to this issue, we propose a fundamental CE framework with integrated DPP characteristics, capable of being adapted in different sector stages for the generation and distribution of DPPs both for stakeholders and consumers. The corresponding solution is further supported through a systematic literature review that follows a technological approach to the DPPs implementation.

Abstract: An automated and lightweight method to accurately segment optical coherence tomography (OCT) images can bring a plethora of benefits, such as the production of objective diagnostic indicators at a fast rate and the implementation in imaging devices with ease. Due to the unique imaging principle, OCT images differ from natural images as they feature layered structures stretching along the image width, instead of completely closed regions. Conventional convolutional neural networks designed for natural images are usually sub-optimal for segmenting OCT images. Therefore, it is imperative to develop a segmentation network with a strong awareness of the structural features in OCT images for more efficient predictions. In this work, we introduce a novel lightweight deformable OCT segmentation network (LiDeOCTNet) to enable a flexible and scalable feature receptive field for an accurate segmentation of the irregular structures in OCT images. When compared with the classic UNet, LiDeOCTNet achieved better performance in segmenting both retinal and endoscopic OCT images. In comparison to the state-of-the-art networks, LiDeOCTNet offered competitive results with a far more lightweight network. The simplistic design of our network may lead to a feasible OCT-aware framework to achieve reliable segmentation of OCT images in real time.

The Internet-of-Things, popularly referred to as IoT, has been gaining magnanimous attention since the last decade. More and more objects are becoming smart by the integration of sensors in them. All of these objects are interconnected by the internet to produce a variety of services. By 2025, the world is predicted to have over 27 billion devices connected by the Internet-of-Things. However, the internet was not built keeping the security of IoT in mind. This poses a multitude of security threats to IoT. With the expected expansion of the IoT network in the near future, the impact of security breaches will get more dangerous. In this survey, we analyze the biggest attacks that have impaired IoT networks in the past along with the methods which could have been employed to safeguard against such attacks. The aim of this survey is to provide comprehensive guidelines to IoT architects about the potential attack vectors that malicious attackers have exploited in the past and the various defenses that can be undertaken to nullify such attacks. This survey summarizes the lessons that real-world IoT security breaches have taught us over the years.

We propose a fully distributed methodology based on factor graphs for joint cooperative localization and distributed noncooperative target tracking in a 3D scenario where multiple surveillance aircraft fly in formation without access to GNSS measurements or communication with anchor nodes. Our approach is based on the Adapt-then-Combine (ATC) diffusion scheme, which is integrated into the factor graph by the introduction of special combine factors to perform geometric average fusion of the target beliefs over a partially connected network. The updated target belief held by each aircraft following the combine step is also fed back to improve the aircraft’s own self-localization, assimilating the target measurements. Simulation results show that the proposed distributed algorithm performed close to the PCRLB of the optimal centralized solution and that the agents approached a consensus about the target state estimate.

Prime number could be formalized with using notation of sequence. And by utilizing the sequence, approximation in generating prime number could be achieved.

Industrial demand response will become increasingly important in power grids with high shares of variable renewables, yet the existing knowledge on how the industrial electricity demand and flexibility will change with the decarbonization of chemical processes is limited. Here we develop a mixed-integer linear optimization model, which we use to compare the cost and flexibility of the most relevant decarbonization options for the combined chlor-alkali electrolysis (CAE) and vinyl chloride monomer (VCM) production process. We combine product and energy storage to enable the full flexibility potential of the decarbonized process. Our results show that flexible operation of the CAE process is deemed technically possible but limited by internal process dependencies due to decarbonization of the VCM production. Combining energy and product storage for demand response enables up to 4% operational cost reduction by shifting loads during peak price hours. High overcapacity of PEM electrolyzers are required to release the full flexibility potential in the hydrogen based decarbonization option, while the less flexible direct electrification option shows a potential for OPEX reduction. Full decarbonization of the combined CAE and VCM process without increasing operational cost significantly appears difficult. Our study emphasizes demand response through product and energy storages as a viable pathway for minimizing the added cost, and also enables a significant reduction of electric demand in high-price hours.

For a long-distance guard radar, MTI filter usually employs low staggered pulse repetition frequencies (PRF) modulation technique to overcome blind speed and detect the targets at low altitude in the presence of clutter. For a given ratio of the maximum-to-the minimum period, we find that there exists an optimum stagger ratios construction criterion (OSRCC) which can guarantee the first null depth minimum, and the associated estimation formula containing variable    has been derived successfully. In addition, based on more understanding to the properties of staggered PRF, we introduce a new stagger ratios optimization method that is much simple, efficient and flexible. In brief, this paper has thoroughly deconstructed this important basic technique in radar system.

Subway timetabling problems are important but difficult discrete optimization problems that are usually solved with strict computational time requirements in order to minimize passenger waiting time while maximizing energy-saving profits. To obtain high-quality solutions, a promising direction is to formulate the problems as Mixed Integer Linear Programming (MILP) problems, thereby taking advantage of widely available MILP methods such as Branch-and-Cut (B&C). However, based on existing MILP formulations, the computational efforts required by MILP methods are high because of the large numbers of decision variables and constraints. In this paper, we develop a novel MILP formulation for optimizing subway timetables. In the formulation, train arrival times are innovatively formulated by using decision variables of two types: continuous variables for formulating energy-saving profits and binary variables for formulating passenger waiting time. Compared to existing formulations, the proposed formulation has far fewer decision variables and constraints. Numerical testing results show that high-quality solutions are efficiently obtained using B&C based on the proposed formulation in reasonable time. Thus, the proposed formulation paves the way for efficiently solving subway problems using MILP methods.

System logs are a valuable source of information for monitoring and maintaining the security and stability of computer systems. Techniques based on Deep Learning and Natural Language Processing have demonstrated effectiveness in detecting abnormal behavior from these system logs. However existing approaches are inflexible and impractical: techniques that rely on log templates are unable to handle variability in log content, while classification-based approaches require labeled data for supervised training. In this paper, a novel log anomaly detection model named LogFiT is proposed. The LogFiT model is robust to changes in log content and only requires self-supervised training. The LogFiT model uses a pretrained BERT-based language model fine-tuned to recognise the linguistic patterns of the normal log data. The LogFiT model is trained using masked token prediction on the normal log data only. Consequently when presented with the new log data, the model’s top-k token prediction accuracy is used as threshold for determining whether the new log data has deviated from the normal log data. Experimental results show that LogFiT’s F1 score exceeds that of baselines on the HDFS, BGL and Thunderbird datasets. Critically, when variability in the log data is introduced during evaluation, LogFiT’s effectiveness surpasses that of the baseline models.

Security is always a prime concern of every cloud user. Many pioneer surveys have been done concerning security during data communication among owners, users, and stakeholders. However, there is a lack of literature surveys that address the concern of security during data processing or the workload execution within cloud data centers. In this context, we present a concise review of the existing methods that have addressed the security of workload or user data during load scheduling and execution within cloud environments. These methods have applied load-balancing strategies that helped to minimize the security attacks via co-residency or covert channels. The pandect comparative summary of these methods follows the corresponding review. The paper is concluded with limitations and future direction concerning secure load management.

The goal of this study was to validate a series elastic actuator (SEA)-based robotic arm that can mimic three abnormal muscle behaviors, namely lead-pipe rigidity, cogwheel rigidity, and spasticity for medical education training purposes. Key characteristics of each muscle behavior were first modeled mathematically based on clinically-observed data across severity levels. A controller that incorporated feedback, feedforward, and disturbance observer schemes was implemented to deliver haptic target muscle resistive torques to the trainee during passive stretch assessments of the robotic arm. A series of benchtop tests across all behaviors and severity levels were conducted to validate the torque estimation accuracy of the custom SEA (RMSE: ~ 0.16 Nm) and the torque tracking performance of the controller (torque error percentage: < 2.8 %). A clinical validation study was performed with seven experienced clinicians to collect feedback on the task trainer’s simulation realism via a Classification Test (CT) and a Disclosed Assessment Test (DAT). In the CT, subjects were able to classify different muscle behaviors with a mean accuracy > 87 % and could further distinguish severity level within each behavior satisfactorily. In the DAT, subjects generally agreed with the simulation realism and provided suggestions on haptic behaviors for future iterations. Overall, subjects scored 4.9 out of 5 for the potential usefulness of this device as a medical education tool for students to learn spasticity and rigidity assessment.