Pair Of Devices Research Articles

Sub-GHz Two-Way Ranging Based on Phase Detection for Remote Keyless Entry Systems

Remote keyless entry systems have penetrated the vehicular market due to its convenience. However, its vulnerability against relay attacks exploiting the wireless channel is a serious threat. One of the prominent countermeasures is radio ranging between the key fob and the vehicle. Taking into account radio regulations, usability and power consumption among other things, a suitable approach is necessary to devise. This article proposes sub-GHz two-way ranging based on phase detection which offers high tolerance against reference clock offset between a pair of transceiver devices and a novel compensation scheme against multipath effects. We present the operation principle and theoretical performance under a two-path model of radio propagation and additive white Gaussian noise. In field experiments with prototype devices, the ranging accuracy is validated. We show that the proposed ranging produces sufficient performance for remote keyless entry systems, confirming if the key fob is within a few meters of the vehicle.

WiFi-Based Cross-Domain Gesture Recognition via Modified Prototypical Networks

Numerous deep learning studies have achieved remarkable advances in WiFi-based human gesture recognition (HGR) using channel state information (CSI). However, since the CSI patterns of the same gesture change across domains (i.e., users, environments, locations, and orientations), recognition accuracy might degrade significantly when applying the trained model to new domains. To overcome this problem, we propose a WiFi-based cross-domain gesture recognition system (WiGr) which has a domain-transferable mapping to construct an embedding space where the representations of samples from the same class are clustered, and those from different classes are separated. The key insight of WiGr is using the similarity between the query sample representation and the class prototypes in the embedding space to perform the gesture classification, which can avoid the influence of the cross-domain CSI patterns change. Meanwhile, we present a dual-path prototypical network (Dual-Path PN) which consists of a deep feature extractor and a dual-path (i.e., Path-A and Path-B substructures) recognizer. The trained feature extractor can extract the gesture-related domain-independent features from CSI, namely, the domain-transferable mapping. In addition, WiGr implements the cross-domain HGR based on only a pair of WiFi devices without retraining in the new domain. We conduct comprehensive experiments on three data sets, one is built by ourselves and the others are public data sets. The evaluation suggests that WiGr achieves 86.8%–92.7% in-domain recognition accuracy and 83.5%–93% cross-domain accuracy under the four-shot condition.

Application of a Pair of Conical Narrowing Devices to Define Flow Rates of Horizontal Two-Phase Water-Gas Flows in Separationless Flow-Meter

Application of a Pair of Conical Narrowing Devices to Define Flow Rates of Horizontal Two-Phase Water-Gas Flows in Separationless Flow-Meter

Development of a Novel 2-Dimensional Neck Haptic Device for Gait Balance Training

Balance problems can be a major cause of falling. Existing gait balance rehabilitation devices usually have limited overground usability due to low portability. Therefore, there is need for development of a portable gait balance rehabilitation system. To fulfill this need, a wearable balance biofeedback system is proposed that utilizes a pair of novel devices to deliver 2-dimensional hybrid haptic biofeedback to the neck that is a combination of indentation and stretching of the skin corresponding to the mediolateral (ML) and anteroposterior (AP) directions, respectively. The system's functionality is demonstrated through an experiment where 14 healthy subjects and 1 stroke patient performed stance and gait tasks under various feedback conditions. Provision of feedback to healthy subjects resulted in significant improvements in two-dimensional balance under all task conditions. It is also observed that provision of feedback during more difficult tasks resulted in more significant balance improvements. Furthermore, use of the system during gait balance evaluation trials did not cause any significant change in gait speed, meaning that it does not have any detrimental effect on the user's gait. Results of the stroke subject pilot trial showed similar trends. We expect that use of the proposed system may help to improve the overground gait balance of people suffering from the after effects of diseases such as stroke.

Validation and Assessment of Diffusive Gradients in Thin-Films (DGT) Technique for Measuring Nutrients in Taihu Lake Water with Algae Bloom.

DGT (diffusive gradients in thin films) technique has been developed for measuring nitrogen in freshwaters and applied to assess the bioavailability of phosphorus in soils/sediments. These two elements are the main nutrients causing algae bloom, but DGT has never been used in the field water conditions with algae bloom. In our study, a pair of DGT devices were used in comparison with grab sampling to characterize the performance of this technique to measure labile NO3-N, NH4-N, and PO4-P concentrations in algae-cultivated Taihu Lake water. The results showed that DGT measurement was highly affected by algae bloom and the environmental conditions using the current assemblies, especially for NH4-N measurement. For in situ measurement of nutrients in the real environment, an improvement to the DGT technique is required. The comprehensive assessment of the level of eutrophication needs to consider a variety of environmental factors rather than just the concentration of nutrients.

New Collaborative Caching Scheme for D2D Content Sharing in 5G

Device-to-Device (D2D) content sharing is an excellent solution to offload the traffic from the core 5G networks and backhaul links. Caching the most popular files at the network edge and in users’ devices to support user proximity services in 5G helps to offload traffic in the core network and increases the cache hit probability. D2D communication in 5G can be utilized to share the cached files between a pair of devices with a minimal involvement from the base station. This paper presents a collaborative content caching scheme with the primary objective to maximize the overall system offloading gain and the cache hit probability in downloading the popular file contents. The proposed system model exploits the social-networking concept within the WiFi Direct radio range, assuming the cell structure present in a densely populated area such as a university campus or an auditorium. The performance of the proposed cache scheme is evaluated through extensive simulations taking into consideration the device mobility and disconnection, and the results are presented.

Robust Adaptive Observer-Based Predictive Control for a Non-Linear Delayed Bilateral Teleoperation System

In this paper, a new robust adaptive nonlinear teleoperation system using an improved extended active observer (IEAOB), adaptive Smith predictor (ASP) and sliding mode control is developed to address the time delay in the communication channels and the nonlinear robot model uncertainties. Firstly, an ASP based on Padé approximation and active observer is designed to compensate for the time delay effect. Specifically, the total network time delay is modelled by Padé approximation, and then an active observer is deployed to estimate the time delay. To ensure the time-varying delay effect is completely suppressed, a sliding mode control algorithm is further developed. Meanwhile, the IEAOB is utilized at both the master and slave sides to deal with the nonlinear model uncertainties, it can provide an accurate robot inertial parameters and friction estimation as well as external force estimation without a structured force model in the presence of disturbances. Finally, the stability of the designed teleoperation system is theoretically studied and the system effectiveness is demonstrated by applying it to a pair of Phantom Omni haptic devices connected via a communication channel with time-varying delays.

A Linear VCO Using Single CFA and Analog Multipliers: Quadrature Oscillator Implementation

A new linear voltage-controlled oscillator (LVCO) implementation using single AD-844 CFA with a pair of AD-835 multiplier devices and a pair of grounded capacitors is proposed. The open-loop transfer function of the topology is analyzed wherein the concept of Short-Circuit Natural Frequency (SCNF) is applied to derive the sinusoid oscillator implementation. The proposed oscillator circuit is then restructured to yield a linear voltage-controlled quadrature oscillator (LVCQO) after appropriate cascade with a CFA-based active integrator. The oscillation frequency is linearly tunable ([Formula: see text][Formula: see text]MHz) by the multiplier control voltage ([Formula: see text]. Subsequently, a high-[Formula: see text] selective band-pass (BP) filter is derived. Effects of the CFA port roll-off parameters and its parasitic capacitors ([Formula: see text] had been analyzed to be negligible. Measured oscillator response exhibited a THD [Formula: see text]%, a linearity error ([Formula: see text]% and a phase noise figure of ([Formula: see text]104 dBc/Hz at 24-kHz offset.

Alternative negative weight for simpler hardware implementation of synapse device based neuromorphic system

Lately, there has been a rapid increase in the use of software-based deep learning neural networks (S-DNN) for the analysis of unstructured data consumption. For implementation of the S-DNN, synapse-device-based hardware DNN (H-DNN) has been proposed as an alternative to typical Von-Neumann structural computing systems. In the H-DNN, various numerical values such as the synaptic weight, activation function, and etc., have to be realized through electrical device or circuit. Among them, the synaptic weight that should have both positive and negative numerical values needs to be implemented in a simpler way. Because the synaptic weight has been expressed by conductance value of the synapse device, it always has a positive value. Therefore, typically, a pair of synapse devices is required to realize the negative weight values, which leads to additional hardware resources such as more devices, higher power consumption, larger area, and increased circuit complexity. Herein, we propose an alternative simpler method to realize the negative weight (named weight shifter) and its hardware implementation. To demonstrate the weight shifter, we investigated its theoretical, numerical, and circuit-related aspects, following which the H-DNN circuit was successfully implemented on a printed circuit board.

Thermal Crosstalk Characterization Using Temperature Dependent Leakage Current Through Gate Stacks

In this letter, a new method based on the temperature dependence of gate leakage current for accurately characterizing thermal crosstalk is proposed and performed on the advanced silicon-on-insulator (SOI) MOSFETs. The developed technique enables to capture the pure thermal crosstalk effect as it excludes the self-heating impact during the measurement. Moreover, it is proven robust over a large range of gate voltages regardless of the difference of device types, which is demonstrated on a pair of devices with a shared port. The accuracy of the thermal crosstalk measurement benefits in determining the overall thermal performance of the nano-scaled circuits under the mutual heating impacts. It offers a possible hardware solution to thermal monitor and management.

Implementing cryptography in LoRa based communication devices for unmanned ground vehicle applications

In this study, a Long Range (LoRa) based bidirectional secured communication link for controlling and monitoring the robots from a remote location is proposed. The security features and structure of the LoRa is build upon the standard protocol called LoRa wide area networks (LoRaWAN). To take advantage of these features, LoRa devices/end modules need to be connected to a network server through a LoRa gateway. However, for certain military scenarios like war zones, terrorist attack sites, disaster sites etc., the availability of standard network cannot be guaranteed, and therefore, the security features available with LoRaWAN protocol cannot be guaranteed. To overcome this critical limitation, a LoRa based secured device for establishing a bidirectional communication link between the base station and the robots without relying on any network is proposed. To secure the exchanged data, a cryptographic protocol is developed, and confidentiality, authenticity and integrity of the transmitted data between the base station and the robot are ensured. The protocol is then implemented with a pair of LoRa devices and its functionality is tested from a remote location by controlling and monitoring a Pioneer-P3Dx robot from a distance of 1.2 miles. The test shows that the proposed cryptographic protocol can securely control and track the robot from a remote location.

WITHDRAWN: A novel RSS based light weight digital certificate and key generation scheme for coherent communication of IoT devices

The Internet-of-Things (IoT) will connect different types of cyber physical systems that are supported by different wireless transmission technologies, e.g., Wi-Fi, ZigBee, etc. However, the security protocols of those wireless technologies cannot be implemented to support seamless and secure IoT communications. Moreover, security protocols need to be light-weighted since some IoT devices have limited computation resources. In this research work, we propose a light weight physical-layer security-based authentication and key generation scheme The Internet-of-Things (IoT) will connect different types of cyber physical systems that are supported by different wireless transmission technologies, e.g., Wi-Fi, ZigBee, etc. However, the security protocols of those wireless technologies cannot be implemented to support seamless and secure IoT communications. Moreover, security protocols need to be light-weighted since some IoT devices have limited computation resources. In this research work, we propose a light weight physical-layer security-based authentication and key generation scheme to bridge different transmission technologies in IoT communications. In the studied scenario, a group certificate is generated based on physical-layer information, i.e., Received Signal Strength (RSS) for all legitimate IoT devices in a network. The group certificate is to support mutual authentication between any pair of IoT devices within the network. Furthermore, the proposed scheme also generates a session key for the authenticated pair of IoT devices for other purposes of security features. The proposed scheme also addresses the issue of access control for new IoT devices with a short-term session key generation process. Security analysis is provided to show the efficacy of the proposed scheme against eavesdropping and replay attacks. A tested based experiment is conducted to further demonstrate the RSS based value generation and distribution.

Ratio-based multi-level resistive memory cells

Ratio-based encoding has recently been proposed for single-level resistive memory cells, in which the resistance ratio of a pair of resistance-switching devices, rather than the resistance of a single device (i.e. resistance-based encoding), is used for encoding single-bit information, which significantly reduces the bit error probability. Generalizing this concept for multi-level cells, we propose a ratio-based information encoding mechanism and demonstrate its advantages over the resistance-based encoding for designing multi-level memory systems. We derive a closed-form expression for the bit error probability of ratio-based and resistance-based encodings as a function of the number of levels of the memory cell, the variance of the distribution of the resistive states, and the ON/OFF ratio of the resistive device, from which we prove that for a multi-level memory system using resistance-based encoding with bit error probability x, its corresponding bit error probability using ratio-based encoding will be reduced to x^2 at the best case and x^{sqrt{2}} at the worst case. We experimentally validated these findings on multiple resistance-switching devices and show that, compared to the resistance-based encoding on the same resistive devices, our approach achieves up to 3 orders of magnitude lower bit error probability, or alternatively it could reduce the cell’s programming time and programming energy by up 5–10times, while achieving the same bit error probability.

Physical Layer Encryption for WDM Optical Communication Systems Using Private Chaotic Phase Scrambling

Protecting the security of optical network is a major worldwide challenge. The conventional schemes for securing communications are based on cryptography at the MAC layer and its higher layers, which are facing a great threat with the development of quantum computers. Optical encryption is a promising way for building security on the physical layer of optical communications. In this work, we propose a novel optical encryption scheme for wavelength division multiplexing (WDM) fiber-optic communication systems, by utilizing the private chaotic phase scrambling. Secure transmission of 50 Gbps signal over 50-km standard single-mode fiber is experimentally and numerically demonstrated. The results show that the WDM signal is efficiently encrypted into a noise-like signal, due to the spectral broadening effect of chaotic phase scrambling. Since the processes of encryption and decryption are totally implemented in the optical domain, the proposed scheme can encrypt the entire network traffic with low latency and high speed. Moreover, the proposed encryption scheme is compatible with the existing WDM optical networks, and only one pair of encryption and decryption devices is sufficient to encrypt all WDM channels, thus the scheme can be easily implemented at low hardware cost.

Machine Learning Implementation of a Diabetic Patient Monitoring System Using Interactive E-App.

Lifestyle influences morbidity and mortality rates in the world. Physical activity, a healthy weight, and a healthy diet are key preventative health behaviours that help reduce the risk of developing type 2 diabetes and its complications, such as cardiovascular disease. A healthy lifestyle has been shown to prevent or delay chronic diseases and their complications, but few people follow all recommended self-management behaviours. This work seeks to improve knowledge of factors affecting type 2 diabetes self-management and prevention through lifestyle changes. This paper describes the design, development, and testing of a diabetes self-management mobile app. The app tracked dietary consumption and health data. Bluetooth movement data from a pair of wearable insole devices are used to track carbohydrate intake, blood glucose, medication adherence, and physical activity. Two machine learning models were constructed to recognise sitting and standing. The SVM and decision tree models were 86% accurate for these tasks. The decision tree model is used in a real-time activity classification app. It is exciting to see more and more mobile health self-management apps being used to treat chronic diseases.

Image Polarity Detection on Resource-Constrained Devices

Image polarity detection opens new vistas in the area of pervasive computing. State-of-the-art frameworks for polarity detection often prove computationally demanding, as they rely on deep learning networks. Thus, one faces major issues when targeting their implementation on resource-constrained embedded devices. This article presents a design strategy for convolutional neural networks that can support image-polarity detection on edge devices. The outcomes of experimental sessions, involving standard benchmarks and a pair of commercial edge devices, confirm the approach suitability.

Wi-PSG: Detecting Rhythmic Movement Disorder Using COTS WiFi

Rhythmic movement disorder (RMD) is closely related to health problems like insomnia, daytime fatigue, anxiety disorder, and depression, or even causes severe injuries resulting from the movements. To obtain detailed information of RMD related abnormal movements for early diagnosis, there are generally three categories of solutions: 1) using camera to record image data; 2) wearing various smart devices; and 3) deploying dedicated hardware to capture sensor data. But none of such are widely accepted for different reasons due to privacy, inconvenience and excessive overhead. We believe one of the essential features in a feasible solution is nonintrusiveness , in which movement data collection should be carried out without the awareness of targets. In addition, it should be fairly accurate and low cost. In this work, we propose Wi-PSG, a contactless and nonintrusive sleep monitoring system, which exploits channel state information (CSI) from existing WiFi infrastructures to detect RMD related movements. Specifically, we introduce new set of sensitivity metrics and reconstruct the collected CSI into an ideal subcarrier sensitive to all target movements. With the estimated CSI background model derived from static propagation paths, nonmovement interference can be canceled from RMD movement detection.We then train the classifier for distinguishing different kinds of RMD movements using both time and frequency features extracted from CSI signals. We implement Wi-PSG with a pair of WiFi devices and wireless access point. We evaluate Wi-PSG with nine volunteers over a one-month period. The extensive experiments demonstrate that Wi-PSG can achieve a recognition accuracy of above 92%, even under challenging scenarios.

Group authentication protocol based on aggregated signatures for D2D communication

Device-to-device (D2D) communication is one of the most promising technologies of new mobile communication networks (5G), and essential for the Communicating Things Networks (CTNs) paradigm. Its application scope has been widened and billions of devices are expected to be communicating in the next years. Moreover, goals, such as end-to-end security and reductions in computational costs required by resource-constrained devices, must be accomplished for its full implementation. The Third Generation Partnership Project (3GPP) standardized authentication and key agreement (AKA) protocols are not suitable for D2D due to differences in the architecture and communication scenario. They may cause several security issues and excessive computational and transmission overhead, since they require individual executions for each pair of devices. The development of AKA protocols adapted to D2D group communication is still in its initial steps. This article introduces a new authentication and key agreement protocol for D2D groups of devices based on asymmetric cryptography and aggregated signatures. The scheme focuses on robust security and reductions in computational and communication costs. It was compared to other group AKA protocols and yielded better results regarding security and overhead reduction. A formal validation conducted by Automated Validation of Internet Security Protocols and Applications tool (AVISPA) proved its robustness.

Respiration Tracking for People Counting and Recognition

Wireless detection of respiration rates is crucial for many applications. Most of the state-of-the-art solutions estimate breathing rates with the prior knowledge of crowd numbers as well as assuming the distinct breathing rates of different users, which is neither natural nor realistic. However, few of them can leverage the estimated breathing rates to recognize human subjects (also known as identity matching). In this article, using the channel state information (CSI) of a single pair of commercial WiFi devices, a novel system is proposed to continuously track the breathing rates of multiple persons without such impractical assumptions. The proposed solution includes an adaptive subcarrier combination method that boosts the signal-to-noise ratio (SNR) of breathing signals, and iterative dynamic programming and a trace concatenating algorithm that continuously tracks the breathing rates of multiple users. By leveraging both the spectrum and time diversity of the CSI, our system can correctly extract the breathing rate traces even if some of them merge together for a short time period. Furthermore, by utilizing the breathing traces obtained, our system can do people counting and recognition simultaneously. Extensive experiments are conducted in two environments (an on-campus lab and a car). The results show that 86% of average accuracy can be achieved for people counting up to four people for both cases. For 97.9% out of all the testing cases, the absolute error of crowd number estimates is within 1. The system achieves an average accuracy of 85.78% for people recognition in a smart home case.

CoSafe: Securing Mobile Devices through Mutual Mobility Consistency Verification

As mobile devices play increasingly important roles in our daily lives, it is of great significance to protect personal mobile devices from being lost. Noticing the trend that one person normally carries more than one mobile device, we propose an innovative scheme, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoSafe</i> , to detect device loss by verifying the motion consistency between a pair of devices. The rationale is that the vibrations perceived on devices carried by the same person should be tightly coupled whereas a lost device would show distinct mobility characteristics from others. Specifically, CoSafe compares the mobility consistency between a pair of devices on three levels, where coarse features (i.e., the mobility state and motion periodicity) are first compared to give fast response and more complex comparison on subtle feature (i.e., the relative phase) is conducted only when needed. In this way, CoSafe can instantly respond and introduce very low computation and communication costs. We implement CoSafe on a Commercial-Off-The-Shelf Android smartphone and a smartwatch, and conduct both trace-driven simulations and real-world experiments to evaluate the performance of CoSafe. The results show that CoSafe achieves a mean false negative ratio and false positive ratio of 1.46 and 3.12 percent, respectively, even under sophisticated stealing attacks.