1,517 research outputs found

    Persepsi Pemustaka Pada Layanan Perpustakaan Keliling Di Alun-alun Mini Ungaran Kabupaten Semarang

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    Judul skripsi ini adalah “Persepsi Pemustaka pada Layanan Perpustakaan Keliling di Alun-alun Mini Ungaran Kabupaten Semarang”. Tujuan penelitian ini adalah untuk mengetahui bagaimana persepsi pemustaka pada layanan perpustakaan keliling di Alun-alun Mini Ungaran Kabupaten Semarang. Skripsi ini menggunakan metode penelitian kualitatif jenis deskriptif bentuk studi kasus. Dalam skripsi ini diambil lima informan yang memenuhi kriteria sebagai informan. Teknik pengambilan sampel dilakukan dengan teknik Purposive sampling. Teknik pengumpulan data menggunakan observasi, dokumen, dan wawancara. Pengolahan data menggunakan reduksi data dengan membuat transkrip, mengelompokkan, kemudian membuat analisis. Penyajian data dilakukan dengan mendeskripsikan hasil dari reduksi data serta menarik kesimpulan. Penelitian ini melihat dari segi bukti langsung, kehandalan, daya tanggap, jaminan, dan empati. Hasil analisis diketahui bahwa persepsi pemustaka pada layanan perpustakaan keliling terkait dengan bukti langsung pemustaka mengetahui adanya layanan perpustakaan keliling, letak perpustakaan keliling sudah strategis, fasilitas sudah bagus, koleksi lumayan lengkap, perilaku dan penampilan petugas baik, rapi, trampil, dan sehat. Kehandalan sudah datang tepat waktu, waktu yang disediakan cukup memadai, dan petugas sudah tepat dan tidak berbelit-belit. Daya tanggap petugas sudah cepat melayani dan merespon, petugas sudah sigap, dan menawarkan bantuan. Jaminan tempat perpustakaan keliling aman dan nyaman, petugas tidak memberikan alternatif koleksi lain, petugas sudah sopan tetapi kurang ramah. Empati petugas peduli kritikan, sabar, telaten, dan objektif. Sebagian besar layanan perpustakaan keliling di Alun-alun Mini Ungaran Kabupaten Semarang sudah bagus. Walaupun masih ada kekurangan yang disampaikan pemustaka, meliputi jumlah koleksi yang kurang beragam, petugas kurang ramah, serta pelayanan petugas yang dirasa masih kurang memuaskan

    Direct Differential Photometric Stereo Shape Recovery of Diffuse and Specular Surfaces

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    This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s10851-016-0633-0Recovering the 3D shape of an object from shading is a challenging problem due to the complexity of modeling light propagation and surface reflections. Photometric Stereo (PS) is broadly considered a suitable approach for high-resolution shape recovery, but its functionality is restricted to a limited set of object surfaces and controlled lighting setup. In particular, PS models generally consider reflection from objects as purely diffuse, with specularities being regarded as a nuisance that breaks down shape reconstruction. This is a serious drawback for implementing PS approaches, since most common materials have prominent specular components. In this paper, we propose a PS model that solves the problem for both diffuse and specular components aimed at shape recovery of generic objects with the approach being independent of the albedo values thanks to the image ratio formulation used. Notably, we show that by including specularities, it is possible to solve the PS problem for a minimal number of three images using a setup with three calibrated lights and a standard industrial camera. Even if an initial separation of diffuse and specular components is still required for each input image, experimental results on synthetic and real objects demonstrate the feasibility of our approach for shape reconstruction of complex geometries.The first author acknowledges the support of INDAM under the GNCS research Project “Metodi numerici per la regolarizzazione nella ricostruzione feature-preserving di dati.

    Signal enhancement and efficient DTW-based comparison for wearable gait recognition

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    The popularity of biometrics-based user identification has significantly increased over the last few years. User identification based on the face, fingerprints, and iris, usually achieves very high accuracy only in controlled setups and can be vulnerable to presentation attacks, spoofing, and forgeries. To overcome these issues, this work proposes a novel strategy based on a relatively less explored biometric trait, i.e., gait, collected by a smartphone accelerometer, which can be more robust to the attacks mentioned above. According to the wearable sensor-based gait recognition state-of-the-art, two main classes of approaches exist: 1) those based on machine and deep learning; 2) those exploiting hand-crafted features. While the former approaches can reach a higher accuracy, they suffer from problems like, e.g., performing poorly outside the training data, i.e., lack of generalizability. This paper proposes an algorithm based on hand-crafted features for gait recognition that can outperform the existing machine and deep learning approaches. It leverages a modified Majority Voting scheme applied to Fast Window Dynamic Time Warping, a modified version of the Dynamic Time Warping (DTW) algorithm with relaxed constraints and majority voting, to recognize gait patterns. We tested our approach named MV-FWDTW on the ZJU-gaitacc, one of the most extensive datasets for the number of subjects, but especially for the number of walks per subject and walk lengths. Results set a new state-of-the-art gait recognition rate of 98.82% in a cross-session experimental setup. We also confirm the quality of the proposed method using a subset of the OU-ISIR dataset, another large state-of-the-art benchmark with more subjects but much shorter walk signals

    A Novel Transformer-Based IMU Self-Calibration Approach through On-Board RGB Camera for UAV Flight Stabilization

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    During flight, unmanned aerial vehicles (UAVs) need several sensors to follow a predefined path and reach a specific destination. To this aim, they generally exploit an inertial measurement unit (IMU) for pose estimation. Usually, in the UAV context, an IMU entails a three-axis accelerometer and a three-axis gyroscope. However, as happens for many physical devices, they can present some misalignment between the real value and the registered one. These systematic or occasional errors can derive from different sources and could be related to the sensor itself or to external noise due to the place where it is located. Hardware calibration requires special equipment, which is not always available. In any case, even if possible, it can be used to solve the physical problem and sometimes requires removing the sensor from its location, which is not always feasible. At the same time, solving the problem of external noise usually requires software procedures. Moreover, as reported in the literature, even two IMUs from the same brand and the same production chain could produce different measurements under identical conditions. This paper proposes a soft calibration procedure to reduce the misalignment created by systematic errors and noise based on the grayscale or RGB camera built-in on the drone. Based on the transformer neural network architecture trained in a supervised learning fashion on pairs of short videos shot by the UAV’s camera and the correspondent UAV measurements, the strategy does not require any special equipment. It is easily reproducible and could be used to increase the trajectory accuracy of the UAV during the flight

    A Novel GAN-Based Anomaly Detection and Localization Method for Aerial Video Surveillance at Low Altitude

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    The last two decades have seen an incessant growth in the use of Unmanned Aerial Vehicles (UAVs) equipped with HD cameras for developing aerial vision-based systems to support civilian and military tasks, including land monitoring, change detection, and object classification. To perform most of these tasks, the artificial intelligence algorithms usually need to know, a priori, what to look for, identify. or recognize. Actually, in most operational scenarios, such as war zones or post-disaster situations, areas and objects of interest are not decidable a priori since their shape and visual features may have been altered by events or even intentionally disguised (e.g., improvised explosive devices (IEDs)). For these reasons, in recent years, more and more research groups are investigating the design of original anomaly detection methods, which, in short, are focused on detecting samples that differ from the others in terms of visual appearance and occurrences with respect to a given environment. In this paper, we present a novel two-branch Generative Adversarial Network (GAN)-based method for low-altitude RGB aerial video surveillance to detect and localize anomalies. We have chosen to focus on the low-altitude sequences as we are interested in complex operational scenarios where even a small object or device can represent a reason for danger or attention. The proposed model was tested on the UAV Mosaicking and Change Detection (UMCD) dataset, a one-of-a-kind collection of challenging videos whose sequences were acquired between 6 and 15 m above sea level on three types of ground (i.e., urban, dirt, and countryside). Results demonstrated the effectiveness of the model in terms of Area Under the Receiving Operating Curve (AUROC) and Structural Similarity Index (SSIM), achieving an average of 97.2% and 95.7%, respectively, thus suggesting that the system can be deployed in real-world applications

    On the well-posedness of uncalibrated photometric stereo under general lighting

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    Uncalibrated photometric stereo aims at estimating the 3D-shape of a surface, given a set of images captured from the same viewing angle, but under unknown, varying illumination. While the theoretical foundations of this inverse problem under directional lighting are well-established, there is a lack of mathematical evidence for the uniqueness of a solution under general lighting. On the other hand, stable and accurate heuristical solutions of uncalibrated photometric stereo under such general lighting have recently been proposed. The quality of the results demonstrated therein tends to indicate that the problem may actually be well-posed, but this still has to be established. The present paper addresses this theoretical issue, considering first-order spherical harmonics approximation of general lighting. Two important theoretical results are established. First, the orthographic integrability constraint ensures uniqueness of a solution up to a global concave-convex ambiguity , which had already been conjectured, yet not proven. Second, the perspective integrability constraint makes the problem well-posed, which generalizes a previous result limited to directional lighting. Eventually, a closed-form expression for the unique least-squares solution of the problem under perspective projection is provided , allowing numerical simulations on synthetic data to empirically validate our findings

    Cloud service brokerage: Exploring characteristics and benefits of B2B cloud marketplaces

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    With the increasing popularity of cloud computing, a new technology and business model called cloud service brokerage (CSB) is emerging. CSB is, in essence, a middleman in the cloud-computing supply chain to connect prospective cloud buyers with suitable service providers. This chapter focuses on a type of CSB, B2B cloud marketplaces. Recently, this type of marketplace has evolved into two broad categories—business application marketplaces and API marketplaces. This chapter reviews the characteristics of B2B cloud marketplaces, and their benefits, which include ease-of-use and ease-of-integration, enhanced security, increased manageability, faster implementation, and cost reduction. The chapter concludes with two mini-case studies, on Salesforce AppExchange and RapidAPI, to illustrate how firms could use B2B cloud marketplaces to generate, capture and measure business value
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