This paper introduces a wireless electro- chemical sensing system for in vivo small-molecule sensing using reagent-less structure-switching aptamers. The system consists of a 65-nm CMOS electrochemical sensing circuit with an on-chip waveform generator, a Bluetooth microcontroller (MCU), and a battery, which allow for robust wireless recording in a freely moving animal. The device can perform data acquisition every 10 seconds, including wireless data transfer, and consumes an average current of 3mA after duty cycling. Multiple steps in the data processing for noise reduction are discussed. Real-time sensing of infused kanamycin concentration in the interstitial fluids (ISF) using “wire” electrodes is demonstrated on a freely moving rat with the device implanted under the skin. The paper also compares the results at different sensing locations and identifies the difference in their diffusion rates. Finally, the authors suggest that the technology can be adapted for monitoring other molecules using DNA aptamers of different sequences.

Crowds are considered trivial by the community because they feel they have implemented health protocols by wearing masks. Crowds must be minimized so that the spread of the Covid-19 virus does not get higher. This paper aims to plan a UAV shuttle route so that it can approach as many locations as possible with potential crowding while simultaneously leading to the destination of the flight route without having to go around first. The method used is a comparison of Greedy algorithms and Dynamic Programs in determining the most effective route. The flight simulation was carried out using Software in The Loop (SITL) and ArduPilot Mission Planner. The results obtained are that the Dynamic Program can visit 14 locations out of 18 existing location choices, whereas with the Greedy algorithm approach, UAV can only visit 8 locations out of 18 existing location choices. The conclusion is that the Dynamic Program is able to maximize routes so that more locations are visited by UAVs and certainly better than the Greedy Algorithm.

This is a pre print version of our work on noise power and area optimization of neural amplifiers. We have introduced a figure of merit to quantify the noise and area optimization.

Everyone now has internet and social media access, making it simple to get information and news. On the other hand, there are fake news articles, also. It not only makes difficult for the public to find their truthfulness but also misleads them. Consequently, developing intelligent systems for separating news is critical. In this paper, four deep learning techniques such as Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks-BiLSTM (CNN-BiLSTM) are used to detect fake news. We trained these models on three large datasets, namely, CoAID, GossipCop, and PolitiFact, that include fake news from social media and news articles. The text is divided into two categories: fake and real. We also trained and compared the results with ten well-known machine-learning classifiers, e.g., Naive Bayes, Decision Tree, Support Vector Machine, K-Nearest Neighbors Classifier, etc. The experimental results indicate that the deep learning-based BERT method achieves better accuracy and F1-score of 91.94\% and 92.06\% for PolitiFact, 99.09\% and 98.38\% for CoAID, 85.59\% and 82.40\% for GossipCop, respectively. These results suggest that deep learning algorithms provide favorably accurate results and outperform machine learning models in terms of accuracy.

Abstract-Using wearable robotics to modulate step width in  normal walking for enhanced mediolateral balance has not been  demonstrated in the field. We designed a bilateral hip exoskeleton  with admittance control to power hip abduction and adduction to  modulate step width. Objective: As the first step to show its potential, the objective of  this study was to investigate how human’s step width reacted to  hip exoskeleton’s admittance control parameter changes during  walking. Methods: Ten non-disabled individuals walked on a treadmill at  a self-selected speed, while wearing our bilateral robotic hip  exoskeleton. We used two equilibrium positions to define the  direction of assistance. We studied the influence of multiple  stiffness values in the admittance control on the participants’ step  width, step length, and electromyographic (EMG) activity of the  gluteus medius. Results: Step width were significantly modulated by the change  of stiffness in exoskeleton control, while step length did not show  significant changes. When the stiffness changed from zero to our  studied stiffness values, the participants’ step width started to  modulate immediately. Within 4 consecutive heel strikes right  after a stiffness change, the step width showed a significant change.  Interestingly, EMG activity of the gluteus medius did not change  significantly regardless the applied stiffness and powered  direction. Conclusion: Tuning of stiffness in admittance control of a hip  exoskeleton, acting in mediolateral direction, can be a viable way for controlling step width in normal walking. Unvaried gluteus  medius activity indicates that the increase in step width were  mainly caused by the assistive torque applied by the exoskeleton. Significance: Our study results pave a new way for future  design and control of wearable robotics in enhancing mediolateral  walking balance for various rehabilitation applications.

Explainability is becoming increasingly crucial in machine learning studies and, as the complexity of the model increases, so does the complexity of its explanation. However, the higher the complexity of the problem the higher the amount of information it may provide, and this information can be exploited to generate a more precise explanation of how the model works. One of the most valuable ways to recover such relation between input and output is to extract counterfactual explanations. In binary classification, counterfactuals allow us to find minimal changes from an observation to another one belonging to the opposite class. But how do counterfactuals work in multi-class problems? In this article, we propose a novel methodology to extract multiple counterfactual explanations (MUCH, MUlti Counterfactual via Halton sampling) from an original Multi-Class Support Vector Data Description algorithm (MC-SVDD). To evaluate the performance of the proposed method, we extracted a set of counterfactual explanations from three state-of-the-art datasets achieving satisfactory results that pave the way to a range of real-world applications, for example disease prevention.

Tactile perception has been a hot topic of research in robotics. Robots sense the shape, material, distributed force, slip during contact, and use the multi-modal contact information to control grasping and manipulation. For vision-based tactile sensors, the contact representation and extraction determine the quality of the raw tactile information, and therefore serve a significant role in the robot perception system. This article highlights for the first time the importance of raw representation and extraction in visuotactile perception, and proposes a new multicolor CMP method for enhancing the performance of vision-based tactile sensors. Based on the principle of continuous marker pattern (CMP), the multicolor CMP method is optimized in the pattern and algorithm design. Regarding information representation, we present a new type of marker pattern based on RGB triangles and a preferred layout. In terms of information extraction, we propose a series of extraction strategies with the adaptive growing algorithm (AGA) and the spin-search algorithm (SSA) as the cores. The experiments reveal that the multicolor CMP method achieves improved precision and reliability compared to the former CMP method.

Representation learning considering high-order relationships in data has recently shown to be advantageous in many applications. The construction of a meaningful hypergraph plays a crucial role in the success of hypergraph-based representation learning methods, which is particularly useful in hypergraph neural networks and hypergraph signal processing. However, a meaningful hypergraph may only be available in specific cases. This paper addresses the challenge of learning the underlying hypergraph topology from the data itself. As in graph signal processing applications, we consider the case in which the data possesses certain regularity or smoothness on the hypergraph. To this end, our method builds on the novel tensor-based hypergraph signal processing framework (t-HGSP) that has recently emerged as a powerful tool for preserving the intrinsic high-order structure of data on hypergraphs. Given the hypergraph spectrum and frequency coefficient definitions within the t-HGSP framework, we propose a method to learn the hypergraph Laplacian from data by minimizing the total variation on the hypergraph (TVL-HGSP). Additionally, we introduce an alternative approach  (PDL-HGSP) that improves the connectivity of the learned hypergraph without compromising sparsity and use  primal-dual-based algorithms to reduce the computational complexity. Finally, we combine the proposed learning algorithms with novel tensor-based hypergraph convolutional neural networks to propose hypergraph learning-convolutional neural networks (t-HyperGLNN).

Accepted for full oral presentation at the 16th Conference on Artificial General Intelligence, taking place in Stockholm, 2023. We integrate foundational theories of meaning with a mathematical formalism of artificial general intelligence (AGI) to offer a comprehensive mechanistic explanation of meaning, communication, and symbol emergence. This synthesis holds significance for both AGI and broader debates concerning the nature of language, as it unifies pragmatics, logical truth conditional semantics, Peircean semiotics, and a computable model of enactive cognition, addressing phenomena that have traditionally evaded mechanistic explanation. By examining the conditions under which a machine can generate meaningful utterances or comprehend human meaning, we establish that the current generation of language models do not possess the same understanding of meaning as humans nor intend any meaning that we might attribute to their responses. To address this, we propose simulating human feelings and optimising models to construct weak representations. Our findings shed light on the relationship between meaning and intelligence, and how we can build machines that comprehend and intend meaning.

Ferritin is a 12 nanometer (nm) diameter iron storage protein complex that is found in most plants and animals. A substantial body of evidence has established that electrons can tunnel through and between ferritin protein nanoparticles and that it exhibits Coulomb blockade behavior, similar to quantum dots and nanoparticles. This evidence can be used to understand the behavior of these particles for use in nanoelectronic devices, for biomedical applications and for investigation of quantum biological phenomena. Ferritin also has magnetic properties that make it useful for applications such as memristors and as a contrast agent for magnetic resonance imaging. This article provides a short overview of this evidence, as well as evidence of ferritin structures in vivo and of tunneling in those structures, with an emphasis on ferritin structures in substantia nigra pars compacta (SNc) neurons. Potential biomedical applications that could utilize these ferritin protein nanoparticles are also discussed.

A document by Hossam Elbakry . Click on the document to view its contents.

Nearly 15% of the global population is affected by disabilities impacting mobility. Monitoring foot pressure distribution during gait is a fundamental aspect of evaluating rehabilitation. Wearable systems provide a portable alternative to stationary equipment monitoring gait without laboratory space limitations. However, wearable sensors in some applications present challenges in the calibration, sensitivity, and human-sensor interface, requiring application-specific sensors. This study aimed to develop wearable sensors where the structural and material properties can characterise the sensitivity and range of measurement during the design phase. We developed wearable piezoresistive sensors using additive manufacturing to create mechanical metamaterials with embedded pressure-sensing capabilities. The sensors were fabricated in TPU using SLS and graphene ink infusion processes. Three structural designs were developed for different measuring ranges (0 – 50 N, 0 – 100 N, and 0 – 150 N) using body-centred cubic lattices constructed via pyramid unit cells. Two graphene infusion processes were evaluated. We tested the sensors’ mechanical and piezoresistive behaviour, measuring the compressive force, strain, and electrical resistance across the sensor. We analysed the influence of structural dimensions and the infusion process on the piezoresistive behaviour. The measuring range was affected mainly by tuneable structural dimensions. The infusion process influenced the piezoresistive sensitivity and affected the linearity response. The results indicate the characterisation of the sensitivity of piezoresistive sensors based on structural parameters and material properties. Mechanical metamaterials could embed pressure sensing in wearables, allowing for customisation based on design parameters using additive manufacture and graphene inks.

Efficient time-interleaved analog-to-digital converters (ADCs) that operate at high sample rates with wide input bandwidths are necessary to support increasing wireline transceiver data rates. This paper presents a 7-bit 38GS/s 32-way time-interleaved ADC that utilizes an 8-way interleaver architecture based on a speed-enhanced bootstrapped switch that increases input bandwidth. ADC sample rate and efficiency is improved with pipelined successive approximation register (SAR) unit ADCs that employ an output level shifting (OLS) settling technique in the dynamic residue amplifier to achieve settling in only 33% of the time required for a conventional current-mode logic (CML) amplifier. Using parallel comparators in the two 4-bit asynchronous pipeline stages allows for further improvements in ADC conversion speed. Fabricated in 22nm FinFET, the proposed ADC occupies 0.107mm2 area. Operating at 38GS/s, the ADC achieves 41.9fJ/conv.-step with low input frequencies, 64.1fJ/conv.-step at Nyquist, and has 20GHz 3dB input bandwidth.

We consider the problem of finding an array of antennas that provides a desired distribution of signal strength in an urban environment. Our approach is based on a discretized Maxwell equation for the electromagnetic field. This equation can be solved numerically and provides training samples for a deep learning algorithm. Finally, the deep learning algorithm, based on a multi layer perceptron, is used to retrieve antenna arrays for a desired signal distribution. In our example we obtain for the retrieval a mean success rate of 90%.

Cellular networks are moving to fully automated schemes to minimize human intervention and maximize efficiency. This is what is called the Zero Touch networks paradigm. Until now, such management has been based on Drive Test results together with network-side metrics. However, this approach is clearly insufficient to achieve Zero Touch due to the complexity and high cost of collecting user side data in an increasingly vast and heterogeneous infrastructure. In this regard, the present work describes a novel framework for Zero Touch cellular networks that exploits the advantages of integrating User Equipment metrics. The suitability of the proposal has bee verified using data acquired from a commercial mobile network, achieving promising results through the use of traditional as well as User Equipment side metrics.

The next generation of Internet services, such as Metaverse, rely on mixed reality (MR) technology to provide immersive user experiences. However, limited computation power of MR headset-mounted devices (HMDs) hinders the deployment of such services. Therefore, we propose an efficient informationsharing scheme based on full-duplex device-to-device (D2D) semantic communications to address this issue. Our approach enables users to avoid heavy and repetitive computational tasks, such as artificial intelligence-generated content (AIGC) in the view images of all MR users. Specifically, a user can transmit the generated content and semantic information extracted from their view image to nearby users, who can then use this information to obtain the spatial matching of computation results under their view images. We analyze the performance of full-duplex D2D communications, including the achievable rate and bit error probability, by using generalized small-scale fading models. To facilitate semantic information sharing among users, we design a contract theoretic AI-generated incentive mechanism. The proposed diffusion model generates the optimal contract design, outperforming two deep reinforcement learning algorithms, i.e., proximal policy optimization and soft actor-critic algorithms. Our numerical analysis experiment proves the effectiveness of our proposed methods.

The popularity of cryptocurrencies has grown significantly in recent years, and they have become an important asset for internet trading. One of the main drawbacks of cryptocurrencies is the high volatility and fluctuation in value. The value of cryptocurrencies can change rapidly and dramatically, making them a risky investment. Cryptocurrencies are largely unregulated, which can exacerbate their volatility. The high volatility of cryptocurrencies has also led to a speculative bubble, with many investors buying and selling cryptocurrencies based on short-term price fluctuations rather than their underlying values. Therefore, how to reduce the fluctuation risk introduced by exchanges, transform uncertain prices to deterministic value, and promote the benefits of decentralized finance are critical for the future development of cryptos and Web 3.0. To address the issues, this paper proposes a novel theory as Automatic Increase Market Systems (AIMS) for cryptos, which could potentially be designed to automatically adjust the value of a cryptocurrency helping to stabilize the price and increase its value over time in a deterministic manner. We build a crypto, WISH (https://wishbank.wtf),  based on AIMS in order to demonstrate how the automatic increase market system would work in practice, and how it would influence the supply of the cryptocurrency in response to market demand and finally make itself to be a stable medium of exchange, ensuring that the AIMS is fair and transparent.

Understanding the success of augmentative exoskeletons is critical to their impact on society. Many exoskeletons are developed to address impairments stemming from neuromotor pathology, which  provides guidance for understanding their abilities; however, for augmentative exoskeletons that  assist able-bodied users, a clear metric of success remains an open question. In this study, we leverage  an approach from economics–the Vickrey second-price auction–to quantify the economic value  added by lower-limb exoskeletons during uphill walking. In our protocol, participants describe the  monetary compensation needed to continue walking uphill for consecutive bouts of two minutes. To incentivize truthful descriptions of participant’s “price to walk,” their compensation amounts were  provided as bids in Vickrey auctions before each bout. We compared these data across different  conditions to determine the marginal value of the exoskeleton weight, assistance, and weight +  assistance. Our approach found that the total value of the exoskeleton and its assistance was modest  ($3.40/hr, SD: $3.35/hr); while most participants found the assistance itself valuable ($19.76/hr, SD:  $19.43/hr), this value was nearly offset by the cost of wearing the exoskeleton weight (-$18.59/hr, SD:  $18.28/hr). Our approach and results demonstrate that economic value can be a powerful tool to  develop exoskeletons that maximize user benefit.