KEBUTUHAN RTH UNTUK MENYERAP EMISI CO2 KENDARAAN BERMOTOR PADA KAWASAN JEMBATAN TELUK KENDARI

Emisi karbon dioksida (CO2) yang dihasilkan akibat aktivitas kendaraan bermotor pada kawasan Jembatan Teluk Kendari (JTK) perlu diimbangi dengan ketersediaan ruang terbuka hijau (RTH). Tujuan dari penelitian ini adalah: (a) mengetahui jumlah kendaraan bermotor yang melintasi Jembatan Teluk Kendari, (b) menganalisis konsentrasi emisi CO2 yang dihasilkan oleh kendaraan bermotor di Jembatan Teluk Kendari, dan (c) menganalisis kebutuhan RTH untuk menyerap emisi CO2 yang dihasilkan oleh kendaraan bermotor pada Jembatan Teluk Kendari. Metode yang digunakan pada penelitian ini adalah observasi lapangan, studi literatur, serta survei instansional. Hasil penelitian menunjukkan bahwa selama waktu pengamatan traffic counting dilakukan, tercatat 10476 unit kendaraan melintas pada ruas Jl. Wr. Soepratman (JTK), 11007 unit kendaraan pada Jl.Insinyur Soekarno, 7172 unit kendaraan Jl. Tinumbu-Jl. Beringin III-Jl. Gajah Mada, dan 8677 unit kendaraan pada Jl. Sukowati. Total konsentrasi emisi CO2 yang dihasilkan dari aktivitas kendaraan bermotor adalah 337,09 kg/jam, dan kebutuhan RTH untuk menyerap seluruh emisi dilakukan dengan mengoptimalkan RTH eksisting menjadi RTH berdaya serap 165,995 kg/jam dan mendesainRTH baru berdaya serap 172,943 kg/jam. RTH baru memanfaatkan lahan potensial yang berada pada kawasan Jembatan Teluk Kendari seluas 1,172 ha dengan komposisi 80% dari total luasan tersebut atau 0,937 ha terdiri atas komponen softscape (vegetasi), sedangkan sisanya sebesar 20% atau 0,234 ha terdiri atas komponen hardscape (benda mati)

Achieving Practical and Accurate Indoor Navigation for People with Visual Impairments

Methods that provide accurate navigation assistance to people with visual impairments often rely on instrumenting the environment with specialized hardware infrastructure. In particular, approaches that use sensor networks of Bluetooth Low Energy (BLE) beacons have been shown to achieve precise localization and accurate guidance while the structural modifications to the environment are kept at minimum. To install navigation infrastructure, however, a number of complex and time-critical activities must be performed. The BLE beacons need to be positioned correctly and samples of Bluetooth signal need to be collected across the whole environment. These tasks are performed by trained personnel and entail costs proportional to the size of the environment that needs to be instrumented. To reduce the instrumentation costs while maintaining a high accuracy, we improve over a traditional regression-based localization approach by introducing a novel, graph-based localization method using Pedestrian Dead Reckoning (PDR) and particle filter. We then study how the number and density of beacons and Bluetooth samples impact the balance between localization accuracy and set-up cost of the navigation environment. Studies with users show the impact that the increased accuracy has on the usability of our navigation application for the visually impaired

Off the Beaten Path: Let's Replace Term-Based Retrieval with k-NN Search

Retrieval pipelines commonly rely on a term-based search to obtain candidate records, which are subsequently re-ranked. Some candidates are missed by this approach, e.g., due to a vocabulary mismatch. We address this issue by replacing the term-based search with a generic k-NN retrieval algorithm, where a similarity function can take into account subtle term associations. While an exact brute-force k-NN search using this similarity function is slow, we demonstrate that an approximate algorithm can be nearly two orders of magnitude faster at the expense of only a small loss in accuracy. A retrieval pipeline using an approximate k-NN search can be more effective and efficient than the term-based pipeline. This opens up new possibilities for designing effective retrieval pipelines. Our software (including data-generating code) and derivative data based on the Stack Overflow collection is available online

Minimally Needed Evidence for Complex Event Recognition in Unconstrained Videos

This paper addresses the fundamental question – How do humans recognize complex events in videos? Normally, humans view videos in a sequential manner. We hypothesize that humans can make high-level inference such as an event is present or not in a video, by looking at a very small number of frames not necessarily in a linear order. We attempt to verify this cognitive capability of humans and to discover the Minimally Needed Evidence (MNE) for each event. To this end, we introduce an online game based event quiz facilitat-ing selection of minimal evidence required by humans to judge the presence or absence of a complex event in an open source video. Each video is divided into a set of temporally coherent microshots (1.5 secs in length) which are revealed only on player request. The player’s task is to identify the positive and negative occurrences of the given target event with minimal number of requests to reveal evidence. Incentives are given to players for correct identification with the minimal number of requests. Our extensive human study using the game quiz validates our hypothesis- 55 % of videos need only one microshot for correct human judgment and events of varying complexity require differ-ent amounts of evidence for human judgment. In addition, the pro-posed notion of MNE enables us to select discriminative features, drastically improving speed and accuracy of a video retrieval sys-tem

Transactional Support for Visual Instance Search

International audienceThis article addresses the issue of dynamicity and durability for scalable indexing of very large and rapidly growing collections of local features for visual instance retrieval. By extending the NV-tree, a scalable disk-based high-dimensional index, we show how to implement the ACID properties of transactions which ensure both dynamicity and durability. We present a detailed performance evaluation of the transactional NV-tree, showing that the insertion throughput is excellent despite the effort to enforce the ACID properties

H∞ LIDAR odometry for spacecraft relative navigation

Current light detection and ranging (LIDAR) based odometry solutions that are used for spacecraft relative navigation suffer from quite a few deficiencies. These include an off-line training requirement and relying on the iterative closest point (ICP) that does not guarantee a globally optimum solution. To encounter this, the authors suggest a robust architecture that overcomes the problems of current proposals by combining the concepts of 3D local feature matching with an adaptive variant of the H∞ recursive filtering process. Trials on real laser scans of an EnviSat model demonstrate that the proposed architecture affords at least one order of magnitude better accuracy compared to ICP

Exploring Spatial Correlation for Visual Object Retrieval

2013-2014 > Academic research: refereed > Publication in refereed journa