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Machine Learning (ML)-powered apps are used in pervasive devices such as phones, tablets, smartwatches and IoT devices. Recent advances in collaborative, distributed ML such as Federated Learning (FL) attempt to solve privacy concerns of users and data ...

Intelligent transportation systems are predicted to change the way people live in the foreseeable future. Vehicular networks are one of the key enablers for such systems, yet no status-quo solutions of vehicular networks make practical deployments ...

We introduce the concept of semantic fast-forwarding of video streams for efficient labeling of training data for activity recognition. We show that this concept can be realized by combining deep learning within individual frames, with spatial and ...

Arm posture tracking is essential for many applications, such as gesture recognition, fitness training, and motion-based controls. Smartwatches with Inertial Measurement Unit (IMU) sensors (i.e., accelerometer, gyroscope, and magnetometer) provide a ...

Low-density parity-check (LDPC) codes have been widely used for Forward Error Correction (FEC) in wireless networks because they can approach the capacity of wireless links with lightweight encoding complexity. Although LoRa networks have been developed ...

Federated learning (FL) has attracted increasing attention as a promising technique to drive a vast number of edge devices with artificial intelligence. However, it is very challenging to guarantee the efficiency of a FL system in practice due to the ...

Cyber-physical systems are starting to adopt neural network (NN) models for a variety of smart sensing applications. While several efforts seek better NN architectures for system performance improvement, few attempts have been made to study the ...

Mobile cloud offloading is indispensable for inference tasks based on large-scale deep models. However, transmitting privacy-rich inference data to the cloud incurs concerns. This paper presents the design of a system called PriMask, in which the mobile ...

In recent years, Deep Neural Network (DNN) has been increasingly adopted by a wide range of time-critical applications running on edge platforms with heterogeneous multiprocessors. To meet the stringent timing requirements of these applications, ...

The integration of deep learning on Speaker Recognition (SR) advances its development and wide deployment, but also introduces the emerging threat of adversarial examples. However, only a few existing studies investigate its practical threat in physical ...

Falls are one of the leading causes of death in the elderly people aged 65 and above. In order to prevent death by sending prompt fall detection alarms, non-invasive radio-frequency (RF) based fall detection has attracted significant attention, due to ...

Tsetlin Machine (TM) is a new machine learning algorithm that encodes propositional logic into learning automata---a set of logical expressions composed of boolean input features---to recognise patterns. The simplicity, efficiency, and accuracy of this ...

Indoor self-localization is a highly demanded system function for smartphones. The current solutions based on inertial, radio frequency, and geomagnetic sensing may have degraded performance when their limiting factors take effect. In this paper, we ...

Federated learning (FL) enables distributed mobile devices to collaboratively learn a shared model without exposing their raw data. However, heterogeneous devices usually have limited and different available resources, i.e., system heterogeneity, for ...

Quality of Experience (QoE) assessment is a long-lasting but yet-to-be-resolved task. Existing approaches, especially for conversational voice services, are restricted to leveraging network-centric parameters. However, their performances are hardly ...

Edge computing is being widely used for video analytics. To alleviate the inherent tension between accuracy and cost, various video analytics pipelines have been proposed to optimize the usage of GPU on edge nodes. Nonetheless, we find that GPU compute ...

Intelligent continuous monitoring of an IoT system to identify the operational changes, encompassing both normal and abnormal scenarios, with drift in sensing device is a challenging problem. It demands capability of learning continuously with multiple ...

In Internet of Things (IoT) scenarios such as smart homes, autonomous vehicles, and wearable devices, data pattern changes over time due to changing environments and user requirements, known as domain shifts. When encountering domain shifts, deep neural ...

Due to the inter-subject variability of Electroencephalogram(EEG) signals, a long calibration time is required to collect a large number of labeled trials to calibrate classifier parameters before using the Brain-computer Interface(BCI). This challenge ...

Pedestrian trajectory prediction is an important module in autonomous vehicles (AVs) to ensure safe and effective motion planning. Recently, many deep learning algorithms that achieve near real-time trajectory predictions have been developed. However, ...