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Go to Editorial ManagerIn this paper ternary logic is encoded into binary and certain processes were conducted on binary logic after which the binary is decoded to ternary. General purpose digital devices were used and the circuit is designed back to front starting from ternary logic provided by transistor pairs at output side back to front end. This provided easier design technique in this particular paper. Practical and simulation results are recorded. K eyw ords: Logic, ternary- binary conversion, coding, decoding. تشفير المنطق الثالثي رونق علي حبيب جامعة الب صرة / كلية الهندسة / العراق الخالصة في هذا البحث يتم تشفير المنطق الثالثي الى ثنائي ويتم اجراء بعض العمليات على المنطق الثنائي التي يتم بعدها االعادة الى الشفرة الثالثية . استخدمت النبائط الرقمية لالغراض العامة (general purpose devices) في تصميم الدائرة الذ ي يتم من مرحلة االخراج (output) الى مرحلة االدخال (input) بدءا من المنطق الثالثي باستخدام ازواج الترانزستور في جهة االخراج والعودة الى جهة االدخال . هذا يجعل تقنية التصميم اسهل ( في هذا البحث خاصة ). ولقد تم تسجيل النتائج العملية ونتائج المحاكاة للدائرة العمل . ية اﻟﻤﺠﻠﺔ اﻟﻌﺮاﻗﻴﺔ ﻟﻠﻬﻨﺪﺳﺔ اﻟﻜﻬﺮﺑﺎﺋﻴﺔ واﻻﻟﻜﺘﺮوﻧﻴﺔ Iraq J. Electrical and Electronic Engineering ﻡﺠﻠﺪ 10 ، اﻟﻌﺪد 1 ، 2014 Vol.10 No.1 , 2014 24
In order to reduce the impact of watermark embedding on the perceptual fidelity of the marked signal, watermarking systems process the generated watermark to match it to the local properties of the underlying host signal prior to embedding. However, this adaptation process could distort the watermark, affecting its robustness and information content. In this paper, a new watermark coding technique is proposed, that enables the application of some mark- nondistorting host-adaptation processing, where the intensity of the watermark could be redistributed according to the local properties of the underlying host without changing the way of interpreting the watermark to be embedded. This completely eliminates the need to equalize adaptation distortions prior to decoding, and hence, to pass any side information about the adaptation processing to the decoder, too.
Drug-drug interactions (DDIs) stand at the forefront of challenges in modern pharmacology, necessitating precise prediction methods to ensure patient safety. This study presents a pioneering approach that synergizes attributed heterogeneous graph embedding with deep learning to forecast DDIs and their specific classifications. Our methodology is delineated into two pivotal stages. The preliminary phase revolves around data assimilation, leading to the creation of specialized feature matrices such as Chemical Composition, Interaction Targets, Enzymatic Reactions, and Biological Pathways. These matrices culminate in a comprehensive drug network where drugs are symbolized as nodes. Upon rigorous data refinement, these matrices serve as attribute markers for each node. Capitalizing on the robustness of the attributed heterogeneous network, we amalgamate diverse drug attributes, thereby amplifying the depth of drug interaction assessments. The subsequent phase sees these drug embedding vectors undergo strategic concatenation, resulting in detailed feature vectors for drug pairings. The final step involves a dense neural network, tasked with decoding intricate drug interaction nuances. The introduction of an attention-driven embedding process further accentuates the model’s capability by emphasizing pivotal interactions. The promising results, coupled with an innovative methodology, sets the stage for future explorations, potentially revolutionizing DDI predictions.