To improve throughput in security inspection procedures, a millimeter-wave (mmW) imaging system with a high-throughput operation with reasonable resolution compared to conventional mmW imaging systems is developed. Investigates the distinctive attributes of mmW, including its safe penetration through clothing, the study demonstrates the generation of detailed two-dimensional reconstructions of objects. Through the strategic use of a lens, signal amplitudes and phases are effectively captured, yielding reconstruction images from the signal reflected from the target. Experimental validations further affirm the effectiveness of mmW imaging with a dielectric lens, showcasing successful reconstructions of targets positioned at the lens’s front focal plane. Notably, the approach exhibits proficiency in discerning objects obscured behind non-metallic materials such as paper and cloth. These findings highlight the potential of utilizing Fourier transform analysis and a dielectric lens in mmW imaging, presenting a promising approach for security applications, particularly in the detection of concealed objects.

This letter proposes a method that can track the movement of noise sources in fixed filter ANC and virtual sensing ANC systems by using source localization with multiple microphones. Since the optimal noise control filter depends on the location of the noise source, the proposed system prepares optimal noise control filters in advance for multiple locations where the noise is expected to move. The noise source location is then identified using the noise source localization method during the operation of the ANC system, and the appropriate noise control filter is selected according to the location. Simulation results using actual impulse responses show that a noise reduction of approximately 20 dB is possible even if the noise source moves.

This paper proposes two VLSI implementation approaches for periods estimation hardware of periodic signals. Digital signal processing is one of the important technologies, and to estimate periods of signals are widely used in many areas such as IoT, predictive maintenance, anomaly detection, health monitoring, and so on. This paper focuses on accumulation for real-time serial-to-parallel converter (ARS) which is a simple parameter estimation method for periodic signals. ARS is simple algorithm to estimate periods of periodic signals without complex instructions such as multiplier and division. However, this algorithm is implemented only on software, suitable hardware implementation methods are not clear. Therefore, this paper proposes two VLSI implementation methods called ARS-DFF and ARS-MEM. ARS-DFF is simple and fast implementation method, but hardware scale is large. ARS-MEM reduces hardware scale by introducing an SRAM macro cell. This paper also designs both approaches using SystemVerilog and evaluates VLSI implementation. According to our evaluation results, both proposed methods can reduce the power consumption to less than 1/1000 compared to the implementation on a microprocessor.

In cloud computing environments with virtual machines (VMs), we propose a VM placement (VMP) method based on traffic estimation to balance loads due to traffic volumes within physical hosts (PHs) and passing through physical network interface cards (NICs). We refer to a VM or a NIC in a cloud environment as node, and define a flow as a pair of nodes. To balance loads for both PHs and NICs, it is necessary to measure flow traffic volumes because each VM may connect to other VMs in different PHs. However, this is not a cost-effective way to measure flow traffic volumes because the number of flows increases with O(N2) for the number N of nodes. To solve this problem, we propose a VMP method using a compressed sensing (CS)-based traffic estimator. In the proposed method, the relationship between flow traffic volumes and node traffic volumes is formulated by a system of underdetermined linear equations. The flow traffic volumes are estimated with CS from the measured node traffic volumes. From the estimated flow traffic volumes, each VM is assigned to the optimal host for load balancing by solving a mixed-integer optimization problem.

A binarized neural network (BNN) inference accelerator is designed in which weights are stores in loadless four-transistor static random access memory (4T SRAM) cells. A time-multiplexed exclusive NOR (XNOR) multiplier with switched capacitors is proposed which prevents the loadless 4T SRAM cell from being destroyed in the operation. An accumulator with current sensing scheme is also proposed to make the multiply-accumulate operation (MAC) completely linear and read-disturb free. The BNN inference accelerator is applied to the MNIST dataset recognition problem with accuracy of 96.2% for 500 data and the throughput, the energy efficiency and the area efficiency are confirmed to be 15.50 TOPS, 72.17 TOPS/W and 50.13 TOPS/mm2, respectively, by HSPICE simulation in 32 nm technology. Compared with the conventional SRAM cell based BNN inference accelerators which are scaled to 32 nm technology, the synapse cell size is reduced to less than 16% (0.235 μm2) and the cell efficiency (synapse array area/synapse array plus peripheral circuits) is 73.27% which is equivalent to the state-of-the-art of the SRAM cell based BNN accelerators.

There has been a growing interest in the application of radar technology to the monitoring of humans and animals and their positions, motions, activities, and vital signs. Radar can be used, for example, to remotely measure vital signs such as respiration and heartbeat without contact. Radar-based human sensing is expected to be adopted in a variety of fields, such as medicine, healthcare, and entertainment, but what can be realized by radar-based animal sensing? This paper reviews the latest research trends in the noncontact sensing of animals using radar systems. We also present examples of our past radar experiments for the respiratory measurement of monkeys and the heartbeat measurement of chimpanzees. The trends in this field are reviewed in terms of the target animal species, type of vital sign, and radar type and selection of frequencies.

Recent years have seen a general resurgence of interest in analog signal processing and computing architectures. In addition, extensive theoretical and experimental literature on chaos and analog chaotic oscillators exists. One peculiarity of these circuits is the ability to generate, despite their structural simplicity, complex spatiotemporal patterns when several of them are brought towards synchronization via coupling mechanisms. While by no means a systematic survey, this paper provides a personal perspective on this area. After briefly covering design aspects and the synchronization phenomena that can arise, a selection of results exemplifying potential applications is presented, including in robot control, distributed sensing, reservoir computing, and data augmentation. Despite their interesting properties, the industrial applications of these circuits remain largely to be realized, seemingly due to a variety of technical and organizational factors including a paucity of design and optimization techniques. Some reflections are given regarding this situation, the potential relevance to discontinuous innovation in analog circuit design of chaotic oscillators taken both individually and as synchronized networks, and the factors holding back the transition to higher levels of technology readiness.

For flight simulators, it is crucial to create three-dimensional terrain using clear remote sensing images. However, due to haze and other contributing variables, the obtained remote sensing images typically have low contrast and blurry features. In order to build a flight simulator visual system, we propose a deep learning-based dehaze model for remote sensing images dehazing. An encoder-decoder architecture is proposed that consists of a multiscale fusion module and a gated large kernel convolutional attention module. This architecture can fuse multi-resolution global and local semantic features and can adaptively extract image features under complex terrain. The experimental results demonstrate that, with good generality and application, the model outperforms existing comparison techniques and achieves high-confidence dehazing in remote sensing images with a variety of haze concentrations, multi-complex terrains, and multi-spatial resolutions.

A wireless sensor terminal module of 5cc size (2.5 cm × 2.5 cm × 0.8 cm) that does not require a battery is proposed by integrating three kinds of circuit technologies. (i) a low-power sensor interface: an FM modulation type CMOS sensor interface circuit that can operate with a typical power consumption of 24.5 μW was fabricated by the 0.7-μm CMOS process technology. (ii) power supply to the sensor interface circuit: a wireless power transmission characteristic to a small-sized PCB spiral coil antenna was clarified and applied to the module. (iii) wireless sensing from the module: backscatter communication technology that modulates the signal from the base terminal equipment with sensor information and reflects it, which is used for the low-power sensing operation. The module fabricated includes a rectifier circuit with the PCB spiral coil antenna that receives wireless power transmitted from base terminal equipment by electromagnetic resonance coupling and converts it into DC power and a sensor interface circuit that operates using the power. The interface circuit modulates the received signal with the sensor information and reflects it back to the base terminal. The module could achieve 100 mm communication distance when 0.4 mW power is feeding to the sensor terminal.

Wi-Fi-based person identification (PI) tasks are performed by analyzing the fluctuating characteristics of the Channel State Information (CSI) data to determine whether the person's identity is legitimate. This technology can be used for intrusion detection and keyless access to restricted areas. However, the related research rarely considers the restricted computing resources and the complexity of real-world environments, resulting in lacking practicality in some scenarios, such as intrusion detection tasks in remote substations without public network coverage. In this paper, we propose a novel neural network model named SimpleViTFi, a lightweight classification model based on Vision Transformer (ViT), which adds a downsampling mechanism, a distinctive patch embedding method and learnable positional embedding to the cropped ViT architecture. We employ the latest IEEE 802.11ac 80MHz CSI dataset provided by [1]. The CSI matrix is abstracted into a special “image” after pre-processing and fed into the trained SimpleViTFi for classification. The experimental results demonstrate that the proposed SimpleViTFi has lower computational resource overhead and better accuracy than traditional classification models, reflecting the robustness on LOS or NLOS CSI data generated by different Tx-Rx devices and acquired by different monitors.

In this paper, we introduce a framework of distributed orthogonal approximate message passing for recovering sparse vector based on sensing by multiple nodes. The iterative recovery process consists of local computation at each node, and global computation performed either by a particular node or joint computation on the overall network by exchanging messages. We then propose a method to reduce the communication cost between the nodes while maintaining the recovery performance.

Compressed sensing is a rapidly growing research field in signal and image processing, machine learning, statistics, and systems control. In this survey paper, we provide a review of the theoretical foundations of compressed sensing and present state-of-the-art algorithms for solving the corresponding optimization problems. Additionally, we discuss several practical applications of compressed sensing, such as group testing, sparse system identification, and sparse feedback gain design, and demonstrate their effectiveness through Python programs. This survey paper aims to contribute to the advancement of compressed sensing research and its practical applications in various scientific disciplines.

A city-level digital twin is a critical enabling technology to construct a smart city that helps improve citizens' living conditions and quality of life. Currently, research and development regarding the digital replica city are pursued worldwide. However, many research projects only focus on creating the 3D city model. A mechanism to involve key players, such as data providers, service providers, and application developers, is essential for constructing the digital replica city and producing various city applications. Based on this motivation, the authors of this paper are pursuing a research project, namely Decentralized Digital Twin EcoSystem (D2EcoSys), to create an ecosystem to advance (and self-grow) the digital replica city regarding time and space directions, city services, and values. This paper introduces an overview of the D2EcoSys project: vision, problem statement, and approach. In addition, the paper discusses the recent research results regarding networking technologies and demonstrates an early testbed built in the Kashiwa-no-ha smart city.

A group signature scheme allows us to anonymously sign a message on behalf of a group. One of important issues in the group signatures is user revocation, and thus lots of revocable group signature (RGS) schemes have been proposed so far. One of the applications suitable to the group signature is privacy-enhancing crowdsensing, where the group signature allows mobile sensing users to be anonymously authenticated to hide the location. In the mobile environment, verifier-local revocation (VLR) type of RGS schemes are suitable, since revocation list (RL) is not needed in the user side. However, in the conventional VLR-RGS schemes, the revocation check in the verifier needs O(R) cryptographic operations for the number R of revoked users. On this background, VLR-RGS schemes with efficient revocation check have been recently proposed, where the revocation check is just (bit-string) matching. However, in the existing schemes, signatures are linkable in the same interval or in the same application-independent task with a public index. The linkability is useful in some scenarios, but users want the unlinkability for the stronger anonymity. In this paper, by introducing a property that at most K unlinkable signatures can be issued by a signer during each interval for a fixed integer K, we propose a VLR-RGS scheme with the revocation token matching. In our scheme, even the signatures during the same interval are unlinkable. Furthermore, since used indexes are hidden, the strong anonymity remains. The overheads are the computational costs of the revocation algorithm and the RL size. We show that the overheads are practical in use cases of crowdsensing.

Radar-based sensing and concealed weapon detection technologies have been attracting attention as a measure to enhance security screening in public facilities and various venues. For these applications, the security check must be performed without impeding the flow of people, with minimum human effort, and in a non-contact manner. We developed technologies for a high-throughput walk-through security screening called Invisible Sensing (IVS) and implemented them in a prototype system. The IVS system consists of dual planar radar panels facing each other and carries out an inspection based on a multi-region screening approach as a person walks between the panels. Our imaging technology constructs a high-quality radar image that compensates for motion blur caused by a person's walk. Our detection technology takes multi-view projected images across the multiple regions as input to enable real-time whole-body screening. The IVS system runs its functions by pipeline processing to achieve real-time screening operation. This paper presents our IVS system along with these key technologies and demonstrates its empirical performance.

The degree of reception of BS signals is affected by various factors. After routinely recording it at two observation points at two locations, we found that momentary upward and downward level shifts occurred multiple times, mainly during daytime. These level shifts were observed at one location. No such signal was sensed at the other location. After producing an algorithm to extract such momemtary level shifts, their statistical properties were investigated. Careful analyses, including assessment of the signal polarity, amplitude, duration, hours, and comparison with actual flight schedules and route information implied that these level shifts are attributable to the interference of direct and reflected waves from aircraft flying at approximately tropopause altitude. This assumption is further validated through computer simulations of BS signal interference.

A super-resolution algorithm is proposed to solve the problem of measuring the thin thickness of oil slick using compressed sensing theory. First, a mathematical model of a single pulse underwater ultrasonic echo is established. Then, the estimation model of the transmit time of flight (TOF) of ultrasonic echo within oil slick is given based on the sparsity of echo signals. At last, the super-resolution TOF value can be obtained by solving the sparse convex optimization problem. Simulations and experiments are conducted to validate the performance of the proposed method.

In the massive machine-type communication scenario, aiming at the problems of active user detection and channel estimation in the grant-free non-orthogonal multiple access (NOMA) system, new sets of non-orthogonal spreading sequences are proposed by using the zero/low correlation zone sequence set with low correlation among multiple sets. The simulation results show that the resulting sequence set has low coherence, which presents reliable performance for channel estimation and active user detection based on compressed sensing. Compared with the traditional Zadoff-Chu (ZC) sequences, the new non-orthogonal spreading sequences have more flexible lengths, and lower peak-to-average power ratio (PAPR) and smaller alphabet size. Consequently, these sequences will effectively solve the problem of high PAPR of time domain signals and are more suitable for low-cost devices in massive machine-type communication.

In order to improve crop production and efficiency of farming operations, an IoT (Internet of Things) system for remote monitoring has been attracting a lot of attention. The existing studies have proposed agricultural sensing systems such that environmental information is collected from many sensor nodes installed in farmland through wireless communications (e.g., Wi-Fi, ZigBee). Especially, Low-Power Wide-Area (LPWA) is a focus as a candidate for wireless communication that enables the support of vast farmland for a long time. However, it is difficult to achieve long distance communication even when using the LPWA because a clear line of sight is difficult to keep due to many obstacles such as crops and agricultural machinery in the farmland. In addition, a sensor node cannot run permanently on batteries because the battery capacity is not infinite. On the other hand, an Unmanned Aerial Vehicle (UAV) that can move freely and stably in the sky has been leveraged for agricultural sensor network systems. By utilizing a UAV as the gateway of the sensor network, the gateway can move to the appropriate location to ensure a clear line of sight from the sensor nodes. In addition, the coverage area of the sensor network can be expanded as the UAV travels over a wide area even when short-range and ultra-low-power wireless communication (e.g., Bluetooth Low Energy (BLE)) is adopted. Furthermore, various wireless technologies (e.g., wireless power transfer, wireless positioning) that have the possibility to improve the coverage area and the lifetime of the sensor network have become available. Therefore, in this study, we propose and develop two kinds of new agricultural sensing systems utilizing a UAV and various wireless technologies. The objective of the proposed system is to provide the solution for achieving the wide-area and long-term sensing for the vast farmland. Depending on which problem is in a priority, the proposed system chooses one of two designs. The first design of the system attempts to achieve the wide-area sensing, and so it is based on the LPWA for wireless communication. In the system, to efficiently collect the environmental information, the UAV autonomously travels to search for the locations to maintain the good communication properties of the LPWA to the sensor nodes dispersed over a wide area of farmland. In addition, the second design attempts to achieve the long-term sensing, so it is based on BLE, a typical short-range and ultra-low-power wireless communication technology. In this design, the UAV autonomously flies to the location of sensor nodes and supplies power to them using a wireless power transfer technology for achieving a battery-less sensor node. Through experimental evaluations using a prototype system, it is confirmed that the combination of the UAV and various wireless technologies has the possibility to achieve a wide-area and long-term sensing system for monitoring vast farmland.

Crowdsensing-based spectrum detection (CSD) is promising to enable full-coverage radio resource availability for the increasingly connected machines in the Internet of Things (IoT) networks. The current CSD scheme consumes a lot of energy and network resources for local sensing, processing, and distributed data reporting for each crowdsensing device. Furthermore, when the amount of reported data is large, the data fusion implemented at the requestor can easily cause high latency. For improving efficiencies in both energy and network resources, this paper proposes a green CSD (GCSD) paradigm. The ambient backscatter (AmB) is used to enable a battery-free mode of operation in which the received spectrum data is reported directly through backscattering without local processing. The energy for backscattering can be provided by ambient radio frequency (RF) sources. Then, relying on air computation (AirComp), the data fusion can be implemented during the backscattering process and over the air by utilizing the summation property of wireless channel. This paper illustrates the model and the implementation process of the GCSD paradigm. Closed-form expressions of detection metrics are derived for the proposed GCSD. Simulation results verify the correctness of the theoretical derivation and demonstrate the green properties of the GCSD paradigm.