Abstract

Dysarthric speech poses significant challenges in developing as- sistive technologies, primarily due to the limited availability of data. Recent advances in neural speech synthesis, especially zero-shot voice cloning, facilitate synthetic speech generation for data augmentation; however, they may introduce biases to- wards dysarthric speech. In this paper, we investigate the effec- tiveness of state-of-the-art F5-TTS in cloning dysarthric speech using TORGO dataset, focusing on intelligibility, speaker sim- ilarity, and prosody preservation. We also analyze potential biases using fairness metrics like Disparate Impact and Parity Difference to assess disparities across dysarthric severity lev- els. Results show that F5-TTS exhibits a strong bias toward speech intelligibility over speaker and prosody preservation in dysarthric speech synthesis. Insights from this study can help integrate fairness-aware dysarthric speech synthesis, fostering the advancement of more inclusive speech technologies.

Abstract

Recent progress and development of artificial intelligence and machine learning (AI/ML) techniques have enabled addressing complex biomolecular problems. AI/ML models learn the underlying distribution of data they are trained on and when exposed to new inputs, they make predictions based on patterns and relationships previously observed in the training set. Further, generative artificial intelligence (GenAI) can be used to accurately generate protein structure or sequence from specific selected properties. This review specifically focuses on the applications of AI/ML in predicting important functional properties of proteins, and the potential prospects of reverse-engineering in depicting the sequence and structure, from available protein-property information.

Abstract

Cloud-based storage service has emerged as a promising alternative to managing local storage, offering users additional features, such as storage, backup, and restoration of various resources, including software, applications, and sensitive private information, within a virtual database, while ensuring data confidentiality and security. However, the widespread use of cloud services by individuals and organisations raises concerns regarding user accessibility and data security due to increasing social threats and adversarial attacks. Searchable encryption (SE) has been introduced to address these issues, providing a secure environment for a convenient and effective way of data searching and sharing. SE models have been advanced by integrating blockchain functionalities to tackle certain existing vulnerabilities and enhance user accessibility, data privacy, and integrity. This survey work explores various research works on SE techniques leveraging cloud and blockchain functionalities, discussing and categorizing the diverse approaches based on different criteria. This study also discusses the different enhancements made to SE techniques over the years, including the underlying requirements that led to the inclusion of blockchain functionalities. Moreover, this study provides a comparative analysis of existing survey works done in this area, which highlights a lack of recent literature surveys that thoroughly explore blockchain-based public key encryption with keyword search (PEKS) schemes. Particularly, we delve into the technical aspects of PEKS by classifying, analyzing and comparing different functionalities based on various aspects. The survey concludes with a comparative analysis of existing PEKS solutions and a discussion on identified research gaps, aiming to improve future research on PEKS approaches in this emerging field.

Abstract

The problem of recovering from qubit erasures has recently gained attention as erasures occur in many physical systems such as photonic systems, trapped ions, superconducting qubits and circuit quantum electrodynamics. While several linear-time decoders for error correction are known, their errorcorrecting capability is limited to half the minimum distance of the code, whereas erasure correction allows one to go beyond this limit. As in the classical case, stopping sets pose a major challenge in designing efficient erasure decoders for quantum LDPC codes. In this paper, we show through simulation, that an attractive alternative here, is the use of quantum expander codes in conjunction with the peeling decoder that has linear complexity. We also discuss additional techniques including small-set-flip decoding, that can be applied following the peeling operation, to improve decoding performance and their associated complexity.

Abstract

This paper presents the development of a real-time object detection system using Frequency Modulated Continuous Wave(FMCW) millimeter-wave radar signals and the PYNQ-ZU FPGA Board, which is widely used in (advanced driving assistance system)ADAS and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment’s point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3D point cloud images into 2D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2D image using the VGG 16 model. We then assessed four machine learning models for object detection and found that the Support Vector Machine (SVM) model and Logistic Regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, Top-View Filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.

Abstract

Robotic inspection of transmission lines presents significant challenges due to the complexity of navigating along the wires. Existing systems often rely on either flight modes for visual inspection or articulated crawling mechanisms for contact-based inspection. However, these approaches face limitations in effectively bypassing in-line obstacles or pylons, which are common in transmission line environments. This letter presents WireFlie, a novel hybrid robotic system that integrates rolling and flight modes to overcome these challenges. The system consists of a pair of underactuated arms mounted on a drone platform, designed for secure, collision-free locking and detaching, enabling seamless transitions between locomotion modes. WireFlie supports both single-arm and dual-arm rolling, allowing it to bypass in-line obstacles such as Stockbridge dampers, dual spacers, and sleeves, and to overcome larger obstacles like pylons using flight. Additionally, we propose a high-level controller for autonomous locking, detaching, and obstacle avoidance. Experiments are conducted on a custom-made setup that closely resembles a transmission wire. We evaluate both the design and control aspects of our system, with results including kinematic analysis, wire detection, autonomous locking and the corresponding trajectory, and obstacle detection and avoidance strategies. This research contributes to the field of robotic infrastructure inspection by merging aerial and wire-based locomotion, providing efficient and autonomous monitoring of power lines.

Abstract

We introduce Berman-intersection-dual Berman (BiD) codes. These are abelian codes of length $3^m$ that can be constructed using Kronecker products of a $3 times 3$ kernel matrix. BiD codes offer minimum distance close to that of Reed-Muller (RM) codes at practical blocklengths, and larger distance than RM codes asymptotically in the blocklength. Simulations of BiD codes of length $3^5=243$ in the erasure and Gaussian channels show that their block error rates under maximum-likelihood decoding are similar to, and sometimes better, than RM, RM-Polar, and CRC-aided Polar codes.

Abstract

Diffusion-based motion planners are becoming popular due to their well-established performance improvements, stemming from sample diversity and the ease of incorporating new constraints directly during inference. However, a primary limitation of the diffusion process is the requirement for a substantial number of denoising steps, especially when the denoising process is coupled with gradient-based guidance. In this paper, we introduce, diffusion in the parametric space of trajectories, where the parameters are represented as Bernstein coefficients. We show that this representation greatly improves the effectiveness of the cost function guidance and the inference speed. We also introduce a novel stitching algorithm that leverages the diversity in diffusion-generated trajectories to produce collision-free trajectories with just a single cost function-guided model. We demonstrate that our approaches outperform current SOTA diffusion-based motion planners for manipulators and provide an ablation study on key components.

Abstract

The shotgun sequencing process involves fragmenting a long DNA sequence (input string) into numerous shorter, unordered, and overlapping segments (referred to as reads). The reads are sequenced and later aligned to reconstruct the original string. Viewing the sequencing process as the read-phase of a DNA storage system, the information-theoretic capacity of noise-free shotgun sequencing has been characterized in literature. Motivated by the base-wise quality scores available in practical sequencers, a recent work considered the shotgun sequencing channel with erasures, in which the symbols in the reads are assumed to contain random erasures. Achievable rates for this channel were identified. In the present work, we obtain a converse for this channel. The arguments for the proof involve a careful analysis of a genie-aided decoder, which knows the correct ordering of the reads. The converse is not tight in general; however, it meets the achievability result asymptotically in some channel parameters.

Abstract

Dual-arm robotic grasping is crucial for handling large objects that require stable and coordinated manipulation. While single-arm grasping has been extensively studied, datasets tailored for dual-arm settings remain scarce. We introduce a large-scale dataset of 16 million dual-arm grasps, evaluated under improved force-closure constraints. Additionally, we develop a benchmark dataset containing 300 objects with approximately 30,000 grasps, evaluated in a physics simulation environment, providing a better grasp quality assessment for dual-arm grasp synthesis methods. Finally, we demonstrate the effectiveness of our dataset by training a Dual-Arm Grasp Classifier network that outperforms the state of-the-art methods by 15%, achieving higher grasp success rates and improved generalization across objects.