The detection accuracy and speed of grasp detection models on benchmarks are the focal points of concern in the robotic grasping community. Especially in a collaborative robot setting, the safety of the model is an essential aspect that cannot be overlooked. In this paper, we explore how to enhance the safety of grasp detection models in autonomous vision-guided grasping. Specifically, we propose a simple yet practical Safety-optimized Strategy, which consists of two parts. The first part involves depth prioritization, optimizing the grasp sequence from top to bottom based on the order of depth values, which can mitigate the issue of grasp collisions that may arise when the depth value of the object with the highest grasp quality is significantly higher than that of other objects in high-clutter scenarios. The second part is false-positive protection, where we introduce the robust Aruco marker as the lowest grasp priority. The marker is fixed at certain positions within the camera's field of view, enabling the robot to halt its movement, thereby restraining the robot from grasping objects that should not be grasped. Once the marker disappears, the robot can resume its operations. We validate our method through real grasping experiments with a parallel-jaw gripper and an industrial robotic arm, demonstrating its effectiveness in high-clutter scenarios.

This research explores the efficacy of machine learning and deep learning models in predicting soil moisture, a critical factor in optimizing agricultural irrigation systems. Utilizing data from the Vantage Vue weather station and Watermark 200 SS soil moisture sensors, we conducted a comparative analysis of traditional models like RandomForest and MLP against advanced deep learning models, particularly LSTM and 1D Convolutional Neural Networks, enhanced with attention mechanisms. The study reveals that attention-augmented models, especially the CONV1D+Attention model achieving an R2 value of 0.51, excel in capturing the complex dynamics of soil moisture. These results underscore the potential of such models in handling complex timeseries data in contexts like soil moisture levels influenced by weather conditions, offering significant insights for improved water management and sustainable agricultural practices globally.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started reverberating around various disciplines. To this end, this work was undertaken: (i) firstly, to track the unfolding landscape of general issues surrounding GenAI tools; (ii) secondly, as an exploratory inquiry to elucidate the specific opportunities and limitations of GenAI tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices. As part of the investigation, we conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Further, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E, and 3DGPT) for mechanical engineering-related tasks. The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

P versus NP is considered as one of the most fundamental open problems in computer science. This consists in knowing the answer of the following question: Is P equal to NP? It was essentially mentioned in 1955 from a letter written by John Nash to the United States National Security Agency. However, a precise statement of the P versus NP problem was introduced independently by Stephen Cook and Leonid Levin. Since that date, all efforts to find a proof for this problem have failed. Another major complexity class is NP-complete. It is well-known that P is equal to NP under the assumption of the existence of a polynomial time algorithm for some NP-complete. We show that the Monotone Weighted Xor 2-satisfiability problem (MWX2SAT) is NP-complete and P at the same time. Certainly, we make a polynomial time reduction from every directed graph and positive integer k in the K-CLOSURE problem to an instance of MWX2SAT. In this way, we show that MWX2SAT is also an NP-complete problem. Moreover, we create and implement a polynomial time algorithm which decides the instances of MWX2SAT. Consequently, we prove that P = NP.

In the pursuit of enhancing human life and safety through artificial intelligence (AI), transparency and predictability emerge as crucial elements. The potential for self-awareness in AI, we argue, hinges on its autonomy in decision-making. To address this, we propose three fundamental laws of AI, emphasizing their practical implementability. These laws aim to establish a framework that ensures transparency, predictability, and independence in decision-making, ultimately contributing to the responsible development and deployment of artificial intelligence for the benefit of humanity.

We discuss an encryption scheme based on reinforcement learning where a ciphertext is given by an optimal path from the message given by the Bellman equation. We then prove that the encryption scheme is CP A-secure. We discuss possible ways to use the photoelectric effect in electric batteries and the construction of a carbon dioxide filter.

I show that a computer cannot have unambiguous thoughts, not even about a number. What we believe computers do is our own convention. It may seem objective because we anchor it in the user interface. But many other conventions are possible, and they yield different computations, equally valid according to the principles of Computer Science. I prove that the alternative computations equally happen when a single computation is carried out, and in principle they can be accessed. I exemplify this with a program that computes the result for a given input, and then decodes it into the results for all other possible inputs. If thinking would be a computation, a computer would have different, possibly opposite thoughts, corresponding to many alternative computations it implements at the same time. I show probabilistically that the human mind does not have this ambiguity. Therefore, even if the human mind can be simulated by a computer, it cannot be reduced to computation.

A document by Sheetal Temara. Click on the document to view its contents.

The photon energy with its corresponding frequency, wavelength and distance can be determined by the main physical parameters (mass, distance, time) of the particles (electron, nucleon, molecule) as a result of their excitation, transition, vibration and rotation. The Planck's constant is interpreted which represents three main physical parameters (mass, distance, time) as a result of the physical mechanisms responsible for excited states of nuclei, atoms and molecules. As a result of equality between Planck's constant and the mentioned three physical parameters a new equation of calculating exact distance of all photons from nuclei, atoms and molecules from radio waves to gamma rays is deduced.

With the rapid development of collaborative computing, the Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model has been initially applied as a technique of Role-Based Collaboration (RBC). By extending the Group Multi-Role Assignment (GMRA) model after in-depth study on E-CARGO-based Group Role Assignment (GRA), a Team Multi-Role Assignment with Role Dependency (TMRARD) model is proposed with a higher degree of generalizability. The model aims to address how to effectively assign collaborative units (agents) to multiple roles for maximizing group performance and synergistic effect in the collaborative process with consideration of the role dependencies and the common team goals. By analyzing the characteristics and elements of the E-CARGO model, the TMRARD model is formally modeled based on E-CARGO. After analyzing the rationalization of the scale of role-dependent inputs, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) has proposed and developed with consideration of the complexity of role-dependent constraints. Simulation experiments have verified the effectiveness and robustness of this method, demonstrating high performance in large-scale and complex constraint situations, in order to provide more scientific and efficient decision-making support for the field of collaborative computing.

This paper presents a systematic approach to generate layouts for two devices, with an arbitrary integer ratio of device sizes, that cancels up to at least 3 rd order gradient effects. A new analysis leads to mathematical constraints on 1dimensional layouts that meet the required integer ratio and cancel 2 nd-order gradients. From those layouts, we apply reflection and rotation symmetries to generate 2-dimensional layouts that cancel higher-order gradients. Importantly, our procedures can handle "dummy lanes" to ensure "routability". Furthermore, we discuss evaluation metrics that identify which of the multiple gradient canceling layout is best for any fixed rectangular grid and device application.

Objective: In this paper we propose a novel use of the wavelet transform in analyzing spectral impedance to discriminate cancerous and non-cancerous lung tissue. Methods: Cancerous and Non Cancerous ex-vivo tissue samples were obtained during a human clinical trial of patients undergoing a lobectomy. Spectral Impedance measurements of the tissues samples were performed using a custom impedance bridge and measurement system. Three methods are described using the wavelet transform of the spectral impedances and comparing results to pathological assessments of the lesion. Results: In a cohort of n=46 patients, for which both tumor and away-from-tumor samples have been collected, we report sensitivity of 87% and specificity of 84%. Conclusion: Three techniques have been proposed as an algorithm basis to improve the sensitivity and specificity of discriminating between cancerous and non-cancerous tissue. Significance: Lung Cancer is the cause of 20% of all cancer related death globally and is the most diagnosed cancer worldwide. Utilizing the proposed methods of analyzing tissue samples and reporting on cancer state at the surgical suite offers clinicians an additional tool to better diagnose and subsequently treat the deadly disease.

Individuals with transtibial amputation can activate residual limb muscles to volitionally control robotic ankle prostheses for walking and postural control. Most continuous myoelectric ankle prostheses have used a tethered, pneumatic device. The Open Source Leg allows for myoelectric control on an untethered electromechanically actuated ankle. To evaluate continuous proportional myoelectric control on the Open Source Ankle, we recruited five individuals with transtibial amputation. Participants walked over ground with an experimental powered prosthesis and their prescribed passive prosthesis before and after multiple powered device practice sessions. Participants averaged five hours of total walking time, and received no visual feedback during practice. After the final testing session, participants indicated their prosthesis preference via questionnaire. Participants increased peak ankle power after practice (powered 1.02 ± 1.09 W/kg and passive 0.3 ± 0.13 W/kg). Additionally, participants generated greater ankle work with the powered prosthesis compared to their passive device (p=0.009, 148% increase). Although peak power generation was not different, participants preferred walking with a prosthesis under myoelectric control compared to their passive device. These results indicate individuals with transtibial amputation can walk with an untethered powered prosthesis under continuous myoelectric control and generate similar magnitudes in peak power to their passive prosthesis after minimal training.

The post-pandemic rebound in Sri Lanka's tourism sector offers a vital opportunity for well-thought-out hotel development. This study identifies the best places for new hotels across the nation using machine learning and geospatial data science methods. Through an extensive dataset analysis of the remaining hotels, which includes district-level characteristics, room capacity, and geographic coordinates, we reveal spatial patterns and clusters that influence the choice of sites. The research methodology evaluates the spatial distribution of hotels and pinpoints market gaps by integrating data processing, geospatial visualization, and K-means clustering. Different geographic clusters that indicate high potential areas for new hotel development are revealed by our findings. Predictive analytics is also used to recommend the best locations based on variables like hotel quality, room capacity, and proximity to popular tourist destinations. The study's conclusions offer insightful recommendations for the hospitality sector's players, offering a data-driven basis for choices about where to locate hotels, how to distribute resources, and how to organize regions. This research helps Sri Lanka's hotel industry grow strategically by utilizing geospatial data and machine learning to make sure that new projects are positioned for long-term success and in line with market demand.

Due to the complex rotor design of reluctance synchronous machines, a finite element analysis is essential for the accurate calculation of machine relevant performance objectives. Reluctance synchronous machines tend to have a large torque ripple, if this objective is not considered during the machine design. This necessitates a large number of simulation steps, resulting in a high computational burden and a long simulation time per design evaluation. Therefore, an efficient optimization algorithm is required. This paper proposes a complete framework for single-objective machine design optimization using Gaussian process regression and Bayesian optimization. Focusing on reluctance synchronous machine design, different kernel functions (squared exponential, Matern, rational quadratic) and hyperparameter configurations ´ are assessed for regression accuracy of the optimization objectives mean torque, torque ripple, and power factor. The impact of noise in the input data on the regression results is also investigated. Bayesian optimization with the infill criterion Expected Improvement is finally used to perform machine design optimization for 18 design variables. Bayesian optimization outperforms the classical algorithms such as genetic or particle swarm algorithms. It results in a faster design optimization, even for such a high number of design variables.

The article introduces an innovative wireless backhauling approach employing non-orthogonal multiple access (NOMA) and automatic repeat request (ARQ) mechanisms. In this novel scheme, power allocation follows a round-robin (RR) method, ensuring equitable performance among paired users. To address the potential packet loss afterARQ, an intelligent packet repair technique is incorporated to recover the dropped packets. A key feature involves storing dropped data packets for subsequent processing before forwarding to their respective IoT devices (IoDs). The proposed methodology hinges on recognizing that interference within a dropped packet may correspond to a packet retrievable in a forthcoming transmission, facilitating recovery through iterative successive interference cancellation (SIC). Significantly, the scheme enhances data reliability without necessitating an increase in the ARQ retransmission limit, which makes it particularly suited for certain Internet of things (IoT) applications. Empirical results confirm a substantial success rate in recovering dropped packets. Notably, the iterative interference cancellation (IIC) technique demonstrated a noteworthy reduction in the packet drop rate (PDR) from 10 −1 to 10 −3 , representing a 100-fold improvement. This implies the successful recovery of 99% of the packets initially dropped in specific scenarios, showcasing the efficacy of the proposed approach.

Wireless audio transmission has always been the focus of Bluetooth application scenarios. Future audio use cases, such as multi-streaming to assist stereo imaging experiences, broadcast audio sharing, hearing aid support, etc., have higher requirements for quality of service (QoS). LE Audio based on Bluetooth Low Energy (BLE) is considered to be a replacement for the Bluetooth classic audio standard in the next generation of audio applications. However, until now, the performance of CIS-based LE Audio has not been fully analyzed. In this paper, we propose a mathematical model to evaluate the performance of CIS such as packet loss rate (PLR), throughput, backlog, delay, and average power consumption. In addition, the feasibility of multi-hop transmission based on CIS is explored, and the model is extended to analyze the end-to-end PLR and throughput. Finally, the accuracy of the proposed model is verified by simulation results, and the relationship between CIS parameters and performance is analyzed, providing guidance for parameter selection in LE Audio applications.

This study addresses the efficiency challenges in the Hadoop fair scheduler when handling interdependent multi-job workloads in a single-tenant Hadoop environment. Hadoop has become the leading choice for large-scale data processing by leveraging MapReduce parallelism. However, conventional fair scheduling struggles with interdependent multi-job workloads, where the dependency is in the form of one job taking the output of another job as input, leading to underutilized cluster resources and extended job waiting times owing to job dependencies. To remedy this, this research introduces a dynamic job grouping approach that optimizes fair scheduler performance and reduces the overall workload completion time. Simulating a healthcare workload with 35 interdependent jobs, the approach demonstrates a 35% to 40% reduction in average job completion time, showcasing adaptability in diverse scenarios. Recommendations include integrating dynamic job grouping into a fair scheduler, optimal parallelism factor consideration, and the exploration of dynamic job submission mechanisms in the Hadoop ecosystem with diverse datasets. As organizations increasingly adopt Hadoop for multi-job workloads, the proposed approach provides valuable enhancement for dynamic and adaptive job submission, improving overall efficiency.