Remote video monitoring for offshore sea farms: Reliability and availability evaluation and image quality assessment via laboratory tests

<div class="page" title="Page 1"><div class="section"><div class="layoutArea"><div class="column"><p class="Abstract">In this article, the availability and reliability of a remote video monitoring system for offshore sea farming plants are studied and tested in laboratory. The scope of the system is to ensure a video surveillance infrastructure so to supervise breeding cages along with the fish inside them, in order to contrast undesired phenomena like fish poaching as well as cages damages. The system is installed on a cage floating structure: it is mainly composed of an IP camera that is controlled by a Raspberry Pi Zero which is the core of the system. Images are streamed thanks to a 3G/4G dongle, while the overall system is powered via two photovoltaic panels charging a backup battery. Simulations are carried out considering two seasonal functioning periods (i.e., winter and summer): each of them is characterised by temperature trends defined according to the average temperatures of the system deployment site, 8 km offshore the city of Piombino, Italy. In order to optimise power consumption without hindering application scenario requirements, the system operates according to a duty cycle of 2 minutes out of 15 (i.e., 8 minutes of operation per hour). The performances of the system are then tested in laboratory exploiting a climatic chamber so to simulate different environmental conditions: variations on image quality are then analysed in order to identify possible dependencies on critical situations related to specific temperature and relative humidity values and to the presence of salt in the air.</p></div></div></div></div>

pplication of deep learning model in recognition of growth stages of Cucumis melo L. in greenhouse

Despite the increasing application of deep learning (DL) models in various socioeconomics such as financial analysis and forecast, intelligent transport, self-driving, disease diagnosis, the effective use of this technology to support agricultural cultivation is still limited. This paper introduces the implementation of the lightest and state-of-the-art YOLOv5 architecture for automatic recognising of important growth stages of Cucumis meloL. from the camera images collected in the greenhouse. This image identification initiative achieved an average accuracy of 96% F1-score in the identification of the five growth stages of Cucumis melo L. using a limited set of training and testing data (total 2,818 images of Cucumis melo L.). These preliminary results lead to the conclusion that the YOLOv5 object detection and classification model is a truly lightweight and promising DL solution after the adoption of the transfer learning technique. Moreover, the YOLOv5 model can execute good performance on edge devices which may open up a new approach in different object detection and classification in real-time directly from a smartphone, Jetson Nano, IP camera...

FirmHunter: State-Aware and Introspection-Driven Grey-Box Fuzzing towards IoT Firmware

IoT devices are exponentially increasing in all aspects of our lives. Via the web interfaces of IoT devices, attackers can control IoT devices by exploiting their vulnerabilities. In order to guarantee IoT security, testing these IoT devices to detect vulnerabilities is very important. In this work, we present FirmHunter, an automated state-aware and introspection-driven grey-box fuzzer towards Linux-based firmware images on the basis of emulation. It employs a message-state queue to overcome the dependency problem in test cases. Furthermore, it implements a scheduler collecting execution information from system introspection to drive fuzzing towards more interesting test cases, which speeds up vulnerability discovery. We evaluate FirmHunter by emulating and fuzzing eight firmware images including seven routers and one IP camera with a state-of-the-art IoT fuzzer FirmFuzz and a web application scanner ZAP. Our evaluation results show that (1) the message-state queue enables FirmHunter to parse the dependencies in test cases and find real-world vulnerabilities that other fuzzers cannot detect; (2) our scheduler accelerates the discovery of vulnerabilities by an average of 42%; and (3) FirmHunter is able to find unknown vulnerabilities.

Pendampingan Pembelajaran Keterampilan Secara Daring di Pondok Pesantren Tarbiyatul Iman Malang

Pengabdian pada masyarakat ini bermitra dengan Pondok Pesantren Tarbiyatul Iman (PPTI), Malang. Pondok mengalami kesulitan penyelenggaraan pembelajaran praktek memasak, pada kondisi pandemi. Pengajar tidak diperkenankan bertemu tatap muka langsung di kelas. Sedangkan pelajaran praktek membutuhkan pengamatan, pembimbingan secara langsung pada saat pelaksanaan. Tujuan pengabdian pada masyarakat ini adalah mendapatkan solusi pembelajaran keterampilan memasak yang terkendala akibat pandemi covid-19. Metodenya adalah pengajar (pengajar) mengajar secara daring, sedang santri berada di kelas praktek bersama. Video tutorial memasak, yang dibuat pengajar di share lebih dulu kepada santri 3 hari sebelum pelajaran. Pengajar mengajar secara online, di ruang guru, menggunakan laptop, dan peralatan bantu extended monitor, microphone smartphone, sound power. Di ruang kelas praktek disediakan laptop untuk komunikasi dengan pengajar, dan juga dilengkapi IP camera yang terhubung dengan laptop/ smartphone pengajar. IP camera dapat dikendalikan menghadapnya sehingga seakan akan pengajar ada di kelas. Masakan yang dicobakan salah satunya adalah makanan yang disukai pada umumnya remaja, dalam hal ini dipilih mie Jepang. Bumbu dan topping semaksimalnya disesuaikan dengan resep aslinya, yang didapatkan di pasar lokal. Masakan dipresentasikan/ disajikan dalam wadah ala restoran. Evaluasi hasil memasak melibatkan pengajar/ ustad lain. Santri dapat mengikuti kegiatan ini dengan baik, mengikuti dengan gembira, dipandu dengan baik, aktifitas santri terpantau dengan baik, dan hasil masak yang memuaskan. Pengajar dapat melaksanakan pembelajaran kelas secara daring-luring, lebih terampil menguasai berbagai peralatan, lebih mudah dalam memantau kelas, dan mudah dalam mengevaluasi. Kegiatan ini berhasil dilaksanakan dengan baik dan dapat dijadikan raw model pembelajaran praktek secara daring-luring. Aman mengikuti protokol pencegahan covid 19.

Estimation of Crowd Density from UAV Images based on Deep Learning

Crowd monitoring is necessary to improve safety and controllable movements to minimize risk, especially in high crowded events, such as Kumbh Mela, political rallies, sports event etc. In this current digital age mostly crowd monitoring still relies on outdated methods such as keeping records, using people counters manually, and using sensors to count people at the entrance. These approaches are futile in situations where people's movements are completely unpredictable, highly variable, and complex. Crowd surveillance using unmanned aerial vehicles (UAVs), can help us solve these problems. The proposed paper uses a UAV on which an IP Camera will be attached to get media, we then use a convolutional neural network to learn a regression model for crowd counting, the model will be trained extensively by using three widely used crowd counting datasets, ShanghaiTech part A and part B, UCF-CC 50 and UCF-QNRF.

Monitoring and Feeding System for Pets

Over the years, more and more households are beginning to have pets. So, the most important issue recently was how to raise pets in an easy way. In today’s world, there is a growing focus on the regular interactions between human and physical devices. This paper introduces a smart pet monitoring and feeding system that is working in an environment of Internet of Things (IoT). Pet feeders came into existence as pet owners found it difficult to cater time to feed their pets. This system allows the owner to schedule and manage feeding times, portion sizes, and food supply for pets. They are controllable on smartphones using blynk and computers in order to feed them even when owners are away. The food dispensing mechanism is performed by the servomotor. It allows the user to monitor the pets using IP camera. A voice recording module is interfaced to the Arduino Uno with the owner’s voice recording for calling the pets at the meal time. Automatic water refilling has been designed and implemented along with the food dispenser.It offers new and developed way of feeding pets without the need of human intervention with refill alert and feeding alert. . Using smart pet feeder in houses will assure pet owners and increase comfort and peace of mind. Especially when we are unavailable for them.

Absensi Kehadiran Menggunakan Kamera Pengawas Berbasis Teknologi Computer Vision

Sistem dapat menggunakan IP Camera maupun CCTV, IP Camera membutuhkan kabel UTP untuk melakukan komunikasi data, sementara CCTV membutuhkan kabel Coaxial. Pengenalan wajah dilakukan melalui tahap Face Detection, Feature Extraction dan Face Recognition, selanjutnya dicocokkan dengan data profil yang tersimpan di dalam Database. Untuk mendeteksi wajah diperlukan OpenCV yang ditanamkan ke dalam sistem. OpenCV adalah sebuah library (perpustakaan) yang digunakan untuk mengolah gambar dan video hingga kita mampu mengekstrak informasi di dalamnya. OpenCV dapat berjalan di berbagai bahasa pemrograman, seperti C, C++, Java, Python, dan juga didukung di berbagai platform seperti Windows, Linux, Mac OS, iOS dan Android. Setiap pengguna sistem Absensi Face Recognition perlu dilakukan registrasi terlebih dahulu 1 (satu) persatu, dan sistem melakukan Training dari video setiap pengguna yang didaftarkan dan dibuat Source Base dalam bentuk foto dan disimpan di komputer server sebagai menjadi pembanding dan mendeteksi wajah dari berbagai sudut kamera nantinya. Database digunakan adalah MySQL dengan data yang ditampung adalah informasi data wajah, data jadwal, data User serta data informasi absensi. Koneksi untuk CCTV menggunakan RTSP yang merupakan jaringan komputer yang dirancang untuk kebutuhan multimedia dan sistem komunikasi data, yang dapat yang dapat mengendalikan aliran media dari server. Protokol ini digunakan untuk menetapkan dan mengendalikan sesi media antara dua titik ujungnya. Sebagian besar server RTSP menggunakan Real-time Transport Protocol (RTP) yang saling melengkapi dengan Real-time Control Protocol (RTCP) untuk pengiriman aliran media. Sementara itu penggunaan IP Camera atau Kamera IP adalah kamera dengan basis Internet Protocol, jenis kamera video digital yang menerima data kontrol dan mengirimkan data gambar melalui jaringan IP. biasanya digunakan untuk pengawasan tetapi berbeda dengan kamera analog Closed-circuit Television (CCTV), yang mana tidak memerlukan perangkat perekaman lokal, namun hanya jaringan area lokal. Kebanyakan kamera IP adalah Webcam, tetapi istilah kamera IP atau Netcam biasanya hanya berlaku untuk kamera yang dapat langsung diakses melalui koneksi jaringan dan dapat digunakan untuk kamera pengawasan.

Attacks and Preventive Measures on Video Surveillance Systems: A Review

Video surveillance systems are widely deployed with large systems for use in strategic places such as home security, public transportation, banks, ATM centers, city centers, airports, and public roads, and play a vital role in protecting critical infrastructures. As various attacks are possible in these systems, identifying attacks and considering suitable security measures are essential. In this paper, we present a detailed review of existing and possible threats in video surveillance, CCTV, and IP-camera systems. This provides insight for the better identification of the security risks associated with the design and deployment of these systems and promotes further research in this emerging field. We also present countermeasures to prevent and protect the surveillance systems from various security attacks.

IP CAMERA SURVEILLANCE SYSTEM USING ANDROID APPLICATION BASED ON ARDUINO

Keamanan menjadi fokus utama bagi setiap orang, terutama bagi mereka yang memiliki barang berharga. Kamera pengintai merupakan salah satu cara untuk mencegah terjadinya pencurian. Sistem pengawasan telah dirancang dengan menggunakan IP kamera dengan fitur Motion Detection di dalamnya. Fungsi kamera IP adalah untuk mendeteksi dan menangkap gambar dari setiap gerakan dengan menggunakan fiturnya dan mengirimkan snapshot ke situs pengguna melalui FTP (File Transfer Protocol). Selain itu, sensor PIR (Passive InfraRed) yang digunakan sebagai motiondetectorakan dihubungkan ke mikrokontroler Arduino uno dan ethernet shield. Sistem ini juga menggunakan aplikasi pada smartphone android sebagai alert. Jika sensor mendeteksi beberapa gerakan, sensor akan mengaktifkan notifikasi di aplikasi android. Notifikasi di aplikasi android berisi 'Motion Detected check ftp: //ftp.roomsurveillance.info'. Hasil dari sistem surveilans ini berupa snapshot yang dikirimkan oleh kamera IP, dapat dilihat dan diunduh dengan mengklik link FTP. Sistem pengawasan ini dapat meningkatkan keamanan ruangan dalam kondisi realtime.