Accurate indoor drone four degrees-of-freedom (4DoF) estimation, including both 3D position and heading, is essential for safe flight in GNSS-denied environments, opening up opportunities for fully autonomous IoT applications such as infrastructure inspection and surveillance. Approaches using a built-in camera are attractive due to their low cost and ease of deployment, however, existing methods face key limitations. Visual-Inertial Odometry suffers from drift and often fails in textureless environments, while Visible Light Positioning requires a direct line of sight to ceiling LEDs, which is challenging for drones with a limited field of view. We propose ReFDrone, a system that simultaneously estimates 3D position and heading from a single image using a downward-facing rolling-shutter (RS) landing camera and floor-reflected light from modulated ceiling LEDs. Our core insight is that RS distortion produces spectral lobes in the frequency domain whose orientation directly encodes the angle of arrival (AoA) of the LED. Leveraging this property, ReFDrone extracts AoA from a single RS image, enabling estimation of 2D position and heading from just two LEDs, and derives altitude using a virtual-view image with a distance model. We evaluated ReFDrone under 1296 conditions in 2.5 m x 2.5 m testbed, achieving a 90th percentile 3D position error of 0.335 m and a heading error of 8.101 degrees, demonstrating its accuracy and practicality.

Smartphone positioning utilizing a single indoor speaker and its reflections offers the advantage of low deployment cost. However, unlike the direct wave, reflected waves are not always stably detectable, even under Line-of-Sight (LOS) conditions, leading to inconsistent positioning performance. Additionally, associating each detected reflection with its corresponding virtual speaker poses a challenge. This study proposes a method to improve positioning performance by tracking the smartphone based on hypotheses about the identity of each detected reflection. Evaluations were conducted in a real-world environment along both rectangular and circular paths, using only the smartphone’s bottom built-in microphone. The results demonstrated 90th percentile errors of 0.62 m and 0.77 m for the rectangular and circular paths, respectively. This accuracy, approximately equivalent to a step length, suggests potential for navigation applications. These results represent error reductions of 55% and 46%, respectively, compared to conventional approaches.

In this paper, we propose an occlusion-resilient Ultra-Wideband (UWB) localization system by rethinking anchor placement. Typically, anchors are installed near the ceiling for broader line-of-sight (LOS) coverage. However, in crowded environments, practical ceiling heights often fail to maintain stable LOS. By placing anchors near the floor and leveraging ceiling reflections, we create a virtual anchor that extends LOS coverage. Experiments show our approach outperforms ceiling-mounted solutions in accuracy and resilience, opening new possibilities for practical UWB localization.

High-accuracy indoor positioning systems provide various location-aware applications, enhancing our daily experiences. The Global Navigation Satellite System is difficult to use indoors, leading to the development of various indoor positioning methods. Among these, acoustic fingerprint-based positioning, which utilizes widely available speakers and microphones, provides robust performance in Non-Line-of-Sight (NLOS) environments that are common due to structural obstacles and furniture. These approaches typically use Received Signal Strength Indicator or Power Spectral Density as location fingerprints but face challenges in achieving high positioning accuracy. In this paper, we propose Geometric Sound Profile (GSP), a temporal feature of complex reflection waves, enabling high-accuracy localization with a single speaker. GSP, defined as the envelope of cross-correlation between the transmitted and received signals, starting from the transmission time, serves as a highly informative feature encapsulating the multipath-Time-of-Flight. Additionally, we implement a Convolutional Neural Network for the estimation of the user’s position using GSP. We generated a high-resolution pre-trained model in the simulation and fine-tuned it with measurement data, allowing accurate positioning with minimal measured training data. Our experiments demonstrated that the median positioning error was 0.14 m and the 90th percentile error was 1.23 m in the Line-of-Sight environment, and the median positioning error was 0.09 m and the 90th percentile error was 1.14 m in the NLOS environment.

We present a visible light positioning (VLP) method that allows indoor positioning using a smartphone camera with a narrow field of view (FoV). Our method employs omni-directional LEDs, each modulated at a unique frequency, mounted on the ceiling. The system calculates the 2-D position and azimuth of the smartphone by analyzing the brightness of ceiling reflection captured by the user’s smartphone front camera. This approach does not necessitate a direct line-of-sight to the LEDs, effectively overcomes the FoV limitations, and enables positioning over a wider space. We model the ceiling reflection as a transmission path between the LEDs and the ceiling, improving coverage and accuracy of estimation for the user’s position and azimuth compared to previous work. In experiments with two LEDs placed 2.6 m apart, our method covered a space over ten times larger than traditional methods, achieving coverage of $50 \mathbf{m}^{2}$ with a mean absolute error (MAE) in the positioning of 0.46 m and an MAE in azimuth estimation of 13.0°.

Low-cost and accurate indoor location information can add spatiotemporal context to information systems, enabling new location-aware applications. Time-of-Flight (ToF)-based acoustic localization using speakers and microphones allows for localization accuracy within a few tens of centimeters, outperforming RF-based techniques. However, ToF-based localization requires synchronization between the speaker and microphone, i.e., the time offset between them must be known. Previous time offset estimation methods required custom hardware for speakers, limiting their practical use. Estimating the time offset using a single, unmodified speaker is essential for leveraging widely deployed speakers and enhancing coverage. This paper presents the first method for time offset estimation using a single speaker and a microphone, enabled by two key factors: (i) a time offset computation method that utilizes higher-order floor-ceiling reflections as multiple geometrically-constrained virtual speakers, and (ii) a signal processing pipeline that isolates these critical reflections from numerous others by leveraging the speaker's frequency-dependent radiation pattern. Experiments show that the proposed technique can achieve time offset estimation with a 90th percentile error of 259 μs at a 5 m distance. Furthermore, we implemented a ToF localization system based on SyncEcho, demonstrating a 11.0 cm localization accuracy with a 90th percentile error.

This study investigates an indoor 3D positioning method for a microphone using a single speaker. The proposed method estimates the distance, azimuth, and elevation of the microphone from the speaker and calculates the 3D position of the microphone. Multiple short signals with different frequency bands were transmitted sequentially at sufficient intervals to avoid the influence of indoor reflected waves. We extracted these signals at the receiver side, and their amplitude spectra were computed. The azimuth and elevation of the microphone were estimated by matching the obtained amplitude spectrum with the reference data previously measured at each azimuth and elevation. The distance was estimated from amplitude attenuation. The 3D positioning performance was evaluated at ten points in a practical environment. The results show that the 90-th percentile value of the error was 0.583 m.

Prediction of the Received Signal Strength Indicator (RSSI) distribution is a very important task. However, most of the current research is on methods that complement the RSSI distribution for beacons that actually collect data. The most common method for fully online simulation without beacons is based on physical Ray-Tracing. However, the Ray-Tracing model requires the determination of the attenuation rate of the wall. This makes it difficult for ordinary people to perform the simulation. Also, without measuring the actual RSSI, it is impossible to know whether the simulation results are appropriate for the actual environment or not. To address these issues, we propose a data-driven RSSI simulation method. RSSI can be collected by various devices, and it is easy for the general public to obtain RSSI for each location. The simulation is based on the actual RSSI data, so that the simulation can be performed in a realistic environment. In this paper, we have realized a data-driven simulation that matches the environment by learning the attenuation of RSSI derived from the environment and actual data using Generative Adversarial Network(GAN). In order to conduct experiments in a real environment, the simulation model is trained in an office environment, and its accuracy is evaluated using actual RSSI values. As a result, the average absolute error of RSSI values was improved by 8% and the average positioning error of indoor localization was improved by 19% compared with the simulation using the radio propagation formula.

Robust indoor positioning systems provide stable location-aware applications, enhancing our daily experiences. Fingerprint-based positioning techniques enable estimation of a user's position in complex indoor environments. While previous studies have used the received signal strength indicator or power spectral density as fingerprints, they typically achieved only submeter accuracy. This paper presents Geometric Sound Profile (GSP) as a novel location fingerprint to elevate the performance ceiling of fingerprint-based positioning. GSP is derived from the cross-correlation of transmitted and received signals based on transmission time, and a user's position is computed using weighted k-nearest neighbors. Our experiments demonstrate a median error of 0.66 m, marking a significant advancement over previous fingerprinting techniques.

Positioning with a small number of anchors is an important issue in the study of acoustic indoor positioning systems (AIPS). We present PT-Sync, a novel approach to time-synchronize between a commercial off-the-shelf (COTS) speaker and a mobile device by leveraging acoustic sensing and reflected signals from the floor. PT-Sync enables ranging with one speaker and 2-D positioning with two speakers without any additional hardware. Our proposed time synchronization method requires height information from the floor in the calculation process, which we estimate by active acoustic sensing. Using a unique averaging technique and IMU enables robust height estimation. PT-Sync can be used in a variety of indoor environments and can be time-synchronized with already installed speakers. The results of the evaluation experiment confirmed that a synchronization error of 0.16 ms was achieved even at a distance of 6 m from the speaker, and that synchronization on the order of microseconds could be achieved. Furthermore, pedestrian tracking experiments confirmed that positioning of less than 38.9 cm can be achieved at the 90th percentile.

Users and operators of shared spaces must ensure safety in such areas to prevent the spread of COVID-19. Although each organization has operated a variety of safety-related systems, including contact tracing, congestion monitoring, and check-in services, it is unclear what elements, such as privacy protection level, benefits, and permission procedures, have promoted the usage of these systems. In this study, we created MOCHA, a platform for sharing and tracking room-level locations. This platform automatically detects visited places by scanning Bluetooth beacons in each room using smartphones and shares location data according to predefined user settings. The collected data is used for room-level contact tracing, congestion monitoring, and reservation services. According to >6,500 users' usage data for a year in a university, outlining the advantages of utilizing the app encouraged people to install the app, and reinforced connections in small private groups are encouraged to use the app continuously.

When customers wait their turn to place an order at a street vendor, they often form a spontaneous queue. Due to the presence of passers-by and people standing outside the queue, it is more difficult than one might think to distinguish between those in the queue and those not in the queue. In this paper, we consider a method that uses acoustic ranging to autonomously detect who is in line and in which order, under the condition that all customers have smartphones. The proposed method is unique in that it can distinguish whether a newly arrived user has joined the end of the queue or not by taking cues from the geometric properties of the queue. Our preparatory queueing simulations confirm that 92.5% of the queuers are estimated correctly.

Indoor localization technology is one of the most important topics in the fields of Internet of Things (IoT) and ubiquitous computing, and it has attracted much attention in recent years due to its ability to enable a variety of services. Localization methods using Bluetooth or Wi-Fi signal strength can introduce a low-cost location estimation system. However, due to the instability of the received signal strength and signals leaking from adjacent rooms, a simple method based on signal strength alone frequently results in misjudgment, depending on the signal propagation characteristics. In this paper, we propose a method to suppress misjudgment by considering the characteristics of stay time in different rooms. Our proposed method estimates the user state by fitting the distribution of time spent in each room to a Weibull distribution and applying survival analysis. The experimental results suggest that the method will provide more accurate information about the rooms in which users stay.

The advancement of semiconductor and battery technologies popularized tiny acoustic wearable devices such as bone conduction wireless headsets. However, this small form factor poses inconvenience when controlling these devices, as they cannot equip large footprint intuitive interfaces such as volume sliders and touch screens. This paper presents a technique using acoustic responses measured by a bone conduction speaker and a microphone to utilize the ear as a touch input interface. We discovered that a finger placed on different parts of the ear affects the acoustic radiation characteristic of the ear, modulating the leaked sound, and by leveraging this effect, the touch position can be estimated. Experimental results show that five distinct frequency responses with five different finger positions can be obtained, which indicates that our method could allow bone conduction headsets to capture continuous finger positions without additional hardware.

In this paper, we describe NL-Beep, which is a novel ranging method between multiple smartphones using acoustic sensing in non-line-of-sight (NLOS) environments. We need to know the distance between people to maintain social distancing because of the COVID-19 pandemic. Acoustic-based ranging methods that use a smartphone built-in speaker and microphone represent one approach to high-accuracy ranging methods. These methods can be used in line-of-sight (LOS) environments where there are no obstacles between the smartphones. However, in daily life, there are usually many obstacles, including pedestrians, and NLOS environments are the norm rather than the exception. Therefore, we propose the NL-Beep system for NLOS environments. To measure the distance between two smartphones without using a direct signal, NL-Beep efficiently uses a reflected signal from the ceiling in an indoor environment. It can also detect LOS and NLOS environments based on the profile of the received signal, and adapt the method used to estimate the distance between the smartphones. In our experiments, we used two smartphones in a room and estimated the distance between them for several placement configurations. In our results, we obtained 90th-percentile errors of less than 11.97 cm for the distances between the smartphones. In this paper, we also mention the effectiveness and limitations of NL-Beep.

In this paper, we propose a 2D localization method using a single speaker and a microphone. Our proposed method estimates the direction of the microphone based on the knowledge that the spectral amplitude of a speaker in each direction is different. Additionally, the distance between the speaker and microphone is estimated based on the received amplitude. The 2D location of the microphone is calculated from the estimated direction and distance values. We conducted real-environment experiments for two different types of speakers and confirmed that the 90-percentile localization error was 0.265 m. We also discuss the systematic errors in the distance estimation that occurred in the experiment, revealing that these errors were caused by the characteristics of the speakers.

The following topics are dealt with: indoor radio; indoor navigation; wireless LAN; smart phones; learning (artificial intelligence); pedestrians; Global Positioning System; radionavigation; mobile computing; Kalman filters.

In this paper, we describe a novel position-recognition method that uses passive acoustic signals from two previously installed speakers (passive acoustic sensing) and active acoustic signals from a smartphone's loudspeakers (active acoustic sensing). In passive acoustic sensing, a locus of positions for the smartphone can be calculated from the measured time difference of arrival from the two installed speakers. In active acoustic sensing, a chirp signal is transmitted from the speakers of the smartphone, and the distance to the side wall is measured from the propagation time of arrival at its microphone. We can obtain the smartphone position from our proposed model equations by combining these two results. In our experiments, we installed speakers at intervals of 10 m along a corridor and estimated the smartphone position at several places. From these results, we obtained 90th percentile errors of less than 0.224 m for 2-D positioning. We found that multipaths from the side wall were causing the positioning error in passive acoustic sensing, and the variance of the positioning error using the top microphone which was omnidirectional was smaller than the bottom one. When we introduced a weighting based on the result of the active acoustic sensing and the difference in the performance between microphones, the 90th percentile errors were reduced to less than 0.134 m.

Many research studies have been conducted to help simultaneous code changes on multiple code fragments. Code clones and logical couplings are often utilized in such research studies. However, most of them have been evaluated on only open source projects or students' software. In this paper, we report our academic-industrial collaboration with a software company. The collaboration is intended to suggest multiple code fragments to be changed simultaneously when a developer specifies a keyword such as variable names on source code. In the collaboration, we propose to use code clones and logical couplings information to reorder the code fragments. We confirmed that code clones and logical couplings worked well on helping simultaneous code changes on three projects that have being developed in the company.

Programmers often copy and paste code fragments when they would like to reuse them. Although copy-and-paste operations enable programmers to realize rapid developments of software systems, it makes code clones. Some clones have negative impacts on software developments. For example, if we modify a code fragment, we have to check whether its clones need the same modification. In this case, programmers often use tools that take a code fragment as input and take its clones as output. However, when programmers use such existing tools, programmers have to open a number of source code and move up/down a scroll bar for browsing the detected clones. In order to reduce the cost of browsing the detected clones, we developed a tool that visualizes clones by using Circle Packing, named ClonePacker. As a result of an experiment with participants, we confirmed that participants using ClonePacker reported the locations of clones faster than an existing tool.

Changing source code is not an easy task. Developers occasionally change source code incorrectly. Such mistakes entail additional cost in having to reedit the source code correctly, and repeated changes themselves can be a vulnerability to software quality. We are conducting research into realizing automated code changing as a countermeasure for human errors. As the first step of this research, we propose a technique to predict the types of program elements deleted and added in a next change to Java methods. This technique is designed to support developers in deciding how to change source code after they have identified a method to be changed. We evaluated predictions using the proposed technique with two thresholds, which are sizes of source code changes. For predictions with the smaller threshold where only a single type of program element was added or deleted, the accuracy of the proposed technique was 74% -- 85%. However, for the larger threshold, where 5 or fewer types of program elements were added or deleted, the accuracy was 44% -- 48%.

This paper introduces a new dataset of clone references, which is a set of correct clones consisting of their locational information with their gapped lines. Bellon's dataset is one of widely used clone datasets. Bellon's dataset contains many clone references, thus the dataset is useful for comparing accuracies among clone detectors. However, Bellon's dataset does not have locational information of gapped lines. Thus, Bellon's benchmark does not evaluate some Type-3 clones correctly. In order to resolve the problem, we added locational information of gapped lines to Bellon's dataset. The new dataset is available at ``http://sdl.ist.osaka-u.ac.jp/~h-murakm/2014_clone_references_with_gaps/''.

A variety of methods detecting code clones has been proposed before. In order to detect gapped code clones, AST-based technique, PDG-based technique, metric-based technique and text-based technique using the LCS algorithm have been proposed. However, each of those techniques has limitations. For example, existing AST-based techniques and PDG-based techniques require costs for transforming source files into intermediate representations such as ASTs or PDGs and comparing them. Existing metric-based techniques and text-based techniques using the LCS algorithm cannot detect code clones if methods or blocks are partially duplicated. This paper proposes a new method that detects gapped code clones using the Smith-Waterman algorithm to resolve those limitations. The Smith-Waterman algorithm is an algorithm for identifying similar alignments between two sequences even if they include some gaps. The authors developed the proposed method as a software tool named CDSW, and confirmed that the proposed method could resolve the limitations by conducting a quantitative evaluation with Bellon's benchmark.

A variety of code clone detection methods have been proposed before now. However, only a small part of them is widely used. Widely-used methods are line-based and token-based ones. They have high scalability because they neither require deep source code analysis nor constructing complex intermediate structures for the detection. High scalability is one of the big advantages in code clone detection tools. On the other hand, line/token-based detections yield many false positives. One of the factors is the presence of repeated instructions in the source code. For example, herein we assume that there are consecutive three printf statements in C source code. If we apply a token-based detection to them, the former two statements are detected as a code clone of the latter two statements. However, such overlapped code clones are redundant and so not useful for developers. In this paper, we propose a new detection method that is free from the influence of the presence of repeated instructions. The proposed method transforms every of repeated instructions into a special form, and then it detects code clones using a suffix array algorithm. The transformation prevents many false positives from being detected. Also, the detection speed remains. The proposed detection method has already been developed as a software tool, FRISC. We confirmed the usefulness of the proposed method by conducting a quantitative evaluation of FRISC with Bellon's oracle.

A 32b 4-way SIMD dual-issue synergistic processor element of a CELL processor is developed with 20.9 million transistors in 14.8mm/sup 2/ using a 90nm SOI technology. CMOS static gates implement the majority of the logic. Dynamic circuits are used in critical areas, occupying 19% of the nonSRAM area. ISA, microarchitecture and physical implementation are tightly coupled to achieve a compact and power efficient design. Correct operation has been observed up to 5.6GHz at 1.4V supply and 56/spl deg/C.

Article Free Access Share on 300MHz design methodology of VU for emotion synthesis Authors: Takayuki Kamei System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Hideaki Takeda System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Yukio Ootaguro System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Takayoshi Shimazawa System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Kazuhiko Tachibana System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Shin'ichi Kawakami Information System Dept., TOSHIBA Microelectoronics Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan Information System Dept., TOSHIBA Microelectoronics Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Seiji Norimatsu EDA and DFT Grp. Micro & Custom LSI Design Dept., Micro & Custom LSI Div., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan EDA and DFT Grp. Micro & Custom LSI Design Dept., Micro & Custom LSI Div., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Fujio Ishihara System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Toshinori Sato System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Hiroaki Murakami System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Nobuhiro Ide System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Yukio Endo System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Akira Aono System LSI development Grp., Toshiba Microelectoronics Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System LSI development Grp., Toshiba Microelectoronics Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile , Atsushi Kunimatsu System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, Japan System ULSI Engineering Lab., TOSHIBA Corp., 580-1, Horikawa-cho, Saiwaiku, Kawasaki 210-8520, JapanView Profile Authors Info & Claims ASP-DAC '00: Proceedings of the 2000 Asia and South Pacific Design Automation ConferenceJanuary 2000 Pages 635–640https://doi.org/10.1145/368434.368847Published:28 January 2000Publication History 4citation178DownloadsMetricsTotal Citations4Total Downloads178Last 12 Months16Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF