Low Computational Sensing with Goertzel Filtering for Mobile Industrial IoT Devices

Internet-of Things (IoT) is getting connected to an increasing number of mobile devices such as autonomous vehicles, drones and robots. Termed as Mobile Industrial Internet-of Things (MI²oT) devices in this paper, a key requirement of these devices is to accurately estimate range and Doppler in various applications, in addition to data communication. Research efforts therefore include incorporating MI²-oT devices with high-data rate communications together with Frequency Modulated Continuous Wave Radar (FMCW) sensing capabilities. Range and Doppler sensing, in FMCW radars is undertaken by a twostage Fast Fourier Transform (FFT) which is computationally demanding. It is challenging to design baseband processing with FFTs that can be implemented as low computational hardware or application specific integrated circuits (ASIC) in MI²-oT devices. This paper, presents a novel range and Doppler sensing technique based on Goertzel filtering, leading to considerable reduction in computations compared to the FFT. FMCW radar with Goertzel filtering and FFT are examined in three cases viz., sensing the range and velocity of a car, vibration and respiration monitoring. Simulation results show a computation reduction of the order of 6.3×, 7.7× and 8.1× \u1d422\u1d427Giga-operations per second (GOPS) for the three cases respectively. The reduced computations increase the feasibility of implementing range and Doppler sensing in MI²oT devices which have restricted computational resources

Forest and Land-Use Practices in Philippine Uplands: National Level Analysis Based on Eight Villages

The study identifies the rural poor and measures their needs and capabilities. It focuses on the tree product from forests and upland cultivation systems and analyzes the forest and land use practices of the upland poor through the development of a common database. It also examines the influence of household-specific characteristics, development intervention mechanisms, institutional and local use conditions on specific tree and forest-use practices.land use planning, natural resources and environment, forestry sector, land management, uplands

Low Power Analog Processing for Ultra-High-Speed Receivers with RF Correlation

Ultra-high-speed data communication receivers (Rxs) conventionally require analog digital converters (ADC)s with high sampling rates which have design challenges in terms of adequate resolution and power. This leads to ultra-high-speed Rxs utilising expensive and bulky high-speed oscilloscopes which are extremely inefficient for demodulation, in terms of power and size. Designing energy-efficient mixed-signal and baseband units for ultra-high-speed Rxs requires a paradigm approach detailed in this paper that circumvents the use of power-hungry ADCs by employing low-power analog processing. The low-power analog Rx employs direct-demodulation with RF correlation using low-power comparators. The Rx is able to support multiple modulations with highest modulation of 16-QAM reported so far for direct-demodulation with RF correlation. Simulations using Matlab, Simulink R2020a® indicate sufficient symbol-error rate (SER) performance at a symbol rate of 8 GS/s for the 71 GHz Urban Micro Cell and 140 GHz indoor channels. Power analysis undertaken with current analog, hybrid and digital beamforming approaches requiring ADCs indicates considerable power savings. This novel approach can be adopted for ultra-high-speed Rxs envisaged for beyond fifth generation (B5G)/sixth generation (6G)/ terahertz (THz) communication without the power-hungry ADCs, leading to low-power integrated design solutions

SmartDR: A Device-to-Device Communication for Post-Disaster Recovery

Natural disasters, such as earthquakes, can cause severe destruction and create havoc in the society.Buildings and other structures may collapse during disaster incidents causing injuries and deaths to victims trapped under debris and rubble. Immediately after a natural disaster incident, it becomes extremely difficult for first responders and rescuers to find and save trapped victims. Often searches are carried out blindly in random locations, which delay the rescue of the victims. This paper introduces a Smartphone Assisted Disaster Recovery (SmartDR) method for post-disaster communication using Smartphones. SmartDR utilizes the device-to-device (D2D) communication technology in Fifth Generation (5G) networks, which enables direct communication between proximate devices without the need of relaying through a network infrastructure, such as mobile access points or mobile base stations. We examine a scenario of multi-hop D2D communication where smartphones carried by trapped victims and other people in disaster affected areas can self-detect the occurrence of a disaster incident by monitoring the radio environment and then can self-switch to a disaster mode to transmit emergency help messages with their location coordinates to other nearby smartphones. To locate other nearby smartphones also operating in the disaster mode and in the same channel, each smartphone runs a rendezvous process. The emergency messages are thus relayed to the functional base station or rescue centre. To facilitate routing of the emergency messages, we propose a path selection algorithm, which considers both delay and the leftover energy of a device (a smartphone in this case). Thus, the SmartDR method includes: (i) a multi-channel channel hopping rendezvous protocol to improve the victim localization or neighbor discovery, and (ii) an energy-aware multi-path routing (Energy-aware ad-hoc on-demand distance vector or E-AODV) protocol to overcome the higher energy depletionrate at devices associated with single shortest path routing. The SmartDR method can guide search and rescue operations and increase the possibility of saving lives immediately aftermath a disasterincident. A simulation-based performance study is conducted to evaluate the protocol performance in post-disaster scenario. Simulation results show that a significant performance gain is achievable when a device utilises the channel information for the rendezvous process and the leftover energy

Dictionary selection for Compressed Sensing of EEG signals using sparse binary matrix and spatiotemporal sparse Bayesian learning

Online monitoring of electroencephalogram (EEG) signals is challenging due to the high volume of data and power requirements. Compressed sensing (CS) may be employed to address these issues. Compressed sensing using sparse binary matrix, owing to its low power features, and reconstruction/decompression using spatiotemporal sparse Bayesian learning have been shown to constitute a robust framework for fast, energy efficient and accurate multichannel bio-signal monitoring. EEG signal, however, does not show a strong temporal correlation. Therefore, the use of sparsifying dictionaries has been proposed to exploit the sparsity in a transformed domain instead. Assuming sparsification adds values, a challenge, therefore, in employing this CS framework for the EEG signal is to identify the suitable dictionary. Using real multichannel EEG data from 15 subjects, in this paper, we systematically evaluated the performance of the framework when using various wavelet bases while considering their key attributes of number of vanishing moments and coherence with sensing matrix. We identified Beylkin as the wavelet dictionary leading to the best performance. Using the same dataset, we then compared the performance of Beylkin with discrete cosine basis, often used in the literature, and the case of using no sparsifying dictionary. We further demonstrate that using dictionaries (Beylkin and DCT) may improve performance tangibly only for a high compression ratio (CR) of 80% and with smaller block sizes; as compared to when using no dictionaries

Forest and land-use practices in Philippine uplands : national level analysis based on eight villages

The Forest/Land-use Practices in the Philippines Study (FLUPPS) investigates forest and landuse practices of the rural poor across various sites. It focuses on the potential relationships between forest, tree-use patterns and land-use practices taking into account: a. forest-related policies and development programs in the national level; b. social, economic and tenurial characteristics of households; and c. conditions specific to the communities' studied. Four investigators gathered information on the household-level through surveys on (1) social and economic conditions of 50 respondents in each eight study sites; (2) forest and tree-use practices of 25 households in each of the same sites; and (3) case analysis of village-specific issues. Additionalinformation on fuelwood use was generated in six of the eight villages

mmWave V2V Localization in MU-MIMO Hybrid Beamforming

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in a number of vehicles reduces the Cramr-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters

MmWave V2V Localization in MU-MIMO Hybrid Beamforming

Recent trends for vehicular localization in millimetre-wave (mmWave) channels include employing a combination of parameters such as angle of arrival (AOA), angle of departure (AOD), and time of arrival (TOA) of the transmitted/received signals. These parameters are challenging to estimate, which along with the scattering and random nature of mmWave channels, and vehicle mobility lead to errors in localization. To circumvent these challenges, this paper proposes mmWave vehicular localization employing difference of arrival for time and frequency, with multiuser (MU) multiple-input-multiple-output (MIMO) hybrid beamforming; rather than relying on AOD/AOA/TOA estimates. The vehicular localization can exploit the number of vehicles present, as an increase in a number of vehicles reduces the Cramr-Rao bound (CRB) of error estimation. At 10 dB signal-to-noise ratio (SNR) both spatial multiplexing and beamforming result in comparable localization errors. At lower SNR values, spatial multiplexing leads to larger errors compared to beamforming due to formation of spurious peaks in the cross ambiguity function. Accuracy of the estimated parameters is improved by employing an extended Kalman filter leading to a root mean square (RMS) localization error of approximately 6.3 meters