Passive acoustic monitoring (PAM) is a powerful tool for studying marine biodiversity, but large-scale analysis of underwater recordings is constrained by noise, overlapping signals, and limited labeled data. Here, we present a scalable, unsupervised contrastive learning framework for marine soundscapes. Using a large PAM dataset spanning multiple biogeographies, we show that the proposed approach organizes recordings into clusters with well-defined internal structure, as assessed using intrinsic clustering metrics and within-cluster similarity. The resulting clusters reveal recurring acoustic patterns that correspond to broad sound-source categories, including biological sounds such as fish calls and choruses, and anthropogenic sounds such as vessel noise, without explicitly enforcing these distinctions during training. Compared with established approaches, including cepstral features, variational autoencoders, and supervised pipelines, the proposed framework produces embeddings that support more compact and stable unsupervised clustering while preserving fine-scale acoustic variation beyond predefined species labels. By learning a shared representation across recordings from multiple sites and years, we examine the reproducibility of acoustic patterns across locations and identify both site-shared and site-specific sound signatures. Although the method is not designed to recover coarse species labels, it enables label-efficient analysis by reducing reliance on manual annotation and supporting exploratory characterization of complex marine soundscapes. Together, these results highlight multi-positive contrastive learning with a teacher network and acoustically informed augmentations as an effective strategy for scalable, discovery-driven analysis of passive acoustic monitoring data.

Teaching astronomy to children has the potential to develop their imagination and critical thinking and might encourage them to pursue a career in science, technology, engineering, and mathematics (STEM) related fields. Gamified mobile applications are tools that could be used to teach effectively astronomy concepts to children by simplifying them and making the learning experience fun and engaging. This study analyzed and assessed the specifications of 6 gamification-based applications for astronomy education, covering their content and technical features. The scoring system assigned a score with a rating from very high to very low for each of the applications. None of the applications scored a very high rating, in either the content or the technical features. The technical features have an overall lower score relative to the content. We discussed the implications of our findings in the paper and suggested recommendations for the developers of future astronomy-related applications.

Tactile object shapes are considered as important properties in robotic manipulation. Many researches have focused recently on using tactile sensing systems to enable tactile information processing in robotics. Spiking Neural Networks (SNNs) are emerging as promising methods alternative to deep learning due to their ability to process information in an event-driven manner. In this paper, we propose a SNN architecture and hardware implementation for tactile object shapes recognition. The network is fed by an array of 160 piezoresistive tactile sensors where the object shapes are applied. Results demonstrate that the proposed system is able to discriminate the tactile object shapes with 100% accuracy on unseen data having time steps up to 0.1 ms. Moreover, the network has been implemented on a Raspberry Pi platform achieving real time classification.

Employing Machine learning algorithms in tactile sensing systems have emerged recently to recognize/classify touch patterns. The high computational complexity of the ML algorithms makes challenging the embedded implementation of tactile data processing. This paper proposes a complexity optimized tensorial-based machine learning algorithm for touch modality classification. The aim is to introduce an efficient algorithm minimizing the system complexity in terms of number of operations and memory storage which directly affect time latency and power consumption. With respect to the state of the art, the proposed approach reduces the number of operations per inference from 545 M-ops to 18 M-ops and the memory storage from 52.2 KB to 1.7 KB. Moreover, the proposed method speeds up the inference time by a factor of 43× at a cost of only 2% loss in accuracy.

This paper presents a novel hardware architecture of the Tensorial Support Vector Machine (TSVM) based on Shallow Neural Networks (NN) for the Single Value Decomposition (SVD) computation. The proposed NN achieves a comparable Mean Squared Error and Cosine Similarity to the widely used one-sided Jacobi algorithm. When implemented on an FPGA, the NN offers$324\times $faster computations than the one-sided Jacobi with reductions up to 58% and 67% in terms of hardware resources and power consumption respectively. When validated on a touch modality classification problem, the NN-based TSVM implementation has achieved a real-time operation while consuming about 88% less energy per classification than the Jacobi-based TSVM with an accuracy loss of at most 3%. Such results offer the ability to deploy intelligence on resource-limited platform for energy-constrained applications.

Electronic-skin aiming to mimic human skin is becoming a reality and systems able to process data close to the sensors are required to reduce latency and power consumption. This paper presents the design and implementation of an energy efficient smart system for tactile sensing based on a RISC-V parallel ultra-low power platform (PULP). The PULP processor, called Mr. Wolf, performs the on-board classification of different touch modalities. This demonstrates the promising use of on-board classification for emerging robot and prosthetic applications. Experimental results demonstrate the effectiveness of the platform on improving the energy efficiency of the online classification. In our experiments, Mr. Wolf runs 3.6 times faster than an ARM Cortex M4F (STM32F40), consuming only 28 mW. The proposed platform achieves 15× better energy efficiency, than the classification done on the STM32F40, consuming only 81mJ per classification.

Developing portable autonomous systems is highly requested for numerous application domains such as Internet of Things (IoT), wearable devices, and biomedical applications. Portable systems usually contain autonomous and networked sensors; each sensor hosts multiple input channels (e.g. tactile) closely coupled to embedded computing unit and power supply. The embedded computing unit should locally extract meaningful information by employing sophisticated methods. This imposes challenges on real time operation and adds a burden regarding power consumption. Approximate or inexact computing represents a promising solution for energy efficient data processing; it tunes the accuracy of computation on the specific application requirements in order to reduce power consumption. In this paper, inexact arithmetic circuits have been employed to improve the energy efficiency for sensors digital data processing. The proposed inexact circuits achieve up to 80% power saving when compared to the exact one, and similar solutions presented in literature with a maximum loss of 1.39 dB in output SNR when applied to FIR filters.

Electronic skin can be integrated into a prosthetic device to endow the prosthesis with artificial cutaneous sensing, thereby partially restoring the sensory information lost due to an amputation. Non-invasive cutaneous electrostimulation transmits the tactile information sensed by the electronic skin on the prosthetic hand to the human brain, through the amputee's afferent nervous system. In this paper, our current benchtop prototype of a distributed sensing-stimulation system is presented, together with the envisaged high-fidelity solution which will be integrated into a real prosthetic hand.

When IEEE 802.11 access points (APs) share the same channel in a multi-cell WLAN, their downlink transmissions can interfere. Typically, an AP whose scheduled transmission to some user is blocked by another cell will apply the CSMA/CA back-off algorithm and continue to make reattempts to the same user. Through analytical models and simulations, we demonstrate that significant capacity gains can be attained by choosing an alternative destination for the reattempt. Results demonstrate that a simple random choice of alternative destination brings almost the same gain as a more sophisticated algorithm that seeks to maximize spatial reuse.

This paper contributes to the definition of an association policy for a multi-channel, multiple AP WLAN that depends on both physical rate and realised throughput. We show that existing proposals are inefficient when several APs share the same channel and define a new policy that is demonstrated to be close to optimal. Policies are compared through their traffic capacity defined as the network stability limit. We determine this capacity analytically using the fluid limit method for some simple network configurations deriving insight into the structure of the optimal policy. The quasi-optimality of our proposal is verified by simulation of a more complex network configuration.

Transparent persistence promises to integrate programming languages and databases by allowing programs to access persistent data with the same ease as non-persistent data. In this work we demonstrate the feasibility of optimizing transparently persistent programs by extracting queries to efficiently prefetch required data. A static analysis derives query structure and conditions across methods that access persistent data. Using the static analysis, our system transforms the program to execute explicit queries. The transformed program composes queries across methods to handle method calls that return persistent data. We extend an existing Java compiler to implement the static analysis and program transformation, handling recursion and parameterized queries. We evaluate the effectiveness of query extraction on the OO7 and TORPEDO benchmarks. This work is focused on programs written in the current version of Java, without languages changes. However, the techniques developed here may also be of value in conjunction with object-oriented languages extended with high-level query syntax.

Performance of WLANs has been extensively studied during the past few years. While the focus has mostly been on isolated cells, the coverage of WLANs is in practice most often realised through several cells. Cells using the same frequency channel typically interact through the exclusion region enforced by the RTS/CTS mechanism prior to the transmission of any packet.

The performance of modern microprocessors is increasingly limited by their inability to hide main memory latency. The problem is worse in large-scale shared memory systems, where remote memory latencies are hundreds, and soon thousands, of processor cycles. To mitigate this problem, we propose the use of Active Memory Operations (AMOs), in which select operations can be sent to and executed on the home memory controller of data. AMOs can eliminate significant number of coherence messages, minimize intranode and internode memory traffic, and create opportunities for parallelism. Our implementation of AMOs is cache-coherent and requires no changes to the processor core or DRAM chips.

New 3G wireless algorithms require more performance than can be currently provided by embedded processors. ASICs provide the necessary performance but are costly to design and sacrifice generality. This paper introduces a clustered VLIW coprocessor approach that organizes the execution and storage resources differently than a traditional general-purpose processor or DSP. The execution units of the coprocessor are clustered and embedded in a rich set of communication resources. Fine grain control of these resources is imposed by a wide-word horizontal micro-code program. The advantages of this approach are quantified on a suite of six algorithms that are taken from both traditional DSP applications and from the new 3G cellular telephony domain. The result is surprising. The execution clusters retain much of the generality of a conventional processor while simultaneously improving performance by one to two orders of magnitude and by reducing energy-delay by three to four orders of magnitude when compared to a conventional embedded processor such as the Intel XScale.