TANGGUNG JAWAB PERUSAHAAN ANGKUTAN TERHADAP PENGIRIMAN BARANG MENGGUNAKAN JASA ANGKUTAN PENUMPANG (SUATU PENELITIAN DI KOTA BANDA ACEH)

ABSTRAKMUHIBUR RAHMAN, TANGGUNG JAWAB PERUSAHAAN2018 ANGKUTAN TERHADAP PENGIRIMAN BARANG MENGGUNAKAN JASA ANGKUTAN PENUMPANG (Suatu Penelitian di Kota Banda Aceh) Fakultas Hukum Universitas Syiah Kuala (vi, 55) pp., tabl., bibl. Dr. DARMAWAN, S.H., M.Hum.Pasal 234 ayat (1) Undang-Undang Nomor 22 Tahun 2009 Tentang Lalu Lintas dan Angkutan Jalan menyatakan bahwa pemilik, penyedia jasa angkutan umum bertanggung jawab atas kerugian yang diderita oleh penumpang dan atau pemilik barang dan berdasarkan pasal 4 Peraturan Menteri Perhubungan Nomor: KM 5 Tahun 2005 tentang Penyelenggaraan Jasa Titip untuk menyelenggarakan jasa titip wajib mendapatkan izin jasa titip, namun di kota Banda Aceh terdapat perusahaan angkutan penumpang yang yang menyelenggarakan jasa titip tanpa izin.Tujuan Penelitian untuk menjelaskan tanggung jawab perusahaan angkutan terhadap pengiriman barang yang merugikan pengguna jasa, akibat hukum bagi pengusaha pengangkutan yang melakukan pengangkutan barang titipan tanpa izin, dan faktor penyebab penguna jasa sering menggunakan angkutan penumpang untuk mengirim barang titipan.Penelitian ini merupakan penelitian yang bersifat yuridis empiris, yaitu penelitian ilmiah untuk menemukan kebenaran berdasarkan pelaksanaan di lapangan yang mengacu pada keilmuan hukum yang menggunakan metode pendeketan penelitian kepustakaan dan lapangan. Penelitian lapangan dilakukan guna memperoleh data primer melalui wawancara dengan responden dan informan.Berdasarkan hasil penelitian bahwa pengangkut yang mengangkut barang titipan tanpa izin menimbulkan kerugian kepada pengguna jasa, maka perusahaan angkutan wajib membayar ganti rugi. Akibat hukum perusahaan angkutan yang tidak memiliki izin jasa titip akan mendapat teguran dan sanksi dari Dinas Perbuhubungan. Faktor pengguna jasa sering menggunkan angkutan penumpang untuk mengirim barang titipan karena hemat, cepat, dan praktis serta kurangnya pengetahuan pengguna jasa.Perusahaan angkutan yang mengangkut barang titipan disarankan untuk tidak menerima jasa titip. Disarankan kepada Dinas Perhubungan supaya memberi sanksi yang tegas terhadap perusahaan angkutan yang mengangkut barang titipan. Kepada pengguna jasa titip, agar mengirim barang kepada perusahaan jasa titip yang memiliki izin bukan kepada perusahaan angkutan penumpang

Resource Allocation Using Reconfigurable Intelligent Surface (RIS)-Assisted Wireless Networks in Industry 5.0 Scenario

ABSTRACT: Mobile communication networks evolved from first-generation (1G) to sixth-generation (6G) and the requirement for quality of services (QoS) and higher bandwidth increased. The evolvement of 6G can be deployed in industry 5.0 to fulfill the future industry requirement. However, deploying 6G in industry 6.0 is very challenging, and installing a reconfigurable intelligent surface (RIS) is an efficient solution. RIS contains the passive elements which are programmed for the tuning of a wireless channel. We formulate an optimization problem to allocate resources in the RIS-supported network. This article presents a mixed-integer non-linear programable problem (MINLP) considering the industry 5.0 scenario and proposes a novel algorithm to solve the optimization problem. We obtain the e optimal solution using the proposed algorithm. The proposed algorithm is evaluated in energy efficiency (EE), throughput, latency, and channel allocation. We compare the performance of several algorithms, and the proposed algorithm outperforms all the algorithms

Low-rank multi-channel features for robust visual object tracking

Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers

Time-Domain Investigation of Switchable Filter Wide-Band Antenna for Microwave Breast Imaging

RÉSUMÉ: This paper investigates the time-domain performance of a switchable filter impulse radio ultra-wideband (IR-UWB) antenna for microwave breast imaging applications. A miniaturized CPW-fed integrated filter antenna with switchable performance in the range of the Worldwide Interoperability for Microwave Access (WiMAX) and Wireless Local Area Network (WLAN) bands could operate well within a 3.0 to 11 GHz frequency range. The time-domain performance of the filter antenna was investigated in comparison to that of the designed reference wideband antenna. By comparing both antennas’ time-domain characteristics, it was seen that the switchable filter antenna had good time-domain resolution along with the frequency-domain operation. Additionally, the time-domain investigation revealed that the switchable filter wide-band antenna performed similarly to the reference wide band antenna. This antenna was also utilized for a tumor detection application, and it was seen that the switchable filter wide-band antenna could detect a miniaturized irregularly shaped tumor easily, which is quite promising. Such an antenna with a good time-domain resolution and tumor detection capability will be a good candidate and will find potential applications in microwave breast imaging

A Low-Cost CPW-Fed Multiband Frequency Reconfigurable Antenna for Wireless Applications

A novel, cedar-shaped, coplanar waveguide-fed frequency reconfigurable antenna is proposed. The presented antenna uses low-cost FR4 substrate with a thickness of 1.6 mm. Four PIN diodes are inserted on the antenna surface to variate the current distribution and alter the resonant frequencies with different combinations of switches. The proposed antenna is fabricated and measured for all states, and a good agreement is seen between measured and simulated results. This antenna resonates within the range of 2 GHz to 10 GHz, covering the major wireless applications of aviation service, wireless local area network (WLAN), worldwide interoperability for microwave access (WiMAX), long distance radio telecommunications, and X-band satellite communication. The proposed antenna works resourcefully with reasonable gain, significant bandwidth, directivity, and reflection coefficient. The proposed multiband reconfigurable antenna will pave the way for future wireless communications including WLAN, WiMAX, and possibly fifth-generation (5G) communication

Image Local Features Description through Polynomial Approximation

This work introduces a novel local patch descriptor that remains invariant under varying conditions of orientation, viewpoint, scale, and illumination. The proposed descriptor incorporate polynomials of various degrees to approximate the local patch within the image. Before feature detection and approximation, the image micro-texture is eliminated through a guided image filter with the potential to preserve the edges of the objects. The rotation invariance is achieved by aligning the local patch around the Harris corner through the dominant orientation shift algorithm. Weighted threshold histogram equalization (WTHE) is employed to make the descriptor in-sensitive to illumination changes. The correlation coefficient is used instead of Euclidean distance to improve the matching accuracy. The proposed descriptor has been extensively evaluated on the Oxford's affine covariant regions dataset, and absolute and transition tilt dataset. The experimental results show that our proposed descriptor can categorize the feature with more distinctiveness in comparison to state-of-the-art descriptors. - 2013 IEEE.This work was supported by the Qatar National Library.Scopu

Adversarial Gaussian Denoiser for Multiple-Level Image Denoising

Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian image denoising tasks. Convolutional neural network-based denoising approaches come across a blurriness issue that produces denoised images blurry on texture details. To resolve the blurriness issue, we first performed a theoretical study of the cause of the problem. Subsequently, we proposed an adversarial Gaussian denoiser network, which uses the generative adversarial network-based adversarial learning process for image denoising tasks. This framework resolves the blurriness problem by encouraging the denoiser network to find the distribution of sharp noise-free images instead of blurry images. Experimental results demonstrate that the proposed framework can effectively resolve the blurriness problem and achieve significant denoising efficiency than the state-of-the-art denoising methods

Deep learning (DL) based joint resource allocation and RRH association in 5G-multi-tier networks

ABSTRACT: Fifth-Generation (5G) networks have adopted a multi-tier structural model which includes femtocells, picocells, and macrocells to ensure the user quality-of-service (QoS). To meet these QoS demands, the system requires optimization of different resources in different network dynamics carefully. However, if ignored, this will lead to long processing delays and high computational burdens. To avoid this, we proposed Deep Learning (DL) based resource allocation (RA) as a promising solution to meet the network requirements. DL is an effective mechanism where neural networks can learn to develop RA techniques. Thus, an optimized RA decision can be achieved using DL without exhaustive computations. Further, DL uses DL to achieve solutions for joint RA and remote-radio-head (RRH) association problems in multi-tier Cloud-Radio Access Networks (C-RAN). Initially, a summary of existing literature on DL-based RA techniques is provided, followed by a deep neural network (DNN) description, its architectures, and the data training method. Then, a supervised DL technique is presented to solve the joint RA and RRH-association problem. An efficient subchannel assignment, power allocation, and RRH-association (SAPARA) technique are used to generate the training data for the DNN model using the iterative approach where the seed data for the SAPARA technique is taken using a uniform power allocation and path-loss based association (UPA-PLBA) model. After training the DNN model, the accurateness of the presented model is tested. Simulation outcomes demonstrate that our proposed scheme is capable of providing an efficient solution in the considered scenario

Quintuple Band Antenna for Wireless Applications with Small Form Factor

RÉSUMÉ: A coplanar waveguide-fed quintuple band antenna with a slotted circular-shaped radiator for wireless applications with a high isolation between adjacent bands is presented in this paper. The proposed antenna resonates at multiple frequencies with corresponding center frequencies of 2.35, 4.92, 5.75, 6.52, and 8.46 GHz. The intended functionality is achieved by introducing a circular disc radiator with five slots and a U-shaped slot in the feed. The proposed antenna exhibits coverage of the maximum set of wireless applications, such as satellite communication, worldwide interoperability for microwave access, wireless local area network (WLAN), long-distance radio telecommunications, and X-band/Satcom wireless applications. The simulation and measurement results of the proposed fabricated antenna demonstrate the high isolation between adjacent bands. A stable realized gain with an advantageous radiation pattern is achieved at the operating frequency bands. The proposed simple design, compact structure, and simple feeding technique make this antenna suitable for integration in several wireless communication applications, where the portability of devices is a significant concern. The proposed antenna is anticipated to be an appropriate candidate for WLAN, long-term evolution, and fifth-generation mobile communication because of its multi-operational bands and compact size for handheld devices

A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System

ABSTRACT: In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intru- sion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in this article an intrusion detection model based on a Real-Time Sequential Deep Extreme Learning Machine Cyber- security Intrusion Detection System (RTS-DELM-CSIDS) security model. The proposed model initially determines the rating of security aspects contributing to their significance and then develops a comprehensive intrusion detection frame- work focused on the essential characteristics. Furthermore, we investigated the feasibility of our proposed RTS-DELM-CSIDS framework by performing dataset evaluations and calculating accuracy parameters to validate. The experimental findings demonstrate that the RTS-DELM-CSIDS framework outperforms con- ventional algorithms. Furthermore, the proposed approach has not only research significance but also practical significance