Abstract

In this work, we describe the creation of our machine- learning-based solution for coma prognosis after cardiac arrest using longitudinal EEG and ECG recordings for the ”Predicting Neurological Recovery from Coma After Car- diac Arrest: The George B. Moody PhysioNet Challenge 2023”. Our team, “ComaToast”, had its best submission ranked 28 out of 36 teams selected worldwide, with a chal- lenge score of 0.381 on the official leaderboard for the hid- den test set. We use a combination of age and signal fea- tures from EEG and ECG recordings. Frequency domain features, specifically mean power spectral density from 4 different bands of frequencies (Delta, Theta, Alpha and Beta) and mean Burst Suppression Ratio, were extracted from pre-processed EEG recordings from the first and last available recording for a given patient. Features like mean and standard deviations were extracted along channels for ECG recordings. After imputing missing values, these fea- tures are fed to an XGBoost classifier for the final binary classification of the outcome prediction task. The features are fed to a random forest regressor to predict the CPC outcome for every patient. A solution like ours, which uses a simple model and training technique, may be more viable than deep-learning solutions in general use cases. In our final model, our approach achieved a 5-fold cross- validation score of 0.34 on the public train set.

Abstract

This paper aims to address the limitations of current water level monitoring systems, which are bulky, expensive, and difficult to maintain, resulting in limited deployment. To overcome these challenges, the study utilises Internet of Things (IoT)-enabled low-cost sensor nodes for water level monitoring. In this study, an ultrasonic sensor-based water level node is developed to send data to the cloud through GPRS (2G). Five such nodes were deployed to monitor water levels in overhead tanks and sumps on the campus of IIIT Hyderabad, India. The collected water level data were analysed for behavioural patterns and detecting faulty float switches. In addition, a deep learning algorithm was employed, which can predict future water needs. Thus, the proposed IoT-based smart water level meter offers a more accessible and cost-effective approach to water level monitoring.

Abstract

This paper presents a novel approach to estimate water usage without directly interfacing with the water line. Instead, it utilizes a device that indirectly gauges water consumption by monitoring the position of the tap handle after it is rotated or moved. The setup process for this device is straightforward, as it doesn’t require any modifications to the existing water infrastructure. The system, known as ”Sensible Flow,” employs a sensor node consisting of an inertial Measurement Unit (IMU) to track and analyze the movement and position of the tap handle. To ensure accurate readings, the device is initially calibrated using a flow rate sensor or through manual measurement with a known-volume beaker. Once the flow rate is inferred based on the tap handle position, the data is transmitted via Bluetooth Low Energy (BLE) to a base gateway unit. This base unit provides real-time updates on water consumption and can also be configured to send the collected data to the cloud via WiFi, enabling further analysis and monitoring. Extensive testing has been conducted to evaluate the device’s accuracy and performance. The Sensible Flow system provides a cost-effective and non-intrusive solution for estimating water usage, with potential applications in water conservation and management.

Abstract

This paper proposes a low-power relaxation oscillator for low-power high-temperature IoT systems. It generates a 959 Hz clock signal from −40 to 170°C, consuming 6.75 nW at 0.65 V. A proposed switch-leakage compensation scheme nullifies the effects of body diode and subthreshold leakages on oscillator output frequency at high temperatures, thereby obtaining a wide operating temperature range. The oscillator implemented in a 180 nm CMOS process achieves a temperature coefficient of 40 ppm/°C from −40 to 170 °C at 0.65 V and a line sensitivity of 0.5 %/V from 0.65 to 2.4 V at room temperature, in simulation. Compared with state-of-the-art sub- μW oscillators, this circuit obtains the highest operating temperature and the maximum temperature range.

Abstract

We identify a family of binary codes whose structure is similar to Reed-Muller (RM) codes and which include RM codes as a strict subclass. The codes in this family are denoted as Cn(r,m) , and their duals are denoted as Bn(r,m) . The length of these codes is nm , where n≥2 , and r is their ‘order’. When n=2 , Cn(r,m) is the RM code of order r and length 2m . The special case of these codes corresponding to n being an odd prime was studied by Berman (1967) and Blackmore and Norton (2001). Following the terminology introduced by Blackmore and Norton, we refer to Bn(r,m) as the Berman code and Cn(r,m) as the dual Berman code. We identify these codes using a recursive Plotkin-like construction, and we show that these codes have a rich automorphism group, they are generated by the minimum weight codewords, and that they can be decoded up to half the minimum distance efficiently. Using a result of Kumar et al. (2016), we show that these codes achieve the capacity of the binary erasure channel (BEC) under bit-MAP decoding. Furthermore, except double transitivity, they satisfy all the code properties used by Reeves and Pfister to show that RM codes achieve the capacity of binary-input memoryless symmetric channels. Finally, when n is odd, we identify a large class of abelian codes that includes Bn(r,m) and Cn(r,m) and which achieves BEC capacity.

Abstract

This paper presents a novel algorithm for a swarm of unmanned aerial vehicles to search for an unknown source. The proposed method is inspired by the well-known particle swarm optimization (PSO) algorithm and is called accelerationbased particle swarm optimization (APSO) to address the sourceseeking problem with no a priori information. Unlike the conventional particle swarm optimization algorithm, where the particle velocity is updated based on the self-cognition and socialcognition information, here the update is performed on the particle acceleration. A theoretical analysis is provided, showing the stability and convergence of the proposed acceleration-based particle swarm optimization algorithm. Conditions on the parameters of the resulting third-order update equations are obtained using Jury’s stability test. High-fidelity simulations performed in CoppeliaSim, show the improved performance of the proposed acceleration-based particle swarm optimization algorithm for searching an unknown source when compared with the state-ofthe-art particle swarm-based source-seeking algorithms. From the obtained results, it is observed that the proposed method performs better than the existing methods under scenarios like different inter unmanned aerial vehicle communication network topologies, varying numbers of unmanned aerial vehicles in the swarm, different sizes of search regions, restricted source movement, and in the presence of measurements noise.

Abstract

Humans have a natural ability to perform semantic associations with the surrounding objects in the environment. This allows them to create a mental map of the environment, allowing them to navigate on-demand when given linguistic instructions. A natural goal in Vision Language Navigation (VLN) research is to impart autonomous agents with similar capabilities. Recent works take a step towards this goal by creating a semantic spatial map representation of the environment without any labeled data. However, their representations are limited for practical applicability as they do not distinguish between different instances of the same object. In this work, we address this limitation by integrating instance-level information into spatial map representation using a community detection algorithm and utilizing word ontology learned by large language models (LLMs) to perform open-set semantic associations in the mapping representation. The resulting map representation improves the navigation performance by two-fold (233%) on realistic language commands with instance-specific descriptions compared to the baseline. We validate the practicality and effectiveness of our approach through extensive qualitative and quantitative experiments.

Abstract

Positive phase coupling plays an attractive role in inducing in-phase synchrony in an ensemble of phase oscillators. Positive coupling involving both amplitude and phase continues to be attractive, leading to complete synchrony in identical oscillators (limit cycle or chaotic) or phase coherence in oscillators with heterogeneity of parameters. In contrast, purely positive phase velocity coupling may originate a repulsive effect on pendulumlike oscillators (with rotational motion) to bring them into a state of diametrically opposite phases or a splay state. Negative phase velocity coupling is necessary to induce synchrony or coherence in the general sense. The contrarian roles of phase coupling and phase velocity coupling on the synchrony of networks of second-order phase oscillators have been explored here. We explain our proposition using networks of two model systems, a second-order phase oscillator representing the pendulum or the superconducting Josephson junction dynamics, and a voltage-controlled oscillations in neurons model. Numerical as well as semianalytical approaches are used to confirm our result

Abstract

Achieving perfect synchronization in a complex network, specially in the presence of higher-order interactions (HOIs) at a targeted point in the parameter space, is an interesting, yet challenging task. Here we present a theoretical framework to achieve the same under the paradigm of the Sakaguchi-Kuramoto (SK) model. We analytically derive a frequency set to achieve perfect synchrony at some desired point in a complex network of SK oscillators with higher-order interactions. Considering the SK model with HOIs on top of the scale-free, random, and small world networks, we perform extensive numerical simulations to verify the proposed theory. Numerical simulations show that the analytically derived frequency set not only provides stable perfect synchronization in the network at a desired point but also proves to be very effective in achieving a high level of synchronization around it compared to the other choices of frequency sets. The stability and the robustness of the perfect synchronization state of the system are determined using the low-dimensional reduction of the network and by introducing a Gaussian noise around the derived frequency set, respectively.

Abstract

Symmetries in a network regulate its organization into functional clustered states. Given a generic ensemble of nodes and a desirable cluster (or group of clusters), we exploit the direct connection between the elements of the eigenvector centrality and the graph symmetries to generate a network equipped with the desired cluster(s), with such a synthetical structure being furthermore perfectly reflected in the modular organization of the network's functioning. Our results solve a relevant problem of designing a desired set of clusters and are of generic application in all cases where a desired parallel functioning needs to be blueprinted.