arXiv preprints from January 1, 2026 through September 11, 2026 — 16:45:54 EST
This paper investigates a massive multiple-input multiple-output (mMIMO) orthogonal frequency-division multiplexing (OFDM) framework for integrated sensing and communication (ISAC) with inverse synthetic aperture radar (ISAR) imaging, supporting applications such as the Internet of Things (IoT). A dual-function architecture combines...
Hydraulic soft fingers offer compliant and gentle manipulation, but their practical deployment is limited by bulky fluidic hardware, fabrication complexity, and insufficient design validation. This paper presents a compact 3D-printed soft hydraulic finger driven by a miniature cyclic peristaltic loop. The finger integrates compliant...
In wireless powered communication networks, medium access control protocols for devices using the harvest-then-transmit strategy must be distributed, low-overhead, and capable of handling irregular and infrequent data transmissions to ensure efficient energy utilisation. However, most existing protocols fail to meet one or more of...
Electrochemical impedance spectroscopy (EIS) is a valuable tool for non-invasive battery characterisation as it provides a compact data representation of physical processes over a wide range of time scales. Commonly, sinusoids at different frequencies are injected sequentially (single-sines). Alternatively, a multisine excitation (a...
A collective tourism brand is jointly produced by assets managed by different custodians, so destination managers must decide which assets require collective protection. We model a destination as a gate times a menu. The gate combines essential inputs and non-reproducible anchors as a weaker-link public good; the menu combines many...
In autonomous driving development, a perception dataset is crucial, as it provides fundamental data for training, testing, and validating algorithms for an autonomous vehicle's multimodal perception systems. So far, most research has concentrated on providing datasets for well-structured urban environments. This work introduces the...
Non-orthogonal multiple access (NOMA) detection using successive interference cancellation (SIC) and joint multiuser maximum likelihood (JML) has been extensively studied, although their presumed optimality for individual-user bit error rate (BER) minimization is unestablished. Moreover, existing BER analyses overlook the SIC-induced...
We derive necessary and sufficient nonparametric conditions for several models of mental accounting. The paper characterises pure mental accounting, separable accounts, and labelled income, and compares these boundedly rational models with two rational multi-stage budgeting benchmarks. The resulting Afriat-style conditions make the...
Background: Retinal fundus imaging is central to the early diagnosis of sight-threatening conditions including diabetic retinopathy, glaucoma, and retinal vein occlusion. Clinical utility of fundus images is routinely compromised by non-uniform illumination, motion blur, and low contrast - artefacts that increase the risk of...
This paper investigates the statistical ambiguity functions (AFs) of orthogonal frequency division multiplexing (OFDM) waveforms that incorporate deterministic unit-modulus pilot symbols and random data payloads for integrated sensing and communication (ISAC). We derive analytical expressions for the mean squared discrete periodic...
Multi-band integrated sensing and communication (ISAC) offers complementary high- and low-frequency echo information for multi-target sensing. However, existing dual-band ISAC sensing methods have a limited ability to exploit deep complementary information across heterogeneous bands and often incur high computational costs. To address...
Over the past decade, governments around the world have made significant investments in creating elderly-friendly urban environments within local neighborhoods. However, the lack of a standardized evaluation framework for Ageing-in-Place (AIP) practices makes it challenging to generalize these experiences. First, we compare the AIP...
This work addresses the need for engineering-grounded evaluation of implement-ed tariff mechanisms in distribution-level energy systems. A digital twin-based method is proposed for assessing local collective tariffs under realistic behavioral and infrastructural conditions. The approach integrates agent-based modeling of household...
We propose a reinforcement learning (RL) based optimal distributed control algorithm for the multi-agent systems (MASs) with stochastic uncertainties. Unlike existing methods, during the optimized backstepping design process, we use the actor-critic-identifier structure. The actor neural network is used to reflect control behavior,...
This study investigates the impact of social integration on immigrants' housing behaviours from a temporal perspective, using Singapore's differential public housing policies on immigrants as a quasi-natural experiment. With the support of a local town council, we conducted a survey on social integration among 1,128 immigrant and...
Simultaneous acoustic information and power transfer (SAIPT) is a promising technique for supporting self-sustainable Internet of Underwater Things (IoUT) networks through concurrent data transmission and energy supplement. However, existing OFDM-based SAIPT studies are vulnerable to severe multipath propagation and Doppler effects in...
Instrumental-variables estimation increasingly pools many or high-dimensional instruments into a single machine-learned first stage, with rich controls partialled out. The resulting estimand, the partialled-out IV coefficient built from any signal of the instruments, is a signal-weighted average of the heterogeneous effects, which...
This paper proposes a robust nonparametric hypothesis test for the existence of heterogeneous treatment effects. We focus on the variance of the Conditional Average Treatment Effect (CATE) as a natural omnibus parameter, where a non-zero variance implies the presence of relevant heterogeneity. Standard inference for this parameter...
Unstructured data, such as images and text, are increasingly used in empirical economics. Since training machine-learning models on unstructured data is costly, economists often use off-the-shelf pre-trained deep learning models developed by computer scientists to extract embeddings, which are then used as covariates in target...
The softmax policy $π(a \mid s) \propto \exp(βQ(s,a))$ is the default model of stochastic choice in reinforcement learning (RL). Various justifications based on robustness, exploration, and optimization have been offered in the RL literature, but none uniquely derives the softmax form from first principles. This leaves a basic tension...
Standard pre-tests of normality on reduced-form innovations are insufficient to detect two or more Gaussian shocks and hence, the failure of identification in non-Gaussian SVARs. We instead propose a bootstrap-based approach to evaluate the asymptotic validity of this condition by measuring the divergence between the conditional...
This paper studies transformations that increase efficiency in solving equilibrium systems without information loss. Our approach exploits order-theoretic structure commonly found in economic problems to obtain conditions under which high-dimensional systems can be transformed into low-dimensional systems while preserving exact...
This paper develops a synthetic control estimator for compositional outcomes, vectors of shares generated by an underlying categorical process. Derived from a random utility model with interactive fixed effects on relative systematic utilities, the estimator maps compositions to log-odds, where the standard convex hull condition...
Experimenters often run pilots, but how much a small pilot should shape the main-wave design has no settled answer. This paper shows how noisy pilot evidence should guide treatment assignment probabilities in two-wave experiments. Two canonical rules mark the extremes. Balanced assignment guards against worst cases but ignores...
We study the identification of belief distributions in a population of Bayesian agents from anonymous aggregate belief data. While a single Bayesian agent's full belief can be recovered from beliefs over a suitable collection of binary events, this principle need not extend to populations: event-by-event distributions of beliefs may...