Storing large volumes of traffic video content in cloud storage is an expensive undertaking, given the limited capacity of cloud storage and its inability to store data beyond a few weeks. To address this issue, this article introduces CLOUD-CODEC , a novel video encoding approach tailored specifically for traffic monitoring video. CLOUD-CODEC offers three key advantages: (i) real-time encoding without any delay, (ii) near-perfect video quality upon decoding, and (iii) one-fifth the storage size of traditional encoding methods. CLOUD-CODEC is generally applicable to traffic cameras under various weather and lighting conditions. The encoding algorithm is a lightweight DNN-based object detection and box-shaped segmentation approach. The method can uniquely detect and segment cars, pedestrians, and moving objects with the marginal box-shaped contours. Periodic object detection makes it possible for CLOUD-CODEC to operate in real-time and estimate the movement of objects between predictions. Proof-of-concept evaluations using a massive dataset indicate that CLOUD-CODEC reduces video size by 80%—surpassing AV1 (34.9%), CloudSeg (58.4%), Detection (76.9%), Segmentation (73.1%), and Segm&Sort (69.5%). It achieves a frame rate of 95.8 when encoding and a VMAF score of 72.54 after decoding, with a storage size that is one-fifth of traditional methods. Field-testing of CLOUD-CODEC on metropolitan traffic cameras demonstrates its ability to extend storage time by 74.92%.

Training and validating models for semantic segmentation require datasets with pixel-wise annotations, which are notoriously labor-intensive. Although useful priors such as foundation models or crowdsourced datasets are available, they are error-prone. We hence propose an effective framework of active label correction (ALC) based on a design of correction query to rectify pseudo labels of pixels, which in turn is more annotator-friendly than the standard one inquiring to classify a pixel directly according to our theoretical analysis and user study. Specifically, leveraging foundation models providing useful zero-shot predictions on pseudo labels and superpixels, our method comprises two key techniques: (i) an annotator-friendly design of correction query with the pseudo labels, and (ii) an acquisition function looking ahead label expansions based on the superpixels. Experimental results on PASCAL, Cityscapes, and Kvasir-SEG datasets demonstrate the effectiveness of our ALC framework, outperforming prior methods for active semantic segmentation and label correction. Notably, utilizing our method, we obtained a revised dataset of PASCAL by rectifying errors in 2.6 million pixels in PASCAL dataset.

The cable-driven redundant manipulator (CDRM) has significant potential for applications in narrow and hazardous spaces. However, traditional CDRMs have limited stiffness and load capacity due to their cable routing method. To address these limitations, several scholars have proposed new mechanisms and control strategies. Nevertheless, the cable routing method has not changed, and CDRMs continue to suffer from their limitations. Recently, a nematode-inspired cable routing method was proposed; however, stiffness calculations, derivation of inverse kinematics, and validation of stiffness and load capacity were incomplete. In this paper, we calculate the analytic equivalent stiffness of the nematode-inspired cable routing method and compare it with other cable routing methods. Additionally, we derived and simulate the kinematics and an effective inverse kinematics algorithm. Finally, we validate the stiffness and load capacity using a developed prototype.

Learning semantic segmentation requires pixel-wise annotations, which can be time-consuming and expensive. To reduce the annotation cost, we propose a superpixel-based active learning (AL) framework, which collects a dominant label per superpixel instead. To be specific, it consists of adaptive superpixel and sieving mechanisms, fully dedicated to AL. At each round of AL, we adaptively merge neighboring pixels of similar learned features into superpixels. We then query a selected subset of these superpixels using an acquisition function assuming no uniform superpixel size. This approach is more efficient than existing methods, which rely only on innate features such as RGB color and assume uniform superpixel sizes. Obtaining a dominant label per superpixel drastically reduces annotators’ burden as it requires fewer clicks. However, it inevitably introduces noisy annotations due to mismatches between superpixel and ground truth segmentation. To address this issue, we further devise a sieving mechanism that identifies and excludes potentially noisy annotations from learning. Our experiments on both Cityscapes and PASCAL VOC datasets demonstrate the efficacy of adaptive superpixel and sieving mechanisms.

Cable driven redundant manipulator (CDRM) can provide complex movements with high dexterity and singularity reduction. However, traditional CDRMs with universal joints have the disadvantages of requiring a high number of motors and having a narrow joint workspace. Furthermore, there is a limitation in terms of stiffness and payload. Recently, CDRMs composed of Quaternion joints have been developed to address these disadvantages. They require fewer motors and have larger joint workspace due to the Quaternion joints. Yet, their cable routing method is the same as the traditional CDRMs. In this paper, we propose a novel nematode-inspired cable routing method to achieve complex movements and stiffness increase. To achieve the stiffness increase of CDRM, the proposed cable routing method was inspired by the alternately arranged muscle structure of nematodes. Moreover, moving pulley structure was selected to amplify the stiffness and force of CDRM. An 8-DOF CDRM prototype composed of four Quaternion joints was developed to show the effectiveness of the cable routing method. Kinematics simulation was conducted and then, verified by trajectory through experiments. Finally, a joint stiffness simulation was conducted and verified with the developed prototype by stiffness experiments.

This paper proposes a new active learning method for semantic segmentation. The core of our method lies in a new annotation query design. It samples informative local image regions ($\textit{e.g.}$, superpixels), and for each of such regions, asks an oracle for a multi-hot vector indicating all classes existing in the region. This multi-class labeling strategy is substantially more efficient than existing ones like segmentation, polygon, and even dominant class labeling in terms of annotation time per click. However, it introduces the class ambiguity issue in training as it assigns partial labels ($\textit{i.e.}$, a set of candidate classes) to individual pixels. We thus propose a new algorithm for learning semantic segmentation while disambiguating the partial labels in two stages. In the first stage, it trains a segmentation model directly with the partial labels through two new loss functions motivated by partial label learning and multiple instance learning. In the second stage, it disambiguates the partial labels by generating pixel-wise pseudo labels, which are used for supervised learning of the model. Equipped with a new acquisition function dedicated to the multi-class labeling, our method outperforms previous work on Cityscapes and PASCAL VOC 2012 while spending less annotation cost. Our code and results are available at [https://github.com/sehyun03/MulActSeg](https://github.com/sehyun03/MulActSeg).

Crowdsourcing systems enable us to collect large-scale dataset, but inherently suffer from noisy labels of low-paid workers. We address the inference and learning problems using such a crowdsourced dataset with noise. Due to the nature of sparsity in crowdsourcing, it is critical to exploit both probabilistic model to capture worker prior and neural network to extract task feature despite risks from wrong prior and overfitted feature in practice. We hence establish a neural-powered Bayesian framework, from which we devise deepMF and deepBP with different choice of variational approximation methods, mean field (MF) and belief propagation (BP), respectively. This provides a unified view of existing methods, which are special cases of deepMF with different priors. In addition, our empirical study suggests that deepBP is a new approach, which is more robust against wrong prior, feature overfitting and extreme workers thanks to the more sophisticated BP than MF.

For immersive interaction in a virtual reality (VR) environment, an omnidirectional treadmill (ODT) can support performance of various locomotive motions (curved walk, side walk, moving with shooting stance) in any direction. When a user performs lateral locomotive motions on an ODT, a control scheme to achieve immersive and safe interaction with the ODT should satisfy robustness in terms of position error of a user to keep a reference position of the ODT by accurately estimating intentional walking speed (IWS) of the user, and it should guarantee postural stability of the user during the control actions. Existing locomotion interface (LI) control focuses on the reference position tracking performance regarding the position of the user's center of mass (COM) in order to respond to forward locomotion that can move at high speed. However, in sideways walking, the movement of the lower extremities is different from that of forward walking, and when the conventional LI control was directly applied to sideways walking, it was observed that excessive acceleration commands caused postural instability. For appropriate interface of sideways walking, we propose an estimation scheme based on an accurate walking model including the movement of the ankle joint. The proposed observer estimates the acting torque generated by the force of both lower extremities through the position information of COM and ankle joint to more accurately predict the user's intentional walking speed (IWS). In the sideways walking experiment conducted using a 1-dimensional user-driven treadmill (UDT), the proposed method allowed more natural interface of the lateral-side locomotion with better postural stability compared to the conventional estimation method that uses only the COM position information.

This paper presents a uGPS (Underground GPS), which is the first SDR (Software Defined Radio)-based GNSS service to commodity GNSS receivers, including the latest iPhone, Android, and Car navigation. The uGPS is a system consisting of SDRs for GNSS signal generation, a GNSS D.O. for nano-second level timing synchronization's economic selection, fiber optic, and leaky feeder for a minimal environmental effect in tunnel environments. The proposed uGPS provides consistent 8 GPS satellites and 4 GLONASS satellites signal through a 1.5-kilometer-long tunnel. The uGPS equipped tunnel is highly compatible with commodity GNSS/GPS receivers. Therefore, it does not require any APP or special hardware. Our benchmark tested package was on top of the FPGA, in which modifications and upgrades were demonstrated via evaluation on three popular GNSS/GPS devices including Android phones, iPhones, and Car navigation systems. Further extensive evaluations demonstrated that the uGPS consistently achieved average location accuracy of 10 meters in 30~70km/h speed driving tests (without a map-matching algorithm), and a seamless handover between live GNSS and the uGPS system.

Mobile apps offer a variety of features that greatly enhance user experience. However, users still often find it difficult to use mobile apps in the way they want. For example, it is not easy to use multiple apps simultaneously on a small screen of a smartphone. In this paper, we present A-Mash, a mobile platform that aims to simplify the way of interacting with multiple apps concurrently to the level of using a single app only. A key feature of A-Mash is that users can mash up the UIs of different existing mobile apps on a single screen according to their preferences. To this end, A-Mash 1) extracts UIs from unmodified existing apps (dynamic UI extraction) and 2) embeds extracted UIs from different apps into a single wrapper app (cross-process UI embedding), while 3) making all these processes hidden from the users (transparent execution environment). To the best of our knowledge, A-Mash is the first work to enable UIs of different unmodified legacy apps to seamlessly integrate and synchronize on a single screen, providing an illusion as if they were developed as a single app. A-Mash offers great potential for a number of useful usage scenarios. For instance, a user can mashup UIs of different IoT administration apps to create an all-in-one IoT device controller or one can mashup today's headlines from different news and magazine apps to craft one's own news headline collection. In addition, A-Mash can be extended to an AR space, in which users can map UI elements of different mobile apps to physical objects inside their AR scenes. Our evaluation of the A-Mash prototype implemented in Android OS demonstrates that A-Mash successfully supports the mashup of various existing mobile apps with little or no performance bottleneck. We also conducted in-depth user studies to assess the effectiveness of the A-Mash in real-world use cases.

Being able to use a single app across multiple devices can bring novel experiences to the users in various domains including entertainment and productivity. For instance, a user of a video editing app would be able to use a smart pad as a canvas and a smartphone as a remote toolbox so that the toolbox does not occlude the canvas during editing. However, existing approaches do not properly support the single-app multi-device execution due to several limitations, including high development cost, device heterogeneity, and high performance requirement. In this paper, we introduce FLUID-XP, a novel cross-platform multi-device system that enables UIs of a single app to be executed across heterogeneous platforms, while overcoming the limitations of previous approaches. FLUID-XP provides flexible, efficient, and seamless interactions by addressing three main challenges: i) how to transparently enable a single-display app to use multiple displays, ii) how to distribute UIs across heterogeneous devices with minimal network traffic, and iii) how to optimize the UI distribution process when multiple UIs have different distribution requirements. Our experiments with a working prototype of FLUID-XP on Android confirm that FLUID-XP successfully supports a variety of unmodified real-world apps across heterogeneous platforms (Android, iOS, and Linux). We also conduct a lab study with 25 participants to demonstrate the effectiveness of FLUID-XP with real users.

While GPS (Global Positioning System) navigation and GPS based autonomous car driving techniques are mature, the security of GPS signal has not been a primary system concern. In fact, it has been shown that an ordinary GPS can be easily spoofed by a low-cost, open-source based software defined radio (SDR) system such as bladeRF and HackRF [3, 4] which can cause serious complications to the navigation system of the car especially for self-driving cars which are driven based on the information from sensors, cameras, and GPS. In this work, we design a new GPS authentication system called lightweight authentication GPS (AuthGPS) to authenticate GPS signal against GPS spoofing and LTE base station broadcast message spoofing. In the past, there have been many successful attempts on GPS spoofing which resulted in shifting the destination of the car to the spoofer's desired location. In a case where the car is connected to the Internet via LTE network, the navigation system can find out if the GPS is spoofed by acquiring the correct GPS satellite information from LTE base stations' location information. However, a skillful attacker can spoof the LTE base station signals [7] as well using another SDR which will produce spoofed LTE base station information. Hence giving wrong information about GPSinformation according to the will of the attacker. Currently, there are defense methods against the GPS spoofing [6]. One of them is signal-processing-based methods. By monitoring unusual or unreasonable signal changes at GPS receivers, GPS spoofing attack can be detected. Received Power Monitoring (RPM) looks at all the received amplitude and automatic gain control (AGC) setpoint [1]. The receiver will sense drastic power jump if a spoofing attack occurs. However, an overly powerful spoofing attack with noise is not detectable in the case of this mechanism. Another way to detect spoofing is the symmetric-key encryption mechanism of GPS signals. According to previous research [2], encrypted precision code for anti-spoofing referred to as P(Y) code, might not be able to be spoofed. A spoofing attack can be detected by calculating cross-correlation between spoofed coarse/acquisition code, referred to as C/A code, and P(Y) code. Unfortunately, this P(Y) code is only for military purpose while the C/A code can be publicly accessible. This method requires knowledge of specific key which is not revealed to the public to decrypt P(Y) code. Monitoring the direction of arrival of the signals can be one of the methods [6]. A GPS receiver measures the direction-of-arrival vector with more than 3 antennas. Even though this signal-geometrybased system with multiple antennas makes GPS robust, an attacker can spoof signals from multiple directions, which is more difficult to detect the spoofing. The idea here is to develop a system that can discriminate the spoofed signal without complex computation or heavy message exchange. An important component of AuthGPS is a verification of valid GPS satellite information via LTE base stations' location information. We propose 6-digit one-time password-based authentication system in this paper.

With significant growth in portable multimedia devices such as smartphones, application processors (AP) play a critical role for running various multimedia applications on these devices. By considering the power constraints of such devices, we often integrate reconfigurable processors (RPs) into APs. This is because RPs offer flexibility and good performance, thereby greatly improving the power efficiency for running these multimedia applications. Like many other processors, RPs also exploit the dynamic voltage/frequency scaling (DVFS) to improve their power efficiency. Owing to the platform cost constraints, however, these RPs are often integrated to low dropout (LDO) voltage regulators (VRs) instead of switching VRs. When compared with switching VRs, LDO VRs are very inexpensive; however, they suffer from considerable power loss when they are required to deliver a low output voltage. However, many previous studies focused on analyzing the power efficiency of various DVFS techniques only with regard to the processors and did not consider the negative impact of the VR power losses on the overall power efficiency of the platform. In this work, we quantitatively compare the power efficiency of a Samsung RP (SRP) adopting the race-to-halt technique with that of the SRP exploiting the DVFS supported by LDO VRs, by considering the effect of the VR power losses. Finally, we demonstrate that using the race-to-halt technique results in high power efficiency when compared with the DVFS in a commercial processor, by considering the VR power efficiency.

In recent years, 3D technology has been a popular area of study that has allowed researchers to explore a number of novel computer architectures. One of the more popular topics is that of integrating 3D main memory dies below the computing die and connecting them with through-silicon vias (TSVs). This is assumed to reduce off-chip main memory access latencies by roughly 45% to 60%. Our detailed circuit-level models, however, demonstrate that this latency reduction from the TSVs is significantly less. In this paper, we present these models, compare 2D and 3D main memory latencies, and show that the reduction in latency from using 3D main memory to be no more than 2.4 ns. We also show that although the wider I/O bus width enabled by using TSVs increases performance, it may do so with an increase in power consumption. Although TSVs consume less power per bit transfer than off-chip metal interconnects (11.2 times less power per bit transfer), TSVs typically use considerably more bits and may result in a net increase in power due to the large number of bits in the memory I/O bus. Our analysis shows that although a 3D memory hierarchy exploiting a wider memory bus can increase performance, this performance increase may not justify the net increase in power consumption.

3D main memory is an emerging technology that stacks DRAM dies underneath the processor die using through-silicon vias (TSVs). Prior studies assumed that such technology would decrease main memory access latency by 45% to 60%, while also allowing designers to increase main memory bandwidth. Although the latter is true, it was recently shown that the latency savings of 3D main memory is only 6.3%. In this paper, we first analyze memory latency reduction opportunities in a 3D main memory system with Wide I/O by taking better advantage of 3D integration technology and quantify their benefit. Specifically, redesigning the DRAM to memory controller synchronizers and placing the address, command, and data pads closer to the DRAM banks can decrease 3D main memory latency by 24.7%. We show that current 3D DRAM with Wide I/O can increase the geometric mean performance of an embedded processor that is similar to a Texas instrument C67x DSP by 9.7% (and up to 23.3%). Second, we observe that 3D DRAM with Wide IO can increase average system energy consumption of energy-constrained embedded DSPs by 2.6% (and up to 8.9%). To improve I/O energy efficiency, we propose to dynamically scale memory bandwidth (i.e. the I/O width) at runtime based on an application's program phases. Our dynamic bandwidth scaling algorithms increase average performance by 6.6% while increasing average energy consumption by only 0.5%.

No abstract available.