Miracle-dz/UniFAR-FTUs huggingface dataset:
Super Fast Link Set-Up in Wi-Fi HaLow Networks

In Wi-Fi HaLow networks, sensors can transmit data only after the link set-up procedure (LSP). After a power outage or when a swarm of sensors appears in an Internet of Things network, all of them contend for the channel to set up links with the access point. For thousands of sensors the LSP can last for hours. However, it can be shortened with advanced LSP coordination algorithms. We show that the best standard-compatible solution found in the literature leaves a gap between the achievable link set-up time and the estimated lower bound. In this paper, we design and evaluate an adaptive solution to control the LSP that fills in this room for improvement and provides significant gains against the existing algorithms.
Empirical Evaluation on the Impact of Class Overlap for EEG-Based Early Epileptic Seizure Detection

Important physiological information is hidden in electroencephalography (EEG), which can reflect the human brain’s activity. EEG, which is a kind of complicated signal, can be used for epileptic seizure detection and epilepsy diagnosis via machine learning. A large amount of effort, including raw signal preprocessing and data preprocessing for machine learning, is required for constructing high-quality training datasets because the classification performance highly depends on high-quality data. Feature extraction has been widely used in EEG-based early epileptic seizure detection. Due to the complexity of data collection and labeling, some of the training instances are inevitably mislabeled. That means some similar instances have different labels. This is called the issue of class overlap, which leads to a poor class boundary for classification models and makes constructing a high-quality classification model more difficult. However, the previous studies investigating the impact of the class overlap for EEG data is quite limited. Our goal is to investigate the impact of the class overlap on EEG-based early epileptic seizure detection. We propose a special neighborhood cleaning rule (SNCR) to solve the class overlap issue. To alleviate the class overlap issue, we conduct large-scale experiments on two widely-used EEG datasets and compare our proposed SNCR strategy with a state-of-the-art data clean strategy, i.e., the improved $k$ -means clustering cleaning approach (IKMCCA). The experimental results show that the classification model can achieve significantly better performance in terms of AUC, recall, and F1 metrics when using our proposed SNCR strategy. Therefore, for EEG-based early epileptic seizure detection, we recommend researchers to apply the SNCR strategy to mitigate the class overlap issue and use the SNCR strategy to perform data preprocessing in a future related study.
A New Heuristic Algorithm for Energy and Spectrum Efficient User Association in 5G Heterogeneous Networks

Macro base stations are densely overlaid by small cells to satisfy the demands of user equipments in heterogeneous networks. Due to their dense deployment, some small cells are not directly connected to macro base stations and thus backhaul connections are required to connect small cells to macro base stations. Millimeter wave backhauls which have high bandwidths are preferred for small cell backhaul communication, since they can increase the capacity of network considerably. In this context, association of user equipments to base stations becomes challenging due to the backhaul architecture. Considering environmental concerns, energy efficiency is a vital criterion in designing the user association algorithms. In this paper, we study the user association problem aiming at the maximization of energy efficiency given a specific spectral efficiency target. Firstly, we derive a quadratic upper bound on the total power consumption in heterogeneous network (access network and backhaul). Then we develop an energy and spectrum efficient user association method based on minimizing the derived quadratic upper bound, which indeed turns our target problem into a generalized quadratic assignment problem. Since this problem is NP-hard in general, we propose a heuristic algorithm for solving it. Extensive simulations show that our approach provides an enhanced level of energy efficiency compared to other well-known alternatives in the literature.
Orthogonal Periodic Sequences for the Identification of Functional Link Polynomial Filters

The paper introduces a novel family of deterministic periodic signals, the orthogonal periodic sequences (OPSs), that allow the perfect identification on a finite period of any functional link polynomials (FLiP) filter with the cross-correlation method. The class of FLiP filters is very broad and includes many popular nonlinear filters, as the well-known Volterra and the Wiener nonlinear filters. The novel sequences share many properties of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of FLiP filters with the cross-correlation method. But, while PPSs exist only for orthogonal FLiP filters, the OPSs allow also the identification of non-orthogonal FLiP filters, as the Volterra filters. In OPSs, the modeled system input can be any persistently exciting sequence and can also be a quantized sequence. Moreover, OPSs can often identify FLiP filters with a sequence period and a computational complexity much smaller that PPSs. The provided experimental results, involving the identification of real devices and of a benchmark model, highlight the potentialities of the proposed OPSs in modeling unknown nonlinear systems.
Short-term memory in neuron-astrocyte network

In this paper, we study the role of astrocyte-induced modulation of signal transmission in network of synaptically coupled spiking neurons in the mechanisms of the short-term memory formation. We show, that due to the local spatial synchronization of signaling in the neural network induced by the astrocyte during the duration of the Ca2+ signal, the proposed neuron-astrocyte network can operate as a temporal Hopfield network.
Cognitive Caching at the Edges for Mobile Social Community Networks: A Multi-Agent Deep Reinforcement Learning Approach

Content caching in the current commercial content delivery networks (CDNs) allows reduction of duplicate traffic and improvement of QoS and QoE but it still suffers from surges of content traffic, network congestion, high mobility of users and dynamic users’ content request patterns which may result in high content access latency. With the increasing interest of large companies in providing next-generation mobile edge applications and services that the users can use despite potentially sparse, non-uniform connectivity, it is becoming increasingly important to provide efficient, smart content caching services at the edges to help with scalable storage and processing of local data as well as sharing data both at the edges and in the cloud. We propose a novel multi-agent deep reinforcement learning approach, CognitiveCache, in which edges adaptively learn their best caching policies while collaborating with other neighbouring edges to better understand if they can be usable for cache content placement optimisation problem in dynamic environments. We show that CognitiveCache can respond and adapt to the spatial-temporal locality of dynamically changing content workloads and resources, improve the reliability and scalability of content sharing, enhance QoE for users and decrease operational costs in mobile social community networks. We perform extensive multi-criteria evaluation of our proposal against four benchmark and competitive protocols over two different real-world scenarios in New York and London in the face of different mobility and users’ interest patterns to show that CognitiveCache achieves higher cache hit ratios, lower delays while reducing resource consumption.
Quantum Computation by Classical Mechanical Apparatuses

We design a mechanical apparatus that works as "quantum" computer even though it follows classical physics. To do quantum computation, we need to know what percentage of a particular state is included in a superposed quantum state. However, the superposition easily breaks unless expensive and large-scale tooling, such as cryogenic temperature is prepared. We pay attention to causal formulation of quantum mechanics. The weighting coefficients of the superposition can be determined by monitoring the trajectory of a quantum particle. Such monitoring is possible only in macroscopic physics. Efficacy of our macroscopic apparatus model is shown in one of typical quantum algorithms, Deutsch algorithm. This classical mechanical design opens the way for "quantum mechanical" artificial intelligence to be mounted on robots in usual macroscopic world.
DETECTIF : Unified Detection & Correction of IoT Faults in Smart Homes

This paper tackles the problem of detecting a comprehensive set of sensor faults that can occur in IoT-instrumented smart homes customized to infer Activities of Daily Living (ADL) from the activation of sensor sets. Specifically, sensors can suffer faults that (a) span durations that vary between several seconds to hours, (b) can result in both missing or false-alarm sensor-events. Previous fault detection approaches are geared primarily to identify missing faults (absence of sensor readings) of a permanent (very long-lived) nature, or sporadic false-alarm events. We propose DetectIF, a fault-detection framework that detects faults of varying time duration, and identifies both missing and false-alarm sensor events. DetectIF's key novelties include developing rules capturing spatiotemporal correlations among sensors and augmenting those rules with statistical properties of such sensor-specific behavior. To test DetectIF under a variety of fault behavior, we develop a unified fault framework where the tuning of a couple of parameters allows us to generate and inject faults of desired type and duration into an underlying sensor stream. Experiments with such comprehensive fault data shows that DetectIF achieves 82-95% fault-detection accuracy, improving precision by a huge amount (33-66%) over competitive, state-of-the-art baselines. Moreover, we demonstrate the benefits of applying DetectIF on unmodified, benchmark smart home datasets: it is able to detect additional likely faults that prior fault detection approaches miss, and thus consequently achieve an average of 30% higher ADL recognition accuracy compared to prior state-of-the-art fault detection techniques.
Jamming and Classification of Drones Using Full-Duplex Radios and Deep Learning

The emerging full-duplex (FD) radio concept is set to double the spectral efficiency of commercial wireless networks, but it also has potential applications in the defense and security domains. In the form of multifunction military full-duplex radios (MFDRs), the FD capability could enable armed forces to conduct simultaneous electronic attacks, electronic support measures, and tactical communications. This paper demonstrates the feasibility of simultaneous jamming and reconnaissance of drones’ remote control (RC) systems using a prototype MFDR. Alongside, we apply deep learning in the form of a convolutional neural network (CNN) for classifying the RC signals and analyze the effect of FD operation on the classification performance.
Developmentally Synthesizing Earthworm-Like Locomotion Gaits with Bayesian-Augmented Deep Deterministic Policy Gradients (DDPG)

In this paper, a reinforcement learning method is presented to generate earthworm-like gaits for a hyperredundant earthworm-like manipulator robot. Partially inspired by human brain’s learning mechanism, the proposed learning framework builds its preliminary belief by first starting with adapting rudimentary gaits governed by a generic kinematic knowledge of undulatory, sidewinding and circular patterns. The preliminary belief is then represented as a prior ensemble to learn new gaits by leveraging apriori knowledge and learning a policy by inferring posterior over prior distribution. While the fundamental idea of incorporating Bayesian learning with reinforcement learning is not new, this paper extends Bayesian actor-critic approach by introducing augmented prior-based directed bias in policy search, aiding in faster parameter learning and reduced sampling requirements. We show results on an in-house built 10-DoF earthworm-like robot that exhibits adaptive development, qualitatively learning different locomotion modes, while given with only rudimentary generic gait behaviors. The results are compared against deterministic policy gradient method (DDPG) for continuous control as the baseline. We show that our proposed method can characterize effective performance over DDPG, and it also achieves faster kinematic indexes in various gaits.
Group Comfort Models: Predicting Indoor Group Thermal Comfort by Learning Preferences of Multiple Occupants

The main operation goal of smart buildings, considering the integration of information and building physical systems, is to provide a more comfortable environment for indoor occupants. However, indoor thermal environment is usually set by an empirical value or manually adjusted under uncomfortable situations, where the thermal preferences of different occupants in the room are rarely considered. In this paper, we develop a multiple preferences-based model to predict group thermal comfort. The model consists of a class-division part, a one-class comfort zone part and a group comfort zone integration part. The class-division part segments occupants into several categories based on body mass index. For each category, the support vector machine algorithm is used to predict the thermal comfort zone. Then according to the distribution of occupants in the room, the weights of each category are dynamically adjusted to obtain a group comfort zone. The developed method solves the non-cooperative group thermal comfort problem of occupants with multiple thermal preferences. The performance of the developed method is demonstrated through comparison with PMV model based on actual data. The results show that the developed method provides a more rigorous and reasonable group comfort zone with only a small sacrifice in energy consumption.
How to Cut Out Expired Data with Nearly Zero Overhead for Solid-State Drives

Owing to flash memory constraints, a garbage collection (GC) mechanism hurts flash storage lifespan and performance since it generates a massive amount of write data to flash memory. To add insult to injury, all GC designs cannot identify disused data from valid data; therefore, all valid data, including disused data, will be rewritten to flash memory during the GC process. Fortunately, a flash storage vendor recently proposed a new write command to bring extra information to flash translation layer (FTL). Thanks to the new write command, the lifetime information of data can be brought from a host-side system to an FTL management layer for disused data identification. By such observations, this work proposes a dual-time referencing FTL (DTR-FTL) design to deal with disused data and minimize the overhead of GC by referring to data lifetime information and block retention time.
A CPW-Fed Square Slot Circularly Polarized Antenna With Asymetrie Stubs For Ultra Wideband Applications

This paper presents a new design for a Circularly Polarized Square Slot Antenna (CPSSA). The proposed antenna is single layered with L-shaped stubs loaded on both the opposite corners of the antenna, which is capable of generating orthogonal modes for the excitation of Circular Polarization. Two asymmetric strips on the top and the left portions of the proposed antenna and various other asymmetric strips are also loaded. The proposed structure is having the dimensions of 40 x 40 x 1.6 mm3.The designed CPSSA operates in the range of 3.2-12.5 GHz with Axial Ratio (AR) bandwidth in the range of 4.5-8.5 GHz. In this paper the improvement of the performance parameters like AR and Return Loss (S11) on the addition of asymmetric stubs is explained in detail.
PAIR: Pin-aligned In-DRAM ECC architecture using expandability of Reed-Solomon code

The computation speed of computer systems is getting faster and the memory has been enhanced in performance and density through process scaling. However, due to the process scaling, DRAMs are recently suffering from numerous inherent faults. DRAM vendors suggest In-DRAM Error Correcting Code (IECC) to cope with the unreliable operation. However, the conventional IECC schemes have concerns about miscorrection and performance degradation. This paper proposes a pin-aligned In-DRAM ECC architecture using the expandability of a Reed-Solomon code (PAIR), that aligns ECC codewords with DQ pin lines (data passage of DRAM). PAIR is specialized in managing widely distributed inherent faults without the performance degradation, and its correction capability is sufficient to correct burst errors as well. The experimental results analyzed with the latest DRAM model show that the proposed architecture achieves up to 106 times higher reliability than XED with 14% performance improvement, and 10 times higher reliability than DUO with a similar performance, on average.
Robust Interference Cancellation for Vehicular Communication and Radar Coexistence

Spectrum sharing of wireless vehicle-to-vehicle (V2V) communication and automotive radar in intelligent transportation systems (ITS) can provide higher spectrum utilization efficiency. However, spectrum sharing between two systems might bring severe interference, which needs to be well solved. To address the communication-radar spectrum sharing (CRSS) and inter-system interference issue, we first model the radar interference estimation as an off-grid compressed sensing problem. Then, we propose an interference cancellation algorithm based on the atomic norm (AN) minimization theory. Simulation results show that the proposed algorithm facilitates the coexistence between radar and communication and improves the V2V communication performance efficiently in the presence of automotive radar interference.
Active Impact Motion for a Quadruped Robot

In this paper, we propose a novel method of generating and executing active impact motion on a quadruped robot by mimicking active impact motion of quadruped animals. In order to generate active impact motion postures, we divide the whole active impact process into three phases and present a Center of Gravity (COG) trajectory planning method for these phases. A controller based on virtual model control (VMC) is used for active impact motion execution and a pitch attitude adjustment controller is utilized to stabilize the robot. Besides, a Hopf oscillator-based Central Pattern Generator (CPG) network with controllable locomotion stride is responsible for generating control signals of quadruped gaits during active impact motion. In order to verify effectiveness of the proposed control strategy, we show experimental results that a quadruped robot pushes the target object weighing 15 kg away for 50 cm by executing active impact motion.
A 3D Simulation Environment and Navigation Approach for Robot Navigation via Deep Reinforcement Learning in Dense Pedestrian Environment

With the rapid development of mobile robot technology, robots are playing an increasingly important role in people’s daily lives. As one of the key technologies of the basic functions of mobile robots, navigation also needs to deal with new challenges. How to navigate efficiently and collision-free in complex and changeable human environments is one of the problems that need to be solved urgently. Currently, mobile robots can achieve efficient navigation in static environments. However, in the unstructured and fast-changing environments of human daily society, robots need to make more flexible navigation strategies to deal with the dynamic scenarios. This paper built a 3D simulation environment for robot navigation via deep reinforcement learning in dense pedestrian environment. We also proposed a new navigation approach via deep reinforcement learning in dense pedestrian environment. The simulation environment of this paper integrates Gazebo, ROS navigation stack, Stable baselines and Social Force Pedestrian Simulator. In order to be able to collect the rich environmental information around the robot, our simulation environment is based on the Gazebo simulation platform. In order to use the traditional path planning methods, we introduce the ROS navigation stack. In order to make it easier to call the current mainstream reinforcement learning algorithms, we introduce Stable baselines which is a set of improved implementations of reinforcement learning algorithms based on OpenAI Baselines. In order to imitate dense pedestrian scenarios realistically, we introduce the Social Force Pedestrian Simulator which is a pedestrian simulation package, whose pedestrian’s movement follows the rules of Social Force Movement. Our robot navigation approach combines the global optimality of traditional global path planning and the local barrier ability of reinforcement learning. Firstly, we plan global path by using A* algorithm. Secondly, we use Soft Actor Critic (SAC) to try to follow the waypoints generated at a certain distance on the global path to make action decisions on the premise of agile obstacle avoidance. Experiments show that our simulation environment can easily set up a robot navigation environment and navigation approaches can be simulated in various dense pedestrian environments.
AHEC: End-to-end Compiler Framework for Privacy-preserving Machine Learning Acceleration

Privacy-preserving machine learning (PPML) is driven by the emerging adoption of Machine Learning as a Service (MLaaS). In a typical MLaaS system, the end-user sends his personal data to the service provider and receives the corresponding prediction output. However, such interaction raises severe privacy concerns about both the user’s proprietary data and the server’s ML model. PPML integrates cryptographic primitives such as Multi-Party Computation (MPC) and/or Homomorphic Encryption (HE) into ML services to resolve the privacy issue. However, existing PPML solutions have not been widely deployed in practice since: (i) Privacy protection comes at the cost of additional computation and/or communication overhead; (ii) Adapting PPML to different front-end frameworks and back-end hardware incurs prohibitive engineering cost.We propose AHEC, the first automated, end-to-end HE compiler for efficient PPML inference. Leveraging the capability of Domain Specific Languages (DSLs), AHEC enables automated generation and optimization of HE kernels across diverse types of hardware platforms and ML frameworks. We perform extensive experiments to investigate the performance of AHEC from different abstraction levels: HE operations, HE-based ML kernels, and neural network layers. Empirical results corroborate that AHEC achieves superior runtime reduction compared to the state-of-the-art solutions built from static HE libraries.
High PE Utilization CNN Accelerator with Channel Fusion Supporting Pattern-Compressed Sparse Neural Networks

Recently CNN-based methods have made remarkable progress in broad fields. Both network pruning algorithms and hardware accelerators have been introduced to accelerate CNN. However, existing pruning algorithms have not fully studied the pattern pruning method, and current index storage scheme of sparse CNN is not efficient. Furthermore, the performance of existing accelerators suffers from no-load PEs on sparse networks. This work proposes a software-hardware co-design to address these problems. The software includes an ADMM-based method which compresses the patterns of convolution kernels with acceptable accuracy loss, and a Huffman encoding method which reduces index storage overhead. The hardware is a fusion-enabled systolic architecture, which can reduce PEs’ no-load rate and improve performance by supporting the channel fusion. On CIFAR-10, this work achieves 5.63x index storage reduction with 2-7 patterns among different layers with 0.87% top-1 accuracy loss. Compared with the state-of-art accelerator, this work achieves 1.54x-1.79x performance and 25%-34% reduction of no-load rate with reasonable area and power overheads.