Finding 9-1-1 Callers in Tall Buildings

Accurately determining a user’s floor location is essential for minimizing delays in emergency response. This paper presents a floor localization system intended for emergency calls. We aim to provide floor-level accuracy with minimum infrastructure support. Our approach is to use multiple sensors, all available in today’s smartphones, to trace a user’s vertical movements inside buildings. We make three contributions. First, we present a hybrid architecture for floor localization with emergency calls in mind. The architecture combines beacon-based infrastructure and sensor-based dead reckoning, striking the right balance between accurately determining a user’s location and minimizing the required infrastructure. Second, we present the elevator module for tracking a user’s movement in an elevator. The elevator module addresses three core challenges that make it difficult to accurately derive displacement from acceleration. Third, we present the stairway module which determines the number of floors a user has traveled on foot. Unlike previous systems that track users’ foot steps, our stairway module uses a novel landing counting technique

Spontaneous Osteoarthritis in Dogs - Clinical Effects of Single and Multiple Intra-articular Injections of Hyaluronic Acid

Background: The treatments of osteoarthritis (OA) are commonly conservative and multimodal to relieve pain and improve movement. Intra-articular injection of hyaluronic acid (IAHA) has been studied as a treatment option for OA in dogs. IAHA helps restore the viscoelasticity of the synovial fluid and relieves the clinical symptoms of OA. However, the efficacy of IAHA in dogs is still a controversial subject. This study aims to confirm the IAHA effect in dogs with spontaneous OA and to compare the effectiveness depending on the number of injections. Materials, Methods & Results: Thirty dogs with spontaneous OA were assigned to a single injection group (n=17) and a 3-weekly injections group (n=13). Dogs weighing less than 10 kg were injected 1 mL of HA to the OA joint, and more than 10 kg dogs were injected 2 mL of HA. In the case of the 3-weekly injections group, the same amount was administered 3 times at 1-week intervals. After the injection, physical and orthopedic examinations were performed to check for complications. Radiographic OA score was evaluated before and 3 months after the injection to confirm and to evaluate the progression of OA. Clinical symptom evaluations were performed on pre-injection, 1-, 2-, and 3-months post-injection. They consisted of the clinical lameness score by veterinarians and Canine Brief Pain Inventory (CBPI) by owners. Results were compared with unpaired t-test, repeated-measures ANOVA with Tukey’s or Sidak’s multiple comparison test, or Wilcoxon test, with P < 0.05. Patients had a median age of 9 years (range 3 to 16 years) and a bodyweight of 4.8 kg (range 2 to 48 kg). No systemic side effects or major complications were detected during the trial period. IAHA produced temporary pain and discomfort in 6 cases. There was no change in the radiographic OA score before and 3 months after injections in both groups, and the difference between groups was not confirmed. In both groups, the clinical lameness score significantly decreased at 1, 2, 3 months after injection compared with pre-injection. The score was lower at 3 months after the injection than at 1 month. The clinical lameness score had no significant difference between the groups. Similarly, CBPI was all decreased in the single injection group and 3-weekly injections group compared to pre-injection, and the score at 3 months post-injection was lower than at 1 month. No significant differences between the groups were found in CBPI. Discussion: Most studies on the efficacy of IAHA in canine OA have been conducted using an experimental model, so studies on spontaneous canine OA are insufficient. This study confirmed that IAHA improves clinical symptoms such as pain relief and movement improvement in spontaneous OA dogs using CBPI and clinical lameness score. In order to confirm the optimal IAHA protocol, a single IAHA and 3-weekly IAHA were compared. The result shows that clinical symptoms improved in both single and 3-weekly injections groups, but no significant difference was confirmed during the 3-month study period. These findings  may suggest that a single IAHA may have a similar effect to multiple IAHA, and repeated injections are unnecessary. In humans and canine OA models, it is reported that the effect of IAHA was maintained for 6 months. This study showed that the effect of IAHA was maintained for 3 months study period and that clinical symptoms improved at 3 months than at 1 month. In conclusion, these findings suggested that IAHA improves clinical symptoms in dogs with spontaneous OA, and a single IAHA showed a similar effect to 3 weekly IAHA. Keywords: canine, treatment, hyaluronic acid, intra-articular injection, osteoarthritis

A robust method for VR-based hand gesture recognition using density-based CNN

Many VR-based medical purposes applications have been developed to help patients with mobility decrease caused by accidents, diseases, or other injuries to do physical treatment efficiently. VR-based applications were considered more effective helper for individual physical treatment because of their lowcost equipment and flexibility in time and space, less assistance of a physical therapist. A challenge in developing a VR-based physical treatment was understanding the body part movement accurately and quickly. We proposed a robust pipeline to understanding hand motion accurately. We retrieved our data from movement sensors such as HTC vive and leap motion. Given a sequence position of palm, we represent our data as binary 2D images of gesture shape. Our dataset consisted of 14 kinds of hand gestures recommended by a physiotherapist. Given 33 3D points that were mapped into binary images as input, we trained our proposed density-based CNN. Our CNN model concerned with our input characteristics, having many blank block pixels, single-pixel thickness shape and generated as a binary image. Pyramid kernel size applied on the feature extraction part and classification layer using softmax as loss function, have given 97.7% accuracy

Improving the Vertical Accuracy of Indoor Positioning for Emergency Communication

The emergency communication systems are undergoing a transition from the PSTN-based legacy system to an IP-based next generation system. In the next generation system, GPS accurately provides a user's location when the user makes an emergency call outdoors using a mobile phone. Indoor positioning, however, presents a challenge because GPS does not generally work indoors. Moreover, unlike outdoors, vertical accuracy is critical indoors because an error of few meters will send emergency responders to a different floor in a building. This paper presents an indoor positioning system which focuses on improving the accuracy of vertical location. We aim to provide floor-level accuracy with minimal infrastructure support. Our approach is to use multiple sensors available in today's smartphones to trace users' vertical movements inside buildings. We make three contributions. First, we present the elevator module for tracking a user's movement in elevators. The elevator module addresses three core challenges that make it difficult to accurately derive displacement from acceleration. Second, we present the stairway module which determines the number of floors a user has traveled on foot. Unlike previous systems that track users' foot steps, our stairway module uses a novel landing counting technique. Third, we present a hybrid architecture that combines the sensor-based components with minimal and practical infrastructure. The infrastructure provides initial anchor and periodic corrections of a user's vertical location indoors. The architecture strikes the right balance between the accuracy of location and the feasibility of deployment for the purpose of emergency communication

A robust method for VR-based hand gesture recognition using density-based CNN

Many VR-based medical purposes applications have been developed to help patients with mobility decrease caused by accidents, diseases, or other injuries to do physical treatment efficiently. VR-based applications were considered more effective helper for individual physical treatment because of their low-cost equipment and flexibility in time and space, less assistance of a physical therapist. A challenge in developing a VR-based physical treatment was understanding the body part movement accurately and quickly. We proposed a robust pipeline to understanding hand motion accurately. We retrieved our data from movement sensors such as HTC vive and leap motion. Given a sequence position of palm, we represent our data as binary 2D images of gesture shape. Our dataset consisted of 14 kinds of hand gestures recommended by a physiotherapist. Given 33 3D points that were mapped into binary images as input, we trained our proposed density-based CNN. Our CNN model concerned with our input characteristics, having many 'blank block pixels', 'single-pixel thickness' shape and generated as a binary image. Pyramid kernel size applied on the feature extraction part and classification layer using softmax as loss function, have given 97.7% accuracy

Joint Link Scheduling and Routing for Load Balancing in STDMA Wireless Mesh Networks

In wireless mesh networks, it is known to be effective to use a TDMA based MAC than a contention-based CSMA. In addition, if spatial TDMA is used, network performance can be improved further because of its spatial reuse effect. However this scheme still has a disadvantage in the system performance aspect without a load-balanced routing because the resource of links that are not used is wasted and frequently used links are out of resources. That is, the number of available flows in network is limited because load balancing is not performed. In this paper, we propose joint link scheduling and routing through a cross-layer scheme. For this, we propose a load balancing routing method to maximize available resources under the given traffic pattern and scheduling method for maximizing link utilization on the given route. These two methods are iterated until an optimized solution can be obtained. The proposed algorithm can be formulated using a mathematical LP problem and we show that it is very effective for load balancing compared to simple adoption of IEEE 802.11s which is a standard TDMA protocol in wireless mesh network. If the proposed algorithm is applied to initial design solution such as Smart Grid, the number of available flows can be increased and the load on each link can be balanced

Preparative Synthesis of dTDP-L-Rhamnose Through Combined Enzymatic Pathways

dTDP-L-rhamnose, an important precursor of O-antigen, was prepared on a large scale from dTMP by executing an one-pot reaction in which six enzymes are involved. Two enzymes, dTDP-4-keto-6-deoxy-D-glucose 3,5-epimerase and dTDP-4-keto-rhamnose reductase, responsible for the conversion of dTDP-4-keto-6-deoxy- D-glucose to dTDP-L-rhamnose, were isolated from their putative sequences in the genome of Mesorhizobium loti, functionally expressed in Escherichia coli, and their enzymatic activities were identified. The two enzymes were combined with an enzymatic process for dTDP-4- keto-6-deoxy-D-glucose involving TMP kinase, acetate kinase, dTDP-glucose synthase, and dTDP-glucose 4,6- dehydratase, which allowed us to achieve a preparative scale synthesis of dTDP-L-rhamnose using dTMP and glucose-1-phosphate as starting materials. About 82% yield of dTDP-L-rhamnose was obtained based on initial dTMP concentration at 20 mM dTMP, 1 mM ATP, 10 mM NADH, 60 mM acetyl phosphate, and 80 mM glucose-1- phosphate. From the reaction with 20 ml volume, approximately 180 mg of dTDP-L-rhamnose was obtained in an overall yield of 60% after two-step purification, that is, anion exchange chromatography and gel filtration for desalting. The purified product was identifiedbyHPLC, ESI-MS,andNMR,showingabout95%purity