UTSOI MOS devices enable the scaling of the channel length of MOS Transistors through scaling of the channel thickness and oxide thickness. Additionally, as they have a planar architecture similar to the conventional Bulk MOSFETs, these UTSOI MOSFETs have a simpler fabrication process compared to 3-D Transistors like FinFETs. Also, their performance can be efficiently tuned by applying a bias through the BOX. This allows a significant dynamic modulation of the speed/power trade-off, which is a powerful knob at the circuit level.

An algorithm is presented to determine the number and direction of multiple indistinguishable radio frequency emitters using a single channel circular antenna array. This algorithm combines pseudo-Doppler direction finding with the MUSIC algorithm.  The theory is provided along with a simulation. The simulation shows that the algorithm performs very well, with accuracy of better than one degree for typical scenarios, even with low signal to noise ratios.

This study introduces a groundbreaking idea: using swallowable sensor capsules with augmented reality (AR) to conduct real-time, high-resolution examinations of the human body. These capsules, ingested like pills, transmit data as they pass through the body. Physicians can then use AR headsets to view this data as a 3D model of the organ or tissue being examined, along with sensor readings and a miniature endoscopic view. While there are technical challenges to overcome, this innovation has the potential to revolutionize non-invasive examinations of the digestive tract and internal cavities, improving the diagnosis of conditions like inflammation, infections, and cancers. With further technological development, swallowable sensor capsules with AR integration could become a valuable complement to traditional medical imaging methods, offering dynamic 3D visualizations of the body’s inner workings.

A document by Mohammed Mahyoub . Click on the document to view its contents.

There has been a rapid advancement in developing computational models originating from the governing principles of quantum mechanics. Quantum computing approaches such as variational quantum algorithms (VQAs) and quantum-inspired algorithms have shown advantages across various applications. VQAs have been widely used as hybrid classical-quantum computational frameworks that are well-suited for NISQ advantages. Alternatively, the quantum-inspired evolutionary algorithm (QiEA) provides an efficient search space and parameter optimization solution, useful in handling problems that consist of large parameters. Following the recent trends, quantum-inspired technique, developments, limitations, and future scope are highlighted. Also, an approach towards advancing neuromorphic computation is discussed. Neuromorphic computation and systems are a fast developing domain inherited from fast and massively parallel processing, low energy consumption, high density of millions of neurons in a chip, and have found successful applications across spatio-temporal domain areas including brain data modelling. However, they require a fast and efficient parameter optimization process in large parameter space. Finally, future directions for the development of quantum-enhanced spiking neural networks and neuromorphic systems are outlined.

This paper offers for the first time a novel method for creating Predictive Associative Memories of Time series (PAMeT), where a full set of time series variables is used to create a machine learning predictive model (global learning), and afterward, only a few temporal variables are used at a shorter time to recall the model on new data (local recall). Inspired by human brain processes, PAMeT-SNN leverages the brain-inspired SNN NeuCube and the concept of spatio-temporal associative memories (STAM). PAMeT-SNN is a 4-dimensional spatio-temporal structure. First, it encodes time series data into spike sequences that reflects on the changes in the data over time and then maps the temporal variables into the SNN using temporal similarity to define the spatial locations of the variables in the SNN. It then learns temporal associations between a full set of time series variables, thereby memorizing these temporal associations as spatio-temporal connections between neurons. These connections are activated when only part of the time series data is used to recall the model on new data. The proposed groundbreaking method is exemplified with PAMeT-SNN for predictive modeling on two distinct case study time series data sets: trade dynamics and commodity prices. In these case studies, the method effectively captures the intricate data dynamics, enabling accurate forecasting of future values using a minimal set of variables. The method has the potential to be applied in diverse domains.

A document by Hao Qin . Click on the document to view its contents.

This paper proposes a specialized artificial neural network layer named SimilarNet, designed to compare two feature vectors while considering the positions of their elements to produce an output in a vector format. By leveraging the advantages of the cosine similarity and concatenation layer, SimilarNet can effectively compare two vectors, enabling the construction of trained comparison models using multidimensional activation functions. In addition, SimilarNet can realize 1:1 comparisons of data with different shapes, unlike cosine similarity.

Digital Contact Tracing (DCT) is a great example of information systems assisting societal problems. However, privacy concerns lead to reduced DCT adoption rates. When dealing with critical societal issues, policymakers seek to use various strategies, such as interventions and financial subsidies, to steer the behavors of individuals. This causes individuals to face a sophisticated decision-making process when coping with the public health crisis and the adoption of DCT, i.e., giving up privacy and freedom to gain information to remain healthy. In this paper, we consider a scenario, where policymakers allocate rewards to individuals to motivate their compliance with the interventions; And the individuals decide the optimal compliance effort based on their health state, privacy loss, interaction with their neighbors, and rewards. To tackle the trade-off between a number of individuals and policymakers, in this paper, we propose a Leader-Followers Mean-Field Game model to analyze this time-dependent, dynamic, and large-scale decision-making problem. The numerical results demonstrate that by allocating appropriate rewards, policymakers can play a role in guiding the behavior of individuals in various scenarios.

This paper introduces a semantic communications architecture that is focused on the transmission of american sign language (ASL). Initially, a novel system model for image-based semantic communications is presented, which utilizes a variant of the quadrature amplitude modulation (QAM) scheme, named 24-QAM. This modulation scheme is derived from the original 32-QAM constellation by removing 8 peripheral symbols and is proven capable of attaining superior error performance in ASL applications. Additionally, a semantic encoder based on a convolutional neural network (CNN) which effectively utilizes the ASL alphabet is presented. An original dataset is created by superimposing redgreen- blue landmarks and key-points on top of the captured images; hence, enhancing the representation of hand posture. Finally, the training, testing, and communication performance of the proposed system is quantified through numerical results that highlight the achievable gains and trigger insightful discussions.

Monitoring performance in organizations can act as a catalyst for (i) fostering a culture of continuous improvement over-time, and (ii) achieving their overarching business goals. To this aims, organizations employ Key Performance Indicators (KPIs). However, a common pitfall is the collection of excessive data which is partially disconnected from the business goals. In this study, we propose a KPI framework in the form of a model, an associated template, and a checklist with the aim of providing guidance to practitioners for creating sound KPIs. We examine KPI development and monitoring through four case studies in the context of software systems in two organizations. Through these case studies, we evaluate our KPI framework for its utility in practice and gain insights to uncover related challenges.

Due to the advantage of providing maximum data reliability at a given storage cost, $(k+r,k)$ maximum distance separable (MDS) array codes, in which all data symbols can be obtained by any $k$ of $k+r$ nodes, are extensively used in distributed storage systems. $(k+r,k,\alpha)$ piggybacking codes, which are a particular kind of $(k+r,k)$ MDS array codes, reaches low repair bandwidth for a failed single node and possesses small sub-packetization level $\alpha$. In this paper, by dividing the table formed by some instances of base MDS code into regions and carefully adding the data symbols in some regions as piggybacks to the parity symbols in some other regions, we design a new piggybacking code which has higher repair efficiency than the existing piggybacking codes in repairing each systematic node, where a node is said to be systematic if it keeps  portions of the raw message in reserve without encoding. The sub-packetization level is roughly equal to 2 times the number $r$ of parity nodes when the new piggybacking code attains the highest repair efficiency.

Obtaining large-scale noisy/clean image pairs from the real world to train a denoising model is a difficult and cumbersome task. Self-supervised image denoisers have gained recent attention by adopting blind-spot networks to remove noise from single noisy images. However, the results of blind-spot denoisers suffer from losses of image detail caused by the selection of random masks. Here, we propose an edge-enhanced denoising approach called Self2Grad that compensates for the loss of important details when using self-supervised networks. Specifically, we develop a gradient-regularized loss function, which when added to the overall network loss acts to preserve details in images processed by blind-spot networks. In addition, we show that the loss obtained with Self2Grad is approximately equal to the loss incurred by supervised approaches. When compared to state-of-the-art (SOTA) blind-spot denoisers, Self2Grad delivers superior performance on both synthetic and real-world datasets. Indeed, Self2Grad attains comparable denoising results as methods that operate on multiple images. In particular, Self2Grad is especially effective on images corrupted by high levels of noise.

A document by Weihua Zhou . Click on the document to view its contents.

Binary code analysis and comprehension is critical to applications in reverse engineering and computer security tasks where source code is not available. Unfortunately, unlike source code, binary code lacks semantics and is more difficult for human engineers to understand and analyze. Limited work has explored incorporating multiple program representations. In this paper, we present ContraBin, a contrastive learning technique that integrates source code and comment information along with binaries to create an embedding capable of aiding binary analysis and comprehension tasks. Specifically, we present three components in ContraBin: (1) a primary contrastive learning method for initial pre-training, (2) a simplex interpolation method to integrate source code, comments, and binary code, and (3) an intermediate representation learning algorithm to train a binary code embedding. We evaluate the effectiveness of ContraBin through four indicative downstream tasks related to binary code: algorithmic functionality classification, function name recovery, code summarization, and reverse engineering. The results show that ContraBin considerably improves performance on all four tasks, measured by accuracy, mean of average precision, and BLEU scores as appropriate. ContraBin is the first language representation model to incorporate source code, binary code, and comments into contrastive code representation learning and is intended to contribute to the field of binary code analysis. The dataset used in this study is available for further research.

Digital divide refers to the gap between populations at different socio-economic levels with respect to their access to the Internet and communication technologies in general. In this chapter, we summarize the recent developments aimed at bridging such inequality from a technical and economic standpoint. After a preliminary discussion on alternative backhaul paradigms, we focus on space-, air-, and ground-based communication paradigms, as well as their integration. Subsequently, we discuss the affordability of these solutions, as it is primarily the economic aspect which hinders digital inclusion.

Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used to solve computationally expensive optimization problems. The extreme learning machine-based DirectRanker (ELDR) is a single-layer feed-forward neural network surrogate model designed for SAEAs. ELDR estimates the superiority of two solutions and uses random input weights and biases while learning the output weights. However, ELDR requires high computation time as the problem dimensionality and the number of hidden neurons increase, making its applicability to high-dimensional problems difficult. A surrogate model should be computationally efficient and enable rapid fitness estimations. Therefore, this study proposes a fast implementation technique, i.e., a learning method for ELDR (fELDR) that achieves mathematically equivalent learning results with small computational complexity. Additionally, we propose a pointwise score function to reuse the prediction results. The experimental results confirmed the effectiveness of fELDR compared with the original ELDR. The learning results of the proposed fELDR were equivalent to those of the original ELDR while reducing the training time by up to 97%, especially when using a large hidden layer on a large dimensionality problem. Moreover, owing to the reusable prediction results, the computation time of the fELDR-assisted SAEA can be further decreased by 79.5% compared with that of the original ELDR-assisted SAEA. The reduced training time and reusable prediction results of fELDR make it feasible to apply ELDR to high-dimensional optimization problems and use high prediction accuracy with a large number of hidden neurons.

The isolated AC-AC/DC-AC/AC-DC matrix converter (MC) is an innovative topology with several benefits including minimum current harmonics, nearly unity power factor, four quadrant (4Q) operation, and bidirectional power flow, making it an ideal choice for energy conversion techniques. Unlike any other AC-AC/DC-AC/AC-DC topologies, the MC topology necessitates electrical medium-frequency (MF)/high-frequency (HF) isolation in order to offer single-stage power conversion. MF/HF power conversion topology has numerous advantages such as high-power density, reduced weight, and low noise while preserving converter’s efficiency, cost, and reliability. This paper aims to report an extensive analysis and classification of different conventional isolated single-stage AC-AC converter, DC-AC inverter, and AC-DC rectifier topologies along with their modified system architecture. In this paper, the AC-AC/DC-AC/AC-DC topologies are classified based on their system architecture, AC-side or DC-side drive techniques, and other subvariants. Our primary objective of this study is to impart a clear understanding of the overarching framework and principles of the isolated AC-AC/DC-AC/AC-DC MC topologies and stimulate the creation of new topologies that cater to specific grid-interconnection requirement.