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As the fifth generation (5G) mobile communication systems are commercially deployed, they bring new services, enhance user experiences, and offer various opportunities to different industries. Despite its advancements, 5G encounters several challenges. To tackle these issues, global industrial, academic, and standards organizations are actively researching on sixth generation (6G) wireless communication systems. 6G networks are envisioned as a transformative shift in the interactions between the physical, digital, and human realms, paving the way for a pervasive human-centered cyber-physical world. 6G is positioned to be a platform that offers communication and beyond communication services considering both performance and value centric technological development approaches. In this paper, we present the way forward towards the design of 6G endto-end (E2E) system as a consolidated view of leading industry stakeholders and academia in the European level 6G flagship project Hexa-X-II. We discuss the key challenges with 5G and how 6G is expected to tackle those by introducing new technological innovations and supporting novel use case requirements. We provide a comprehensive methodology for the design of a 6G E2E system including ten principles, a blueprint, and a structured design process. The architecture design principles prioritize environmental sustainability, digital inclusiveness, and trustworthiness, considering their impact on the 6G E2E system. The blueprint is described corresponding to the infrastructure, network centric application, and application layers, as well as the pervasive functionalities and the relevant technological innovations. Following the design principles and the system blueprint, the design process is demonstrated as two-way approaches (i.e., 1) key performance and value indicators based design process. 2) top-down versus bottom-up alignment process) through the lens of a collaborative robot use case. Through this use case, a special attention is given to the technological enablers that cover management and orchestration functionalities and their 6G enhancement to go beyond the limitations characterizing the previous network generation. In addition, virtual modelling aspects related to digital twining and simulations for 6G E2E system design are also discussed.

The SKY130 Process Design Kit (PDK) offers limited options for Static Random-Access Memory (SRAM) configurations, providing only three predefined memory sizes: $8\times 1024$ , $32\times 256$ , and $32\times 512$ , this poses a challenge for designers who require memories with different characteristics, as they must either design an entire memory architecture from scratch or resort to interleaving techniques with the available memory configurations. To address this issue, we present a novel framework that automates the generation of multiple memory arrays with custom floorplans, leveraging the concept of interleaving memory. Our framework enables designers to create various sizes and configurations memories by combining and interleaving the existing SKY130 PDK memories, additionally, the framework allows designers to easily specify their desired memory size, word length, floorplan, and other essential parameters. The framework then automatically generates multiple memory arrays that meet the specified requirements. Furthermore, it provides the files required by OpenLane, facilitating the seamless integration of these memories into the Register Transfer Level (RTL) to Graphic Data System II (GDSII) flow. The key advantage of our framework lies in its ability to streamline the creation of custom memories by automating the interleaving process and offering flexibility in floorplan design. This significantly reduces design time and effort, empowering designers to efficiently create memories with specific characteristics while adhering to the limitations of the SKY130 PDK. The framework thus serves as a valuable tool for memory design in the context of the SKY130 PDK, opening doors for more efficient and optimized chip designs.

For various daunting physical world structural optimization design problems, a novel multi-objective water strider algorithm (MOWSA) is proposed, and its non-dominated sorting (NDS) framework is explored. This effort is inspired by the recent proposals for the Water Strider Algorithm, a population-based mathematical paradigm focused on the lifespan of water strider insects. The crowding distance characteristic is integrated into MOWSA to improve the exploration and exploitation trade-off behavior during the advancement of the quest. Furthermore, the suggested a posteriori approach exercises the NDS technique to maintain population diversity, a key issue in meta-heuristics, especially for multi-objective optimization. Structural mass reduction and nodal deflection maximization are two diverse objectives for the posed design problems. At the same time, stress on the components and discrete cross-sectional areas are imposed on safety and side constraints, respectively. Eight planar and spatial truss design problems demonstrate the utility of the proposed MOWSA approach for solving complex problems where the performance analysis is based on ten globally accepted metrics. Moreover, MOWSA outcomes were compared with four state-of-the-art optimization techniques to measure the viability of the suggested algorithm. MOWSA outperforms other considered algorithms concerning computational run to achieve optimal solutions and their qualitative behavior over Pareto fronts. The Matlab code for MOWSA can be obtained from https://github.com/kanak02/MOWSA.

Graph-based methods, distinguished by their resilience to noise, have demonstrated efficacy in image segmentation compared to alternative techniques. Despite their notable capabilities, these methods present over- and under segmentation problems. This paper introduces a novel graph-based approach specially designed to solve these issues effectively. Unlike conventional graph-based methodologies, which predominantly favor either local or global features, our approach considers a combination of these two. Initially, the complete graph is partitioned into multiple subgraphs considering the similarities of the local features in the nodes. Subsequently, these subgraphs are evaluated and classified as either significant or irrelevant, based on the number of nodes they contain. To mitigate over segmentation, we merged the irrelevant subgraphs with the significant subgraphs considering their global features. These mechanisms allow our method to maintain integration between the local and global features, leading to a more precise and accurate representation of the objects in the image. The proposed method was evaluated using various complex images and was benchmarked against state-of-the-art segmentation techniques. The results confirm the superiority of our approach in delivering higher-quality and more dependable segmented images than existing methods.

Musculoskeletal disorders of the foot are a common complaint in the population. It has been found a flatfoot prevalence of 13.6% in young adults and a prevalence of 26.62% in adults between 42 and 91 years. Different non-invasive techniques can identify the type of foot by analyzing the soles of the feet, such as the Chippaux-Smirak index (CSI). Although CSI is a non-invasive technique, it is performed manually, and the intervention of an expert is necessary to give a clinical opinion. The use of automatic systems is an alternative. This article introduces a machine learning-based tool that permits the identification of foot types. The proposal employs a wrapper feature selection mechanism to select the subset of features that improves the classification. This task is considered from an optimization perspective, and the optimal subset is chosen using metaheuristic algorithms. Eight algorithms used in the optimization are compared, and an increase in the Accuracy of the K-nearest neighbors (KNN) classifier is observed from 73.5% to 94.7%. Of the 39 total features proposed in the dataset, only 10 features are considered significant. The significance of the characteristics implies that they have an effect on the morphology of the foot. If they are considered in treatments to minimize this disease, it can reduce their development costs.

Homomorphic filtering (HF) is a methodology that separates an image into two components: illumination and reflectance. Through the processing of these components, it is possible to significantly improve the contrast of the low-frequency components while preserving the edges and sharp features of the image. The parameter values of the filter that produces the best possible contrast enhancement depends on the image conditions for each image. However, finding the optimal parameters for the filter can be challenging, often involves a trial-and-error process, and can be prone to errors due to human factors. In this paper, we consider the problem of identifying the filter parameters as an optimization problem. Under such conditions, the cluster chaotic optimization (CCO) method is used to efficiently explore the parameter space by evaluating an objective function that assesses the contrast quality of an enhanced image. The experimental results show that the proposed method produces competitive results in terms of quality, stability, and accuracy compared with other methods on various datasets. Different metrics were evaluated to demonstrate the quality of the results of our method compared with the other algorithms.

In recent years, statistical methods have been applied to the study of crime patterns. However, these schemes have several drawbacks that prevent accurate modeling of complex behaviors. Agent-based models (ABM) allow the modeling of human behavior by employing simple rules that consider each agent’s neighborhood. In this paper, a new agent-based model is proposed to emulate crime patterns produced by the interaction of different urban actors, such as offenders (criminals), citizens, and defenders (police officers). Using this approach, the simulation results provide escape trajectories and robbery frequencies that can be used to create or improve public security strategies. Although our scheme can be generically applied, we validated the model by considering different scenarios for the case of Guadalajara, Mexico. Experimental results show that the proposed scheme creates realistic offender behaviors that efficiently predict criminal patterns and provides essential data that allow the creation and improvement of public security strategies to reduce the number of crimes.

Automatic circle detection is one of the most important construction elements to design more complex industrial image tasks such as target detection, manufacture inspection and process control. Most of the literature concerning multi-circle detection can be categorized under deterministic or stochastic perspectives. Deterministic approaches combine geometric and histogram information to identify circle shapes. However, deterministic techniques are unable to detect circles in presence of noise, shape variance, or occlusion. On the other hand, stochastic methodologies such as metaheuristic algorithms have been also proposed as alternatives to deterministic approaches for circle detection. Although these techniques are in general more robust, they allow detecting only one circle per execution, translating the detection problem into a unimodal optimization problem. Under such conditions, it is necessary to execute multiple times the algorithm to identify all the circles contained in the image. In this paper, the multi-circle detection problem has been reformulated as a multimodal optimization problem. The proposed approach adopts the Multimodal Flower Pollination Algorithm (MFPA) to detect all the circular instances contained in the image. Experimental results suggest that the proposed approach significantly improves the multi-circle detection problem.

Presents corrections to the paper, (Corrections to “Wrapper-Based Feature Selection to Classify Flatfoot Disease”).

Neural networks have been used for a long time for image detection and recognition applications due to their ability and efficiency in complex problem solving. Several researchers have chosen to design and develop hardware accelerators for the convolution layer due to the large computational expense consumed by this layer. For that reason, a system that performs indirect GEMM convolution is implemented in a FPGA in this letter. Thus, the input data is segmented and distributed into acceleration modules in a parallel and distributed manner using the Network-on-Chip (NoC) paradigm, and a systolic array (SA) is implemented for the matrix multiplication operation as processing blocks within each NoC Node. Synthesis and performance results show that the implementation of this system presents better results compared to the state of the art in areas, such as acceleration factor, consumption of resources, latency, and operational frequency. [ABSTRACT FROM AUTHOR]

Caravel is an open-source project developed by Efabless for creating custom system-on-chips (SoCs). It includes the design of a configurable chip, development tools, and documentation. The Caravel SoC includes a RISC-V with the Instruction Set Architecture RV32I. One of the key features of Caravel is its open-source nature. In this letter, the vector accelerator unit for the Caravel SoC template is presented to increase the RISC-V capabilities, allowing parallel data processing through 14 vector operations. The accelerator is based on 4 Arithmetic Logic Units connected directly to the RISC-V through the logic analyzer port and the general-purpose input/output (GPIO) port. The total area of this accelerator is less than 20% of the User Project Wrapper area allowing the user to implement their custom designs in 8.4256 mm2. [ABSTRACT FROM AUTHOR]